Abstract:
Hashimoto&#39;s Thyroiditis (HT) is the most common type of inflammation of the thyroid gland and accurate diagnosis of HT would be advantageous in predicting thyroid failure. The application presents a three tier architecture for image-based diagnosis and a monitoring application using a network cloud. The presentation layer is run on the tablet (e.g., a mobile device), while the business and persistence layers run on a single network cloud or distributed on different network clouds in a multi-tenancy and multi-user application. Such three tier architecture is used for automated data mining application for diagnosis of Hashimoto&#39;s Thyroiditis (HT) Disease using ultrasound.

Description:
PRIORITY APPLICATIONS 
       [0001]    This is a continuation-in-part patent application of co-pending patent application Ser. No. 12/799,177; filed Apr. 20, 2010 by the same applicant. This is also a continuation-in-part patent application of co-pending patent application Ser. No. 12/802,431; flied Jun. 7, 2010 by the same applicant. This is also a continuation-in-part patent application of co-pending patent application Ser. No. 12/896,875; filed Oct. 2, 2010 by the same applicant. This is also a continuation-in-part patent application of co-pending patent application Ser. No. 12/960,491; filed Dec. 4, 2010 by the same applicant. This is also to continuation-in-part patent application of co-pending patent application Ser. No. 13/053,971; filed Mar. 22, 2011 by the same applicant. This is also a continuation-in-part patent application of co-pending patent application Ser. No. 13/077,631; filed Mar. 31, 2011 by the same applicant. This is also a continuation-in-part patent application of co-pending patent application Ser. No. 13/107,935; filed May 15, 2011 by the same applicant. This is also as continuation-in-part patent application of co-pending patent application, Ser. No. 13/219,695; filed Aug. 28, 2011 by the same applicant. This is also a continuation-in-part patent application of co-pending patent application, serial no, 13/253,952; filed Oct. 5, 2011 by the same applicant. This is also a continuation-in-part patent application of co-pending patent application Ser. No. 13/407,602; filed Feb. 28, 2012 by the same applicant. This is also a continuation-in-part patent application of co-pending patent application Ser. No. 13/412,118; filed. Mar. 5, 2012 by the same applicant. This is also a continuation-in-part patent application of co-pending patent application Ser. No. 13/449,518; filed Apr. 18, 2012 by the same applicant. This is also a continuation-in-part patent application of co-pending patent application Ser. No. 13/465,091; filed May 7, 2012 by the same applicant. This is also a continuation-in-part patent application of co-pending patent application Ser. No. 13/589,802; filed Aug. 20, 2012 by the same applicant. This present patent application draws priority from the referenced co-pending patent applications. The entire disclosures of the referenced co-pending patent applications are considered part of the disclosure of the present application and are hereby incorporated by reference herein in its entirety. 
     
    
     TECHNICAL FIELD 
       [0002]    This application relates to a method and system for use with data processing and imaging systems, according to one embodiment, and more specifically, for a mobile architecture using cloud for data mining application such as Hashimoto Thyroiditis (HT) classification and diagnosis. 
       BACKGROUND 
       [0003]    Imaging-based technologies have been active for over a century and today the same imaging-based technologies are used electronically for creating pictures of the human body and examining it. Majority of these imaging modalities are non-invasive and painless. Depending upon the symptoms of the patient&#39;s disease, a physician will choose a type of the imaging modality, its diagnosis, treatment and monitoring. Some of the most famous medical imaging modalities are Ultrasound, X-ray, MR, CT, PET, SPECT and now more molecular and cellular level. These imaging modalities are conducted by the radiologist or a technologist who are well trained, to operate and know the safety rules. 
         [0004]    The importance of imaging-based techniques for diagnosis, treatment, monitoring is increasing day-by-day. Thus more and more body images are generated every day. Hospitals and health care providers are generating image data at an alarming rate. There is no doubt that one has to design complex medical imaging software for diagnosis, treatment and monitoring, but it is becoming challenging to access these data in this age of the world. Storage of the medial images is one issue and how to access this data for decision making such as diagnosis, treatment and monitoring is another issue. 
       BRIEF SUMMARY AND THE OBJECTS OF THE DISCLOSED EMBODIMENTS 
       [0005]    Hashimoto&#39;s Thyroiditis (HT) is an autoimmune disease that is characterized by lymphocytic infiltration and disruption of thyroid gland tissue architecture and production of specific autoantibodies against thyroid. Hashimoto&#39;s Thyroiditis is the most common type of inflammation of the thyroid gland, and a most frequent cause of hypothyroidism. Early diagnosis of Hashimoto&#39;s Thyroiditis would be advantageous in predicting thyroid failure. 
         [0006]    The following are the commonly first lowed diagnostic criteria of Hashimoto&#39;s Thyroiditis: (i) a positive test for thyroid autoantibodies in serum, (ii) an elevated serum thyrotropin (TSH) concentration, or (ii) the presence of lymphocytic infiltration of the thyroid in histopathologic examination. Other common diagnostic tests are fine-needle aspiration biopsy and an ultrasound (US) scan. Among these techniques, the most preferred choice is thyroid ultrasonography which is a non-invasive diagnostic test that provides an image of the structure and the characteristics of thyroid. It was reported that autoimmune thyroiditis could be successfully excluded on the basis of ultrasound alone in 1962 cases among 2322 cases studied (84%). Moreover, ultrasound is affordable, widely available, does not use harmful ionizing radiation, and has relatively shorter acquisition time compared to other modalities like Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). 
         [0007]    A regular thyroid tissue is characterized by homogeneity and high echogenicity in ultrasound. In Hashimoto&#39;s Thyroiditis, the architecture destruction of the follicles and lymphocytic infiltrations result in decreased echogenicity. There is evidence that reduced thyroid echogenicity demonstrated by ultrasonography is a strong predictor of chronic autoimmune thyroiditis even when this disorder has not been suspected clinically. Earlier, this change in echogenicity was evaluated based on a rough visual comparison with the surrounding neck muscular tissue. Subsequently, analysis of grayscale histogram was carried out for quantitative measurement of echogenicity decline. Other studies too have proposed that computerized gray-scale ultrasound gives quantitative determination of thyroid echogenicity and mean tissue density in thyroid autoimmune diseases. 
         [0008]    These computerized methods have the advantages of being more objective. However, they are limited by the fact that there is lack of procedure standardization because individual investigators use various initial ultrasound settings. Echogenic appearance of the thyroid gland varies with the adjustment of the gain. Thus, ultrasound diagnosis of Hashimoto&#39;s Thyroiditis is still operator-dependent and defined conditions are necessary to evaluate exact data. To compensate the attenuation of ultrasound energy as the pulses traverse the different layers of the neck, a corresponding amplification of ultrasound signals by the operator is necessary. Too much amplification may mask a true reduction in thyroid echogenicity, and too little amplification may lead to a false diagnosis of reduced thyroid echogenicity. Furthermore, in the end stage of Hashimoto&#39;s Thyroiditis, mean tissue density assessment may be misleading because of the presence of a combination of the hyperechoic and hypoechoic signals in the examined zone. These operator dependent and echogenic limitations is another reason for development of an objective, non-invasive, and accurate Hashimoto&#39;s Thyroiditis diagnosis support systems that use medical image mining techniques. 
         [0009]    Image mining uses techniques from statistics and artificial intelligence to determine features which quantitatively characterize the patterns in an image. In this context, these features quantify the histopathologic components of the US thyroid images obtained from normal and Hashimoto&#39;s Thyroiditis-affected patients. These features can then be used to train supervised learning based classifiers to relate the extracted features from an image to the corresponding class (normal or Hashimoto&#39;s Thyroiditis-affected abnormal). The trained classifiers can then be used to predict the class of a new image which was not used for training. The key objective of this work is to develop one such Computer Aided Diagnosis (CAM-based paradigm that uses classification techniques to automatically differentiate ultrasound images from normal and Hashimoto&#39;s Thyroiditis affected cases in cloud-based settings. Thus, the proposed technique will have the following characteristics: (a) It will use thyroid images from the most commonly used, affordable and available, non-invasive and safe ultrasound modality; (b) The interpretations will be more objective and reproducible due to the use of standard image analysis algorithms; (c) Use of this technique will, not incur any additional cost because the proposed algorithm can be written into a software application at no extra cost and can be installed in the physician&#39;s computer; and (d) It will act as an adjunct tool that provides to second opinion on the initial diagnosis thereby increasing the confidence of the physician in planning, the subsequent treatment evaluation protocol for the patient. 
         [0010]    This application is a novel method that presents a three tier architecture for image-based diagnosis and monitoring application using cloud. The presentation layer is run on the tablet (mobile device), while the business and persistence layer runs on the cloud or as set of clouds. The business and presentation layers can be in one cloud or multiple clouds. Further, the system can accommodate multiple users in this architecture set-up with multiple tenancies. 
         [0011]    The application is designed to assist the endocrinologist, internal medicine or a physician in examining the Thyroid Disease and in particular diagnosis the Hashimoto Disease. 
         [0012]    Data access from remote locations has become important day-by-day in this high information technology world. Due to this, now Cloud-based imaging can provide solution to such challenges. Even though, HIPPA or security or data ownership technologies are evolving, but the pros of Cloud-based technologies have outweighed the cons. 
         [0013]    The Cloud-based technology offers, the first one is pricing. Cloud-based processing is less expensive due to low storage cost. Additional benefit is that if one uses Cloud for Software as a Service (SaaS) application, the storage cost can be free. 
         [0014]    Another advantage of Cloud-based processing is the capacity to handle. Compared to costs for the local processing when the data storage requirements are changing dynamically, Cloud-based capacity may be advantageous. Expansion possibility is easy to handle. Emergency storage requirements may also less challenging to handle in Cloud-based processing. 
         [0015]    Another major advantage is the disaster recovery. One needs regular backups and maintenance; this can be avoided in the Cloud-based processing. 
         [0016]    Having discussed the benefits of Cloud-based processing, it is thus important on how to use Cloud-based services for applications which short time to run applications. This innovative application is about the architecture is designed for medical imaging applications, such as cardiovascular, prostate cancer, ovarian cancer, liver cancer, thyroid cancer and in particular diagnosis of Hashimoto Disease. Today&#39;s medical based applications do not just require viewing of the images, but also processing business layers for doctors to get the clinical information such as diagnosis, treatment support and monitoring. Thus the main requirement in today&#39;s Cloud-based processing is how to build medical imaging architectures which can benefit from Cloud-based processing, particularly for Thyroid Disease Diagnosis and in particular Hashimoto Disease. 
         [0017]    Now that hand held devices have come into the world such as iPad, Samsung tablets or iPhones, it is thus important to understand how to build medical imaging architectures which has several tiers or layers in their architectural designs. This innovative application demonstrates an imaging-based architecture utilizing the Cloud-based processing. The application shows coverage for Thyroid Cancer Diagnosis and in particular Hashimoto Disease. Besides this, the application can be extended to vascular imaging or Cardiac imaging, gynecological imaging, prostate cancer imaging and liver cancer imaging, but is extendable to other anatomies as well. 
         [0018]    In view of the foregoing, it is a primary object of the present invention to provide a novel method and apparatus for automated mobile data mining from ultrasound images for diagnostic and monitoring application, particular Hashimoto Disease of Thyroid organ, and further providing extensions to MR or CT images and in general to any other imaging-based data mining application. 
         [0019]    It is another object of the present invention to develop a mobile-based architecture which can process images by distributing components of the architecture in different Clouds, but same physical location. 
         [0020]    It is another object of the present invention to develop a data mining architecture having the business layer in one Cloud while running the Persistence Layer in another Cloud, not necessarily in the same physical location, particularly applied to the Thyroid Disease Management and in particular for the Hashimoto Disease Diagnosis. 
         [0021]    It is another object of the present invention to develop an image-based data mining Cloud-based application which can have multiple-tenants and multiple-users. This data mining application can be where the Business layer is for cardiovascular application (such as IMT measurement, IMTV measurement, Plaque Characterization for Symptomatic vs. Asymptomatic classification of plaque, Stroke Risk computation, and monitoring stroke risk), or urology application such as benign vs. malignant tissue prostate tissue classification for prostate cancer, or gynecological application for classification of ovarian cancer or benign vs. malignant thyroid cancer for endocrinology application, particularly Hashimoto Disease Diagnosis and Classification, or for liver application such as a classification of fatty liver disease (FLD) compared to normal liver. 
         [0022]    It is another object of the present invention to provide different configuration options in the Business Layer controlled by the Presentation Layer, where the Presentation Layer can control wirelessly different configurations. Each configuration can be another scientific method for generation of clinical information, such as different set of classifiers used for training and testing during the Thyroid Disease Diagnosis and in particular Hashimoto Disease Diagnosis. 
         [0023]    It is another object of the present invention to provide multi-tenancy for data mining applications using distributed architectures, where data mining application can be Business layer for (a) cardiovascular application (such as IMT measurement, IMTV measurement, Plaque Characterization for Symptomatic vs. Asymptomatic classification of plaque, Stroke Risk computation, and monitoring stroke risk); (b) prostate cancer application such as benign vs. malignant prostate tissue classification or characterization for prostate cancer); (c) ovarian cancer tissue characterization and classification; or (d) thyroid cancer application (such as benign vs. malignant thyroid tissue classification or characterization for thyroid cancer) and in particular Diagnosis of Thyroid Disease and its management; or (e) classification of liver tissue such as Fatty Liver Disease. 
         [0024]    It is another object of the present invention to provide multi-tenancy for data mining applications using, distributed architectures, where multi-tenancy can be using different imaging modality like MRI, CT, Ultrasound or a combination of these for fusion. The multi-tenancy set-up has data mining application where Business layer is: a) cardiovascular application (such as IMT measurement, IMTV measurement, Plaque Characterization for Symptomatic vs. Asymptomatic classification of plaque, Stroke Risk computation, and monitoring stroke risk); (b) prostate cancer application (such as benign vs. malignant prostate tissue classification or characterization for prostate cancer); (c) ovarian cancer tissue characterization and classification; or (d) thyroid cancer application (such as benign vs. malignant thyroid tissue classification or characterization for thyroid cancer) and in particular Hashimoto Disease Management; or (e) classification of liver tissue such as Fatty Liver Disease. 
         [0025]    It is another object of the present invention to provide data mining applications using distributed architectures, where the presentation layer can be hand-held device like iPhone, iPad, Samsung Tablet or notebook or laptop or desktop and data mining application can be for (for (a) cardiovascular application (such as IMT measurement, IMTV measurement, Plaque Characterization for Symptomatic vs. Asymptomatic classification of plaque, Stroke Risk computation, and monitoring stroke risk); (b) prostate cancer application (such as benign vs. malignant prostate tissue classification or characterization for prostate cancer); (c) ovarian cancer tissue characterization and classification; or (d) thyroid cancer application (such as benign vs. malignant thyroid tissue classification or characterization for thyroid cancer) and in particular Hashimoto Disease Diagnosis and Management or (e) classification of liver tissue such as Fatty Liver Disease. 
         [0026]    It is another object of the present invention to provide data mining applications where Business layer for (a) cardiovascular application (such as NT measurement, IMTV measurement. Plaque Characterization for Symptomatic vs. Asymptomatic classification of plaque. Stroke Risk computation, and monitoring stroke risk); (b) prostate cancer application (such as benign vs. malignant prostate tissue classification or characterization for prostate cancer); (c) ovarian cancer tissue characterization and classification; or (d) thyroid cancer application (such as benign vs. malignant thyroid tissue classification or characterization for thyroid cancer); and in particular Hashimoto Disease Management or (e) classification of liver tissue such as Fatty Liver Disease, such that it can process the B-mode ultrasound or RF-mode ultrasound image 
         [0027]    It is another object of the present invention to provide a method to diagnose a Thyroid Disease, in particular Hashimoto Disease using a combination of training-based image classification, system. 
         [0028]    It is another object of the present invention to provide a method to diagnose a Thyroid Disease, in particular Hashimoto, Disease using a combination of training-based image classification system, where the training system (off line system) uses a set of grayscale features such as Entropy features, Gabor wavelet features, Inverse Moment Features, Higher Order Spectra Features. 
         [0029]    It is another object of the present invention to provide a method to diagnose a Thyroid Disease, in particular Hashimoto, Disease using a combination of training-based image classification system and testing based image classification system (on line process), where the testing system uses a set of grayscale features such as Entropy features, Gabor wavelet features, Inverse Moment Features and Higher Order Spectra Features. 
         [0030]    It is another object of the present invention to provide a method to diagnose a Thyroid Disease, in particular Hashimoto, Disease using a combination of training-based image classification system and testing based image classification system, where the testing system uses a set of grayscale features such as Entropy features, Gabor wavelet features, Inverse Moment Features, Higher Order Spectra Features, such that a feature selection system is able to select the beast combination of features for training and testing classifiers in online and offline processing. 
         [0031]    It is another object of the present invention to provide mobile data mining application where Business layer can be a 2D processing unit or a 3D processing unit. 
         [0032]    It is another object of the present invention to provide mobile data mining application where Business layer can be a 2D processing unit or a 3D processing unit for diagnostic and monitoring application with different configuration options for the Business Layer. 
         [0033]    It is another object of the present invention to provide mobile data mining application where Business layer can be a 2D processing unit or a 3D processing unit for diagnostic and monitoring application with different configuration options for the Business Layer, where these applications use training-based systems. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0034]    The various embodiments is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which: 
           [0035]      FIG. 1  illustrates an example of mobile architecture system. 
           [0036]      FIG. 2  shows an illustrative example of multi-user application using cloud. 
           [0037]      FIG. 3  shows an illustrative example of business layer and persistence layer combined on a cloud. 
           [0038]      FIG. 4  shows an illustrative example of multi-tenancy approach with business layer and persistence layers in ultrasound framework. 
           [0039]      FIG. 5  shows an illustrative example of multi-tenancy approach with business layer and persistence layers in MR framework. 
           [0040]      FIG. 6  shows an illustrative example of multi-tenancy approach with business layer and persistence layers in CT framework. 
           [0041]      FIG. 7  shows an illustrative example of configuration options from presentation layer for a cloud-based setting. 
           [0042]      FIG. 8  shows an illustrative example of multiple clouds demonstrating the components of the applications hosted by different clouds. 
           [0043]      FIG. 9  shows an illustrative example of business logic and persistence layers for Hashimoto Disease diagnosis. 
           [0044]      FIG. 10  shows an illustrative example of business logic that uses the combination of different feature processors for computing different on-line features. 
           [0045]      FIG. 11  shows an illustrative example of business logic that uses the combination of different feature processors using a combination of relative entropy, relative energy, probability of entropy and probability of energy for computing different on-line features 
           [0046]      FIG. 12  shows an illustrative example on-line Hashimoto Disease decision making. 
           [0047]      FIG. 13  shows the overall view of the system. 
           [0048]      FIG. 14  shows a diagrammatic representation of machine in the example form of a computer system within which a set of instructions when executed may cause the machine to perform any one or more of the methodologies discussed herein. 
       
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
       [0049]      FIG. 1  show the example embodiment  100  of the architecture where the application is split into three tiers: Tier-1 is the presentation layer and Tier-2 and Tier-3 are the business layer and persistence layers. The main advantage of this data mining applications which require large space and still be able to maintain near real-time applications. Another key advantage of such architecture is the ability to decouple business and persistence layers in different clouds and still be able to execute data mining applications. An example embodiment can be for vascular application for atherosclerosis disease monitoring, men&#39;s urology application, women&#39;s urology application, breast mammography application, liver application, cardiac application, kidney application and thyroid disease application. Blocks  200 ,  210  and  220  represent different health care systems connected to the cloud  300  having architectures  400  and  500  called as Tier-2 and Tier-3. The connection between the health care systems  200 ,  210  and  220  to the Cloud  300  is shown using links  230 ,  240  and  250 , respectively. Inside each health care system run the patient data collection systems using the scanners:  205 ,  215 , and  225 . These scanners collected image data on the patient  201 ,  211  and  221  using the scanners  202 ,  212  and  222 , respectively. The physician or technologist is shown in  FIG. 203 ,  213  or  223 . The image data collected is shown in the blocks  206 ,  216  and  226  respectively, which is sent to the cloud  300  using the links  230 ,  240  and  250 , respectively. This application uses automated data mining business layer  401 ) and persistence layer  500  in the cloud  300 . The hand-held devices  204 ,  214  and  224  (Tier-1) are used for running the data mining applications receding in the cloud  300 . These hand-held devices can be iPad or a Tablet or a notebook or a laptop or mobile device. This application can be useful for the architecture for a) cardiovascular application (such as IMT measurement, IMTV measurement, Plaque Characterization for Symptomatic vs. Asymptomatic classification of plaque, Stroke Risk computation, and monitoring, stroke risk) (b) prostate cancer application (such as benign vs. malignant prostate tissue classification or characterization for prostate cancer); (c) ovarian cancer tissue characterization and classification; or (d) thyroid cancer application (such as benign vs. malignant thyroid tissue classification or characterization for thyroid cancer); or (e) classification of liver tissue such as Fatty Liver Disease, such that it can process the B-mode ultrasound or RF-mode ultrasound images and (f) thyroid disease classification such as benign thyroid or malignant thyroid or Hashimoto Disease Classification. 
         [0050]      FIG. 2  shows the example embodiment  600  where multiple healthcare providers having multiple Tier-1&#39;s and are connected to the Cloud running the Tier-2 and Tier-3. For example  602  and  603  represent one health care system where the Tier-1 block  603  is interacting with the Cloud  300  which has the Tier-2, block  400  and Tier-3, block  500  using a wireless system. Similar pairs can be blocks  604  and  605  representing a scanner and a presentation layer in combination. A cyclic order of such combination representing several healthcare systems can be  606  and  607 ;  608  and  609 ;  610  and  611 ;  612  and  613 ;  614  and  615 , respectively. Those skilled in the art can add more clients in such a cyclic framework. The wireless signals are represented by  620  which are sending the client signals to the Tier-2 which in return can store the intermediate results in Tier-1 Using this architecture, one can also send signal from Tier-1 such as ( 603 ,  605 ,  607 ,  609 ,  611 , 613  and  615 ) to Tier-3 receding in the Cloud  300 . The main advantage of such a system is the decoupling of the Tier-1 from Tier-2 and Tier-3. Those skilled in the art of using client-server model, can reside the Tier-2 on one server and Tier-3 in another server or both Tier-2 and Tier-3 in the same Cloud. Such an application of multi-tenancy can be adapted for a) cardiovascular application (such as IMT measurement, IMTV measurement, Plaque Characterization for Symptomatic vs. Asymptomatic classification of plaque, Stroke Risk computation, and monitoring stroke risk); (b) prostate cancer application (such as benign vs. malignant prostate tissue classification or characterization for prostate cancer); (c) ovarian cancer tissue characterization and classification; or (d) thyroid cancer application (such as benign vs. malignant thyroid tissue classification or characterization for thyroid cancer); or (e) classification of liver tissue such as Fatty Liver Disease, such that it can process the B-mode ultrasound or RF-mode ultrasound images and (f) thyroid disease classification such as benign thyroid or malignant thyroid or Hashimoto Disease Classification, where these applications are the business layers in the three tier architectures. 
         [0051]      FIG. 3  shows the example embodiment  700 , where the Cloud  300  hosts the Business Layer  800  and Persistence Layer  900 . The image data is present in the Cloud storage  710 . When the Tier-1 presentation layer  715  interacts with the Cloud hosting the application having Tier-2 and Tier-3, then the Clinical information is generated by the Business Logic Layer  800 . This Clinical information can be seen on the presentation layer  715 . The persistence, layer  900  has the data information which is saved for the application. This can be a database management system which stores the clinical information  920  by running the data mining application. Such a model is very suitable for diagnostic, treatment support and monitoring of the diseases. An example can be for cardiovascular risk application for (a) cardiovascular application (such as IMT measurement, IMTV measurement, Plaque. Characterization for Symptomatic vs. Asymptomatic classification of plaque, Stroke Risk computation, and monitoring stroke risk); (b) prostate cancer application (such as benign vs. malignant prostate tissue classification or characterization for prostate cancer); (c) ovarian cancer tissue characterization and classification; or (d) thyroid cancer application (such as benign vs. malignant thyroid tissue classification or characterization for thyroid cancer); or (e) classification of liver tissue such as Fatty Liver Disease and (f) thyroid disease classification such as benign thyroid or malignant thyroid or Hashimoto Disease Classification, where these applications are the business layers in the three tier architectures such that it can process the B-mode ultrasound or RF-mode ultrasound images. Under cardiovascular risk, it can compute say the intima-media thickness for the distal wall for the common carotid artery of ultrasound. Along the same lines can be the lumen quantification or lumen segmentation of the common carotid artery ultrasound or any blood vessels. This model is applicable for CCA, brachial artery, aortic arch and peripheral artery. Those skilled in the art can use this application for other arterial systems. Such an application can be for any 2D or 3D application. Another application can be the image data  710  that can be in 3D format and business logic layer  800  can process the image data  710  to give the segmentation results  720  which are being display on the Tier-1 device  710 . Those killed in the art can use an iPad, iPhone or Samsung hand held devices for display of the transformed images or segmented images. An example can be a 3D Thyroid image data mining system such as ThyroScan™. 
         [0052]      FIG. 4  shows the example embodiment  1000 , where the Cloud  300  hosts the Business Layer  400  and Persistence Layer  500 . Health care system is represented by blocks  200 ,  210  and  220 . The health care system  200  has the block  207  can be used as a body scanner says an ultrasound scanning system. Similarly, there can be another health care system  210  that has the scanner represented by the block  217 . The embodiment  1000  also shows as an example where the third health care system is represented by  220  having the scanner block  227  and is an Ultrasound scanning system. The ultrasound scanner can be a portable ultrasound scanner or an ultrasound scanner having, a cart-based mobile in the hospital or health care system. The embodiment also shows the setup where the patient comes for scanning in the health care system. For example, patient block  201  shows the scanner  207  scanning the patient to generate the image data  206  in the healthcare system  200 . Similarly, the embodiment also shows the setup where the patient block  211  shows the scanner  217  scanning the patient to generate the image data  216  in the healthcare system  210 . Also shown are the wireless system  230 ,  240  and  250 . Such an set-up can use for (a) cardiovascular application (such as IMT measurement, MTV measurement, Plaque Characterization for Symptomatic vs. Asymptomatic classification of plaque, Stroke Risk computation, and monitoring stroke risk); (b) prostate cancer application (such as benign vs. malignant prostate tissue classification or characterization for prostate cancer); (c) ovarian cancer tissue characterization and classification; or (d) thyroid cancer application (such as benign vs. malignant thyroid tissue classification or characterization for thyroid cancer); or (e) classification of liver tissue such as Fatty Liver Disease; or f) thyroid disease classification such as benign thyroid or malignant thyroid or Hashimoto Disease Classification, where these applications are the business layers in the three tier architectures such that it can process the B-mode ultrasound or RF-mode ultrasound images. 
         [0053]      FIG. 5  shows the example embodiment  1100 , where multiple tenants  1110 ,  1120  and  1130  are shown running the data mining application using Cloud  300  which hosts the Business Layer  400  and Persistence Layer  500 . Tenant  1110  is the heath care system having the imaging device  208  such as MRI and the technologist or doctor  203  for scanning protocol  205  to yield the image data  206  for the patient  201 . Similarly, there is a tenant  1120  is the heath care system having the imaging device  218  such as MRI and the technologist or doctor  213  for scanning protocol  215  to yield the image data  216  for the patient  211 . Similarly, there is a tenant  1130  is the heath care system having the imaging device  228  such as MRI and the technologist or doctor  223  for scanning protocol  225  to yield the image data  226  for the patient  221 . Also shown are the wireless system  230 ,  240  and  250 . Such an set-up is used for (a) cardiovascular application (such as IMT measurement, IMTV measurement, Plaque Characterization for Symptomatic vs. Asymptomatic classification of plaque, Stroke Risk computation, and monitoring, stroke risk); (b) prostate cancer application (such as benign vs. malignant prostate tissue classification or characterization for prostate cancer); (c) ovarian cancer tissue characterization and classification; or (d) thyroid cancer application (such as benign vs. malignant thyroid tissue classification or characterization for thyroid cancer); or (c) classification of liver tissue such as Fatty Liver Disease or f) thyroid disease classification such as benign thyroid or malignant thyroid or Hashimoto Disease Classification, where these applications are the business layers in the three tier architectures such that it can process MR images. 
         [0054]      FIG. 6  shows the example embodiment  1200 , where multiple tenants  1210 ,  1220  and  1230  are shown running the data mining application using Cloud  300  which hosts the Business Layer  400  and Persistence Layer  500 . Tenant  1210  is the heath care system having the imaging device  208  such as CT and the technologist or doctor  203  for scanning protocol  205  to yield the image data  206  for the patient  201 . Similarly, there is a tenant  1220  is the heath care system having the imaging device  218  such as CT and the technologist or doctor  213  for scanning protocol  215  to yield the image data  216  for the patient  211 . Similarly, there is a tenant  1230  is the heath care system having the imaging device  228  such as CT and the technologist or doctor  223  for scanning protocol  225  to yield the image data  226  for the patient  221 . Also shown are the wireless system  230 ,  240  and  250 . Such an set-up is used for (a) cardiovascular application (such as IMT measurement, IMTV measurement, Plaque Characterization for Symptomatic vs. Asymptomatic classification of plaque, Stroke Risk computation, and monitoring stroke risk); (b) prostate cancer application (such as benign vs. malignant prostate tissue classification or characterization for prostate cancer); (c) ovarian cancer tissue characterization and classification; or (d) thyroid cancer application (such as benign vs. malignant thyroid tissue classification or characterization for thyroid cancer); or (e) classification of liver tissue such as Fatty Liver Disease or f) thyroid disease classification such as benign thyroid or malignant thyroid or Hashimoto Disease Classification, where these applications are the business layers in the three tier architectures such that it can process CT images. 
         [0055]      FIG. 7  shows the example embodiment  900  showing different configuration options from presentation layer for a cloud-based setting. Business Logic. Layer  800  received the image data from the tenant using the wireless system, which in turn processes the clinical information and gives the output  920 . The configuration option  810 ,  820  and  830  are available for choosing the different types of engines such as Scientific Engine Type 1, Scientific Engine Type 2 or Scientific Engine Type 3. Such a business layer  800  can be for (a) cardiovascular application (such as IMT measurement, IMTV measurement, Plaque Characterization for Symptomatic vs. Asymptomatic classification of plaque. Stroke Risk computation, and monitoring stroke risk); (b) prostate cancer application (such as benign vs. malignant prostate tissue classification or characterization for prostate cancer); (c) ovarian cancer tissue characterization and classification; or (d) thyroid cancer application (such as benign vs. malignant thyroid tissue classification or characterization for thyroid cancer); or (e) classification of liver tissue such as Fatty Liver Disease; or f) thyroid disease classification such as benign thyroid or malignant thyroid or Hashimoto Disease Classification, where these applications are the business layers in the three tier architectures such that it can process the B-mode ultrasound or RF-mode ultrasound images. Tier 1,  710  can interact with the clinical information  920  to display the clinical diagnosis on  710 , such as iPhone, iPad, Samsung Table, or even laptop, notebook or Desktop-based display devices. The persistence layer process  1000  processes the clinical information  920  and stores in the persistence layer. This information can also be accessed by Tier-1,  710 . Output  930  is the information which is saved in the cloud or local server. 
         [0056]      FIG. 8  shows the example embodiment  1300  showing different configuration options from presentation layer for a cloud-based setting. Business Logic Layer  1320  receives the image data from the tenant using the wireless system, which in turn processes the clinical information and gives the output  1330 . Such a business layer  1320  can be for (a) cardiovascular application (such as IMT measurement. IMTV measurement, Plaque Characterization for Symptomatic vs. Asymptomatic classification of plaque, Stroke Risk computation, and monitoring stroke risk); (b) prostate cancer application (such as benign vs. malignant prostate tissue classification or characterization for prostate cancer); (c) ovarian cancer tissue characterization and classification; or (d) thyroid cancer application (such as benign vs. malignant thyroid tissue classification or characterization for thyroid cancer); or (e) classification of liver tissue such as Fatty Liver Disease; or f) thyroid disease classification such as benign thyroid or malignant thyroid or Hashimoto Disease Classification, where these applications are the business layers in the three tier architectures such that it can process the B-mode ultrasound or RF-mode ultrasound images. The configuration option is available for choosing the different types of engines such as Scientific Engine Type 1, Scientific Engine Type 2 or Scientific Engine Type 3. Tier 1,  710  can interact with the clinical information  1330  to display the clinical diagnosis on  710 , such as iPhone, iPad, Samsung Table, or even laptop, notebook or Desktop-based display devices. The persistence layer process  1340  processes the clinical information  1330  and stores in the persistence layer. This information can also be accessed by Tier-1,  710 . Output  1350  is the information which is saved in the cloud or local server. It is important to note that Persistence layer  1340  and clinical data results  1350  are stored in the cloud  1302  while Business Layer  1320  and the clinical information results  1330  are stored in the cloud  1301 . Even though the entire data mining application is responding from the presentation layer  710 , but the rest of the components are partitioned, in different clouds using wireless operations. Such as business layer  1320  can be for (a) cardiovascular application (such as IMT measurement, IMTV measurement, Plaque Characterization for Symptomatic vs. Asymptomatic classification of plaque, Stroke Risk computation, and monitoring stroke risk); (b) prostate cancer application (such as benign vs. malignant prostate tissue classification or characterization for prostate cancer); (c) ovarian cancer tissue characterization and classification; or (d) thyroid cancer application (such as benign vs. malignant thyroid tissue classification or characterization for thyroid cancer); or (e) classification of liver tissue such as Fatty Liver Disease; or f) thyroid disease classification such as benign thyroid or malignant thyroid or Hashimoto Disease Classification, where these applications are the business layers in the three tier architectures such that it can process the B-mode ultrasound or RF-mode ultrasound images. 
         [0057]      FIG. 9  illustrates an example embodiment  1600  showing the Hashimoto Disease Diagnosis system. Block  1620  receives the image data from health care system in the Cloud 1. Processor  1630  is controlled by block  1625 , which is the presentation layer. Block  1630  gives the on tine features of the Thyroid grayscale images. These are fed to the ThyroScan™ class Processor  1670  as part of the Business Layer which yields Hashimoto Binary Decisions as a diagnostic index, and saved in block  1690  in the persistence cloud 2. Block  1625  is a hand-held device which can display the Hashimoto Diagnostic Decision using, the channel  1665 . Block  1670  allows saving the image data into the Persistence Layer  1690 . 
         [0058]      FIG. 10  illustrates an example embodiment  1630  showing the Hashimoto grayscale on line feature extraction system. Block  1621 , block  1623 , block  1625 , and block  1627  use four different kinds of on-line processors for computing four different kinds of features. Block  1621  is an on-line entropy processor which yields the on-line entropy features  1622 . Block  1623  is a on-line Gabor Wavelet Processor that computes the on-line Gabor Wavelet Features, Block  1625  is an on-line Inverse Moment Processor and computes the on-line inverse moment features  1626 . Block  1627  is a on-line HOS processor which computes the on-line HOS features. The novelty of this set-up is the combination of this feature which constitutes the support in diagnosis of Hashimoto Disease. Block  1629  uses a Feature Selection Processor which finally gives the on line features  1650 . The on-line features are fed to the ThyroScan Class Processor  1670  as detailed out in  FIG. 9 . The block  1610  can be one cloud which feed to the block  1690  in cloud 2. The same concept is applied for the training-based system by the block  1665  as shown in  FIG. 11 . 
         [0059]      FIG. 11  illustrates another example embodiment  1641  showing the Hashimoto grayscale on line feature extraction system. Block  1631 , block  1633 , block  1625 , and block  1637  use four different kinds of on-line processors for computing four different kinds of features. Block  1631  is an on-line relative wavelet energy processor which yields the on-line relative wavelet energy features  1632 . Block  1633  is relative wavelet entropy Processor that computes the on-line relative entropy features  1634 . Block  1635  is a probability of energy processor which yields online probability of energy features  1636 . Block  1637  is an on-line probability of entropy processor which computes the on-line probability of entropy features. The novelty of this set-up is the combination of this feature which constitutes the support in diagnosis of Hashimoto Disease. Block  1639  uses a Feature Selection Processor which finally gives the on line features  1651 . The on-line features are fed to the ThyroScan Class Processor  1670  as detailed out in  FIG. 9 . The block  1610  can be one cloud which feed to the block  1690  in cloud  2 . The same concept is applied for the training-based system by the block  1665  as shown in  FIG. 11 . 
       Stationary Wavelet Transform (SWT) for Feature Extraction 
       [0060]    Wavelet transform captures both the spatial and frequency information of a signal. Discrete Wavelet Transform (DWT) uses filter banks composed from finite impulse response filters to decompose signals into low and high pass components. The low pass component contains information about slow varying signal characteristics, and the high pass component contains information about sudden changes in the signal. DWT, however, is not a time-invariant transform. The translation invariance of DWT can be restored by using Stationary Wavelet Transform. 
         [0061]    A 2D sub-band transform with three levels of decomposition. When low pass filtering, using filter g[n] is applied to both the rows and columns of the image, the LL coefficients are obtained which are called the approximation coefficients ‘A’. These coefficients are representative of the total energy in the images. When low pass filtering is applied to the rows, and high pass filtering using filer h[n] is applied to the column values, the resultant HL coefficients contain the vertical details of the image ‘V’ Row-wise high pass filtering and column-wise low pass filtering result in the LH coefficients, which contain the horizontal details of the image ‘H’. High pass filtering of both row and column values results in the finest-scale HH coefficients, which contain the diagonal details of the image D. Decomposition is further performed on sub-band LL to attain the next coarser scale of wavelet coefficients. The input approximation coefficients cA j  and the results for level j+l. In this application, we first converted the image to grayscale range of [0, 255] and then applied SWT using rhio3.1 as the mother wavelet. 
         [0062]    After obtaining, the wavelet coefficients at each level of the three-level SWT decomposition, we determined the following features for each of the ten subsets of coefficients: (a) Relative Wavelet Energy (RWEng); (b) Relative Wavelet Entropy (RWEnt); (c) Probability of Energy (PEng), and (d) Probability of Entropy (Pent). Energy probability distribution in scales is the relative wavelet energy. Relative wavelet entropy tells how similar a probability distribution p j  is with respect to another probability distribution q j  referenced. In the following, sample equations. Eng N   a  indicates the energy of the approximation coefficients cA obtained at level N. Eng N   h  indicates the energy of the horizontal detail coefficients cD h  obtained at level N. Eng N   v  indicates the energy of the vertical detail coefficients cD v  obtained at level N. Eng N   d  indicates the energy of the diagonal detail coefficients cD d  obtained at level N. Similar definitions hold true for the other terms used in the equations. 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       RWEng_cA 
                       1 
                     
                      
                     
                       : 
                     
                      
                     
                         
                     
                      
                     
                       RWEng 
                       1 
                       a 
                     
                   
                   = 
                   
                     
                       Eng 
                       1 
                       a 
                     
                     
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Eng 
                           N 
                           a 
                         
                       
                       + 
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Eng 
                           N 
                           d 
                         
                       
                       + 
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Eng 
                           N 
                           v 
                         
                       
                       + 
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Eng 
                           N 
                           h 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
             
               
                 
                   
                     
                       PEng_cA 
                       2 
                     
                      
                     
                       : 
                     
                      
                     
                         
                     
                      
                     
                       PEng 
                       2 
                       a 
                     
                   
                   = 
                   
                     
                       Eng 
                       2 
                       a 
                     
                     
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Eng 
                           N 
                           a 
                         
                       
                       + 
                       
                         Eng 
                         2 
                         d 
                       
                       + 
                       
                         Eng 
                         2 
                         h 
                       
                       + 
                       
                         Eng 
                         2 
                         v 
                       
                     
                   
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
             
               
                 
                   
                     
                       RWEnt_cA 
                       1 
                     
                      
                     
                       : 
                     
                      
                     
                         
                     
                      
                     
                       RWEnt 
                       1 
                       a 
                     
                   
                   = 
                   
                     
                       Ent 
                       1 
                       a 
                     
                     
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Ent 
                           N 
                           a 
                         
                       
                       + 
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Ent 
                           N 
                           d 
                         
                       
                       + 
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Ent 
                           N 
                           v 
                         
                       
                       + 
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Ent 
                           N 
                           h 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   3 
                   ) 
                 
               
             
             
               
                 
                   
                     
                       RWEnt_cH 
                       2 
                     
                      
                     
                       : 
                     
                      
                     
                         
                     
                      
                     
                       RWEnt 
                       2 
                       h 
                     
                   
                   = 
                   
                     
                       Ent 
                       2 
                       h 
                     
                     
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Ent 
                           N 
                           a 
                         
                       
                       + 
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Ent 
                           N 
                           d 
                         
                       
                       + 
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Ent 
                           N 
                           v 
                         
                       
                       + 
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Ent 
                           N 
                           h 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   4 
                   ) 
                 
               
             
             
               
                 
                   
                     
                       RWEnt_cV 
                       2 
                     
                      
                     
                       : 
                     
                      
                     
                         
                     
                      
                     
                       RWEnt 
                       2 
                       v 
                     
                   
                   = 
                   
                     
                       Ent 
                       2 
                       v 
                     
                     
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Ent 
                           N 
                           a 
                         
                       
                       + 
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Ent 
                           N 
                           d 
                         
                       
                       + 
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Ent 
                           N 
                           v 
                         
                       
                       + 
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Ent 
                           N 
                           h 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   5 
                   ) 
                 
               
             
             
               
                 
                   
                     RWEnt_A 
                      
                     
                       : 
                     
                      
                     
                         
                     
                      
                     
                       RWEnt 
                       N 
                       a 
                     
                   
                   = 
                   
                     
                       
                         ∑ 
                         N 
                       
                        
                       
                         Ent 
                         N 
                         a 
                       
                     
                     
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Ent 
                           N 
                           a 
                         
                       
                       + 
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Ent 
                           N 
                           d 
                         
                       
                       + 
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Ent 
                           N 
                           v 
                         
                       
                       + 
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Ent 
                           N 
                           h 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   6 
                   ) 
                 
               
             
             
               
                 
                   
                     
                       PEnt_cA 
                       2 
                     
                      
                     
                       : 
                     
                      
                     
                         
                     
                      
                     
                       PEnt 
                       2 
                       a 
                     
                   
                   = 
                   
                     
                       Ent 
                       2 
                       a 
                     
                     
                       
                         
                           ∑ 
                           N 
                         
                          
                         
                           Ent 
                           N 
                           a 
                         
                       
                       + 
                       
                         Ent 
                         N 
                         d 
                       
                       + 
                       
                         Ent 
                         N 
                         h 
                       
                       + 
                       
                         Ent 
                         N 
                         v 
                       
                     
                   
                 
               
               
                 
                   ( 
                   7 
                   ) 
                 
               
             
           
         
       
     
         [0000]    where Eng N   a =Σ k |cA N (k)| 2 ; Ent N   1 =−Σ k cA N   2 (k)log(cA N   2 (k))
 
where N is the number of levels of decomposition, taken as 3; and k is the number of coefficients at each decomposition level.
 
         [0063]      FIG. 12  shows the example embodiment  1670  showing the table concept for an image-based data mining application using the Cloud Concept to Hashimoto Disease Diagnosis utilizing the ThyroScan Test Classifier. Block  1650  receives the online grayscale features. Block  1677  shows the select processor for selection of the type of the classifier, given three sets of classifiers:  1681 ,  1679  and  1680 . Select Trigger  1676  is sent to the Select Processor  1677  and corresponding Classifier Type is selected out of  1681 ,  1679  and  1680  and the output  1685  is fed to the block  1675  which is used for classification of the online feature of the grayscale thyroid scan  1650 . Note that the block  1675  uses off-line Hashimoto features along with the on-line Thyroid Scan features and yields the Hashimoto binary decision if the Thyroid has the Hashimoto Disease or not. 
         [0064]      FIG. 13  shows the example embodiment  2000  of the data mining application. Data mining application  2010  using single Clouds or a set of Clouds which consist of Tier-1 as a presentation layer. Tier-2 is the business layer and Tier-3 is the Persistence Layer. The set-up  2010  is used for diagnostic and monitoring application. The Presentation Layer in data mining framework for cardiovascular risk assessment, stroke risk assessment, liver disease assessment, vascular imaging assessment such as IMT measurement using AtheroEdge™, plaque characterization using Atheromatic™, stroke risk assessment using AtheroRisk™, atherosclerosis disease monitoring using Atherometer™, Vessel Analysis using, VesselOmeasure™, fatty liver disease characterization using Symptosis™, tissue characterization for prostate using UroImage™ and Thyroid Disease Diagnosis, particularly Hashimoto Disease Classification and Management. Block  2020  receives the image data from the Cloud for processing. Block  2030  runs the business layer and Block  2040  is the Persistence Layer for the application. Block  2050  is the block where the application can use multiple tenancy-multi use frame work. Block  2060  show the Hashimoto Disease Diagnosis Application using multiple image-based setting such as Ultrasound, MR, CT, or its fusion. 
         [0065]      FIG. 14  shows a diagrammatic representation of machine in the example form of a computer system  2700  within which a set of instructions when executed may cause the machine to perform any one or more of the methodologies discussed herein. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of as server or a client machine in server-client network environment, or as a peer machine in as peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing as set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” can also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein. 
         [0066]    The example computer system  2700  includes a processor  2702  (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both), a main memory  2704  and a static memory  2706 , which communicate with each other via a bus  2708 . The computer system  2700  may further include a video display unit  2710  (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The computer system  2700  also includes an input device  2712  (e.g., a keyboard), a cursor control device  2714  (e.g., a mouse), a disk drive unit  2716 , a signal, generation device  2718  (e.g., a speaker) and a network interface device  2720 . 
         [0067]    The disk drive unit  2716  includes a machine-readable medium  2722  on which is stored one or more sets of instructions (e.g., software  2724 ) embodying any one or more of the methodologies or functions described herein. The instructions  2724  may also reside, completely or at least partially, within the main memory  2704 , the static, memory  2706 , and/or within the processor  2702  during execution thereof by the computer system  2700 . The main memory  2704  and the processor  2702  also may constitute machine-readable media. The instructions  2724  may further be transmitted or received over a network  2726  via the network interface device  2720 . While the machine-readable medium  2722  is shown in an example embodiment to be a single medium, the term “machine-readable medium” should be taken to include a non-transitory single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “machine-readable medium” can also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the various embodiments, or that is capable of storing, encoding or carrying data structures utilized by or associated with such a set of instructions. The term “machine-readable medium” can accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media. 
         [0068]    The Abstract of the Disclosure is provided to comply with 17 C.F.R. §1.72(b), requiring an abstract that will allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separate embodiment.