Patent Publication Number: US-7719543-B2

Title: Methods and apparatus for method to optimize visual consistency of images using human observer feedback

Description:
FIELD OF THE INVENTION 
   This invention relates in general to image enhancement in digital image processing and in particular to the brightness and contrast scale rendering to improve the visibility of radiographic image. 
   BACKGROUND OF THE INVENTION 
   The growing availability of digital technology is having an impact on medical imaging practice. Image archiving and communications systems allow for digital acquisition, image processing and display for a wide variety of medical imaging modalities in a digital environment. Digital medical imaging has led to an increase in productivity for the health care professionals who need to utilize images as part of patient care. To maintain this high productivity it is important for the health professional to extract diagnostically useful information from the medical images at a digital display. Digital displays have many advantages over film such as a wide variety of tools to manipulate and process the image to enhance structures or make them more visible. Yet another advantage is the availability of tools to segment images, detect, and classify lesions and to make comparisons with previous images that can be viewed from imaging archives. Chiefly due to these tools, the radiologist now has a considerable amount of influence over the quality of the image. While overall enhancing quality assurance (QA) it has led to variability in how perception and observer performance affects the quality assurance process because a completely new array of considerations arises. There are significant variations in a general image appearance with different anatomies, views, and x-ray image acquisitions. 
   For a radiologist, the acquired digital x-ray image may not be always satisfactory from a diagnostic point of view. Some of the image areas may be too dark, or there may be insufficient contrast in the regions of interest. Fortunately, with digital radiography it is possible to adjust the image brightness and contrast of the image by adjusting the window level (WL) and the window width (WW) of the image. However, WL and WW adjustments need to be such that overall brightness and contrast are consistent from one image to the next for a given set of conditions. The WL and WW adjustment issue is further complicated by the fact that each user has a personal preference of the consistent appearance. 
   Diagnostic accuracy is certainly the major consideration, but a good quality assurance system can affect observer performance in other ways. Workflow or the amount of time the radiologist spends on an individual image can easily be regarded as a reflection of the quality assurance process. If the display is inadequate in some way, it is quite likely that the radiologist will have to compensate by taking more time to examine the image before rendering a diagnostic decision. When radiographic images are viewed with electronic displays, adjustment of window width and level is a standard practice used to improve the rendering of a region-of-interest. Adjustment of window width and level is most often done manually, most often using a mouse or track-ball, and requires substantial time and effort to obtain a desired result. Window width and level adjustment requires a skilled operator to be accomplished successfully. Further, adjusting window width and level for a region of interest often has the effect of making the remaining areas of the image sub-optimally rendered. In addition to this loss of image quality, adjusting window width and level can also cause a loss of context for the region of interest leading to some areas being under expose while other areas are overexposed. 
   For the reasons stated above, and for other reasons stated below which will become apparent to those skilled in the art upon reading and understanding the present specification, there is a need in the art for automating the selection of window width and window level with minimal user interaction. There is also a need in the art for electronic displays to provide consistent brightness and contrast settings for a set of images. 
   BRIEF DESCRIPTION OF THE INVENTION 
   The above-mentioned shortcomings, disadvantages and problems are addressed herein, which will be understood by reading and studying the following specification. 
   It is therefore an object of the present invention to provide an apparatus and method for adjusting window width and window length parameters of one or more images with minimal user interaction. 
   In one aspect, a method is described for optimizing visual consistency for a dataset of images by using observer and formulated display attributes. The formulated display attributes are derived from an analysis of a sample test set and a sample training set of images from the image dataset. The formulated display attributes from the training set is combined with the observer&#39;s display attributes to determine brightness and contrast adjustment (BCA). The estimated window width and window length are then derived from the calculated BCA and formulated window width and window length. 
   In yet another aspect, a computerized method for automatically modifying the rendering of images to meet an observer preference by selecting a scaling factor defining an observer&#39;s preference for rendered images. The images can be categorized into one or more anatomy and view, tissue thickness, imaging production site, patient attributes, processing type. Further, a unique identifier is used to associate the observer to a selected scaling factor. Once selected the scaling factor is used to map the images that are subsequently displayed in a display terminal. 
   In still another aspect, a computer executable medium capable of directing a processor to perform the actions of receiving display attributes from an observer about a set of images, wherein the observer manually tuned the image display attributes to make images visually consistent, further the process derives optimal set of image display attributes from the received image display attributes of the observer. The processor causing the displaying of one or more images with the derived optimal set of image display attributes on a display device. 
   Systems, clients, servers, methods, and computer-readable media of varying scope are described herein. In addition to the aspects and advantages described in this summary, further aspects and advantages will become apparent by reference to the drawings and by reading the detailed description that follows. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a flowchart of a method performed by a client according to an embodiment for deriving optimal set of scaling factors; 
       FIG. 2  is a flowchart of a method performed by a client according to an embodiment for estimating attribute value; 
       FIG. 3  is a flowchart of a method performed by a client according to an embodiment for applying attribute adjustments when displaying images; 
       FIG. 4  is a block diagram of the hardware and operating environment in which different embodiments can be practiced; 
       FIG. 5  is part of a flowchart of a method performed by a client according to an embodiment for categorizing and selecting images for an observer to review and for estimating attributes; 
       FIG. 6  is part of a flowchart of a method performed by a client according to an embodiment for selecting analysis method for dataset category; 
       FIG. 7  is a diagram illustrating the relationship between display attribute values, scale factor, and images in first set; and 
       FIG. 8  is a diagram illustrating the relationship between attribute values from optimized analysis method and modified attribute values. 
   

   DETAILED DESCRIPTION OF THE INVENTION 
   In the following detailed description, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration specific embodiments which may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the embodiments, and it is to be understood that other embodiments may be utilized and that logical, mechanical, electrical and other changes may be made without departing from the scope of the embodiments. The following detailed description is, therefore, not to be taken in a limiting sense. 
     FIG. 1  is a flowchart of a method  200  performed by a client according to an embodiment. Method  100  meets the need in the art for selection of window width and window level with minimal user interaction. 
   Method  100  includes manual and automatic adjustments ( 104 , 106 ) for a set of images  102  that are combined to derive optimal set of scaling factors  108 . The optimal scaling factors are optimization parameters  110  that when compiled by a suitably programmed computer (e.g., computer  402  at  FIG. 4 ) produce a visually consistent set of images at a display terminal without the need for adjustments by the observer. Method  100  meets the need in the art for automating the selection of window width and window level with minimal user interaction 
   Method  100  begins with action  102  by collecting of one or more images that are capable of being categorized into one or more categories for the purpose of analysis or to extract information that may lead to conclusions about the subject of the image. A possible set of categories can be based on image appearance when viewing on a display terminal. Using image appearance as a basis can produce categories such as anatomy and view, image acquisition parameters, processing type, hospital site or imaging site, anatomically relevant regions, and any other category that may be conceived to distinguish the images. 
   Anatomy and view is a major factor concerning the image appearance. Different body parts have different tissue thicknesses and compositions. Because of these differences incident x-ray will vary widely for each body parts leading to variations in pixel intensities for a given anatomy. For example, the images of chest and foot will inherently have a very different appearance because the tissue compositions of these body parts are quite dissimilar. Additionally, images of same anatomy vary widely in a general image appearance with different views due to different tissue thickness with changing view. A great example of how the same body part can produce inconsistent image appearance is chest lateral and chest posterior-anterior (“PA”) images. 
   Variation in image acquisition parameters can cause a significant effect on the image appearance. Parameters such as Kilo-voltage (kVp), milliamperage-second (mAs), source image receptor (“SID”), etcetera are some of the important parameters to consider during x-ray acquisition process. Milliamperage-second (mAs) is a prime controller of radiographic image density. Since density is a function of exposure and exposure increases proportionally with increasing mAs or until a saturation point the amount of mAs controls the image density and has an effect on image consistency. Concerning the Kilovoltage (kVp) image acquisition parameters it is a major factor deciding image contrast because kVp affects beam penetrability in radiographic densities. As kVp increases, the image contrast decreases, and, as kVp decreases the image contrast increases. Kilovoltage also controls the amount of scatter radiation produced. Increasing kVp increases scatter, thereby reducing image contrast. Radiation source-to-detector distance (SID) alters the beam intensity arriving image receptor as per the inverse square law. This parameter changes the image density and contrast. Image contrast is also affected by filtering techniques such as grid. Grids remove the scatter radiation and improve image contrast. Thus, variations in these acquisition parameters have a profound change in the image appearance. Since major differences in acquisition parameters produce significantly different inherent appearances of images, even windowing will not produce a consistent appearance. 
   Each processing type is designed to obtain a different image look from the same raw image according to the anatomy under consideration and the observer&#39;s preference. 
   Each imaging facility generally has its own fixed set of acquisition parameters for each anatomy and view. It is customary for an image facility to have policies regarding prefer values for each image acquisition parameters such as mAs, kVp, SID, etcetera. The acquisition parameters vary significantly for the different hospital sites and are a significant cause of variation in image consistency. To add to this variations most radiologists prefer a certain image appearance and consequently use a certain customized processing type, which may or may not be same as the other hospital type. 
   The anatomically relevant regions limit image consistency to the area being studied. Consider, for the cervical spine (C-spine) images, consistency can be checked only in the relevant regions of cervical spine, the brightness (and contrast) in other regions of an image such as shoulders, skull etcetera can vary widely with each image and is irrelevant while considering a consistent image appearance. 
   Knowing the sources of variations of image consistency the objective is to develop a window level (W_l) and window width (W_w) estimation technique for digital radiology image so that a set of images of same anatomy and view, same processing type and acquired at the same imaging site will have a consistent image look. The technique should be able to attain a consistent look across all the images in the set rather than achieving the best look for any or all the individual images. The preferred image look varies with each observer that is each observer will have different preferences for image brightness and contrast. The goal then is to achieve a consistent look, which is clinically acceptable to the majority of observers. Secondary goal is to customize this consistent appearance to suit exact preferences of a given observer by fine-tuning of the parameters through a mapping or scaling factor. 
   Other categorization or further narrowing of the above categories is possible. For example, another category could be patient age and size at a broad range of patient ages and sizes, from a toddler to an octogenarian. Generally speaking, the patients from and above 18 years of age are adults; however, many pediatric patients of age over 13 years could be potentially larger than many small adult patients. One can conclude from observing a general trend that the images of patients of ages 13 years and above can be classified as adult cases and otherwise as pediatric cases. This category could be further defined as very small, small, medium, large, and very large patient size. The classification according to patient sizes can be secondary in nature and not use for the formation of image sets and analysis. However, it would be useful for developing different estimation techniques according to patient sizes if needed. Further, it enables one to identify some special cases such as very large or very small patient images, which may give considerable error in the estimation of window settings. 
   Knowing the sources of image inconsistencies the aim is then to develop an estimation technique for all the images of a particular image category, for example, all the images of hand PA, custom 4 processing from imaging site X, by using some sample images from that category enumerated above. To accomplish this aim the number of image sets are separated into a training set (sample set) and a corresponding test set. The training set is used for analysis, development, and selection of an estimation technique while the test set was used to verify the results. The images should be classified in such a way that both training and test sets will have cases of all patient sizes. 
   In action  104  one or more experts review all the images in a set and adjust the window level and window width of each image to give all the images in that particular set a consistent appearance. Action  104  may be performed at programmed imaging workstation having storage capabilities for storage of parameter settings to be used at a later point in time. Regardless of whether the parameters are acquired in real-time or at another point in time the user-defined window settings would be the desired window settings. More than one expert could be used to develop a cross spectrum of desired settings that can then be averaged or compared to develop observed settings. Notwithstanding the number of experts, the setting of the expert is considered to be the gold standard by which all techniques are judged against. 
   In action  108  values for W —W  and W —L  are generated by the system based on the manual and automatic display-attribute adjustments. These values are referred to as default window settings. In action  110  the derived optimal set of scaling factors are saved in memory to be used by a display terminal to change the display to the desired settings. 
     FIG. 2  is a flowchart of a method  200  performed by a client according to an embodiment. Method  200  meets the need in the art for selection of window width and window level with minimal user interaction. 
   Method  200  begins with an analysis method  202  where a set of images are segmented  204  and subjected to histogram characterization  206 . 
   In action  204 , the images are segmented. An algorithm is used to remove collimator edges &amp; raw radiation regions from the image. An image mask is a matrix (Image) that consist of zeros (0s) and ones (1s); the image mask when combined with an image allows only selected sections of the image to be included and the non-selected section of the image to appear as a dark pattern. In the present arrangement those sections of the image that correspond to the collimator edges &amp; raw radiation are removed by zeroing it out of the considered image. The edges are a subrange of data found in the image file that can be easily discarded by turning the individual pixels off. Once the image dataset has been segmented control passes to action  206  for further processing. 
   In action  206 , histogram characterization is performed on the segmented images of action  204 . The pixel values of the image can be a histogram to determine the range of values. The histogram displays the image data as a function of numerical pixel value. Peaks in the histogram represent values with large pixel counts or an area of uniform density. In a histogram a range of a variable is divided into class intervals (bins) for which the frequency of occurrence is represented by a rectangular column. The height of the column is proportional to the frequency of observations within the interval. Histogram characterization is the statistical measure for the pixel values of an image. In histogram characterization the interval is expressed as nth percentile values, selected from zero (0) to one hundred (100) that are incremented to percentile greater than nth by an arbitrary factor. For example, for the 60th percentile acceptable ranges would be 60-70, 60-80, 60-90, 60-100. Other possible ranges and combinations are possible. Then W_L is determined in terms of the local means of each of these ranges e.g., mean of the 10-70 range &amp; W_W is estimated in terms of the actual difference in the higher &amp; lower percentile values of that range for example in terms of the difference between 70th &amp; 10th percentile values. Once histogram characterization has been performed, control passes to action  208  for further processing. 
   In action  208 , the values for each of the segmented images are averaged. After the values have been averaged, control passes to action  214  for further processing. 
   In action  210 , one or more observer selects attribute values for a set of images. These attribute values are one or more window width (W —W ), window length (W —L ) all selected by one or more observer. When the observers are done with the selection then there will be settings for each anatomy and view, processing type, and imaging site. All the images in a particular dataset e.g., Chest PA all acquired from the same site (Hospital, Medical Facility, Department within a Hospital or Medical facility) are displayed simultaneously, including all the patient sizes, both the pediatrics &amp; adult cases. The observer adjusts the window width and window length on a display terminal to select attributes for each image in a set while viewing all the images in that particular set. These attributes adjustment should not be done to obtain the best look for individual images in the set nor for highlighting the relevant clinical details in each image; it is solely for a consistent look so that the brightness &amp; contrast distribution of the image while being displayed is substantially uniform. This uniformity should hold at least for the anatomically relevant region. For example, cspine images consistency can be achieved and determined in the relevant anatomical region. That is the remaining portion of the cspine image such as shoulder and skull the brightness &amp; contrast will largely vary from image to image. Thus, for each image the desired value of W —W  &amp; W —L  as selected by the observer is used as the default value. Control then passes to action  204  for further processing. 
   In action  212 , the observed settings in action  210  are averaged. The average describes the most typical of a full range of observed settings from the lowest to the highest. The average is inclusive of the arithmetic average describing the total of all values for each type of observed settings divided by the number of values. Those in the art also understand that average includes such statistical principles as mean, mode, and removal of outliers to describe the range of observed settings. Once the average has been determined control passes to action  214  for further processing. 
   In action  214 , a ratio is determined between the average observed settings  212  and the average computed settings  208 . The purpose if the correlation is to find the relationship between the observer settings (action  210 ) and the determined averaged parameters (action  208 ) for both the window width and window length. The ratio can be a statistical correlation such as linear regression, a relationship describing the best fit between the selected and the calculated, or it can be a ratio of the observed setting to the calculated setting. Once the ratio is determined control passes to action  216  for further processing. 
   In action  216 , a scaling factor is determined from the observed settings and the formulated settings. The scaling factor would be a unique value or a set of values that mathematically expresses the relationship of the desired settings and the formulated settings. As shown graphically in  FIG. 7 , display attribute values (Y Axis) and images in first set (X Axis), the scale factor pushes average attribute value from optimized analysis method up to the average attribute value from observer adjustment. Once the scale factor has been determined control is passed action  218  to save scaling factor  218  in a storage device. 
   Action  218  saves the scaling factor in a storage device. For immediate use the scaling factor is saved in RAM  406  and for permanent storage in mass storage  410  or any suitable storage that may be coupled to computer  402 . The saved scaling factor  218  shifts the determined W —L  and W —W curves  from their respective original position to the desired setting of the observer. See  FIG. 7 . When the optimized parameters are processed by display terminal  422  the brightness and contrast (W —L  and W —W ) will correspond to the desired settings of the observers. 
     FIG. 3  is a flowchart of a method  300  performed by a client according to an embodiment. Method  300  meets the need in the art for selection of window width and window level with minimal user interaction. Method  300  is applies the determined scaling factor to a second set of images having similar characteristics such as same imaging site (hospital), same anatomy and view, and same processing type. 
   Method  300  begins with action  302  of selection of the image data set based on the same imaging site, processing type, etcetera to form a second set of images. The second set of images  302  and the scaling optimal scaling factor  304  are used by optimized analysis method (method  200 )  306  to derive display attribute adjustments. The optimal display attributes  308  are processed at a display terminal (e.g., display  422 )  310  to select the window width and the window level with minimal user interaction. The contribution of the scale factor is shown in  FIG. 8  when the attribute values from optimized analysis method are mapped to modified attribute values. 
     FIG. 4  is a block diagram of the hardware and operating environment  400  in which different embodiments can be practiced. The description of  FIG. 4  provides an overview of computer hardware and a suitable computing environment in conjunction with which some embodiments can be implemented. Embodiments are described in terms of a computer executing computer-executable instructions. However, some embodiments can be implemented entirely in computer hardware in which the computer-executable instructions are implemented in read-only memory. Some embodiments can also be implemented in client/server computing environments where remote devices that perform tasks are linked through a communications network. Program modules can be located in both local and remote memory storage devices in a distributed computing environment. 
   Computer  402  includes a processor  404 , commercially available from Intel, Motorola, Cyrix and others. Computer  402  also includes random-access memory (RAM)  406 , read-only memory (ROM)  408 , and one or more mass storage devices  410 , and a system bus  412 , that operatively couples various system components to the processing unit  404 . The memory  406 ,  408 , and mass storage devices,  410 , are types of computer-accessible media. Mass storage devices  410  are more specifically types of nonvolatile computer-accessible media and can include one or more hard disk drives, floppy disk drives, optical disk drives, and tape cartridge drives. The processor  404  executes computer programs stored on the computer-accessible media. 
   Computer  402  can be communicatively connected to the Internet  414  via a communication device  416 . Internet  414  connectivity is well known within the art. In one embodiment, a communication device  416  is a modem that responds to communication drivers to connect to the Internet via what is known in the art as a “dial-up connection.” In another embodiment, a communication device  416  is an Ethernet® or similar hardware network card connected to a local-area network (LAN) that itself is connected to the Internet via what is known in the art as a “direct connection” (e.g., T1 line, etc.). 
   A user enters commands and information into the computer  402  through input devices such as a keyboard  418  or a pointing device  420 . The keyboard  418  permits entry of textual information into computer  402 , as known within the art, and embodiments are not limited to any particular type of keyboard. Pointing device  420  permits the control of the screen pointer provided by a graphical user interface (GUI) of operating systems such as versions of Microsoft Windows®. Embodiments are not limited to any particular pointing device  420 . Such pointing devices include mice, touch pads, trackballs, remote controls and point sticks. Other input devices (not shown) can include a microphone, joystick, game pad, satellite dish, scanner, or the like. 
   In some embodiments, computer  402  is operatively coupled to a display device  422 . Display device  422  is connected to the system bus  412 . Display device  422  permits the display of information, including computer, video and other information, for viewing by a user of the computer. Embodiments are not limited to any particular display device  422 . Such display devices include cathode ray tube (CRT) displays (monitors), as well as flat panel displays such as liquid crystal displays (LCD&#39;s). In addition to a monitor, computers typically include other peripheral input/output devices such as printers (not shown). Speakers  424  and  426  provide audio output of signals. Speakers  424  and  426  are also connected to the system bus  412 . 
   Computer  402  also includes an operating system (not shown) that is stored on the computer-accessible media RAM  406 , ROM  408 , and mass storage device  410 , and is and executed by the processor  404 . Examples of operating systems include Microsoft Windows®, Apple MacOS®, Linux®, UNIX®. Examples are not limited to any particular operating system, however, and the construction and use of such operating systems are well known within the art. 
   Embodiments of computer  402  are not limited to any type of computer  402 . In varying embodiments, computer  402  comprises a PC-compatible computer, a MacOS®-compatible computer, a Linux®-compatible computer, or a UNIX®-compatible computer. The construction and operation of such computers are well known within the art. 
   Computer  402  can be operated using at least one operating system to provide a graphical user interface (GUI) including a user-controllable pointer. Computer  402  can have at least one web browser application program executing within at least one operating system, to permit users of computer  402  to access intranet or Internet world-wide-web pages as addressed by Universal Resource Locator (URL) addresses. Examples of browser application programs include Netscape Navigator® and Microsoft Internet Explorer®. 
   The computer  402  can operate in a networked environment using logical connections to one or more remote computers, such as remote computer  428 . These logical connections are achieved by a communication device coupled to, or a part of, the computer  402 . Embodiments are not limited to a particular type of communications device. The remote computer  428  can be another computer, a server, a router, a network PC, a client, a peer device or other common network node. The logical connections depicted in  FIG. 4  include a local-area network (LAN)  430  and a wide-area network (WAN)  432 . Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet. 
   When used in a LAN-networking environment, the computer  402  and remote computer  428  are connected to the local network  430  through network interfaces or adapters  434 , which is one type of communications device  416 . Remote computer  428  also includes a network device  436 . When used in a conventional WAN-networking environment, the computer  402  and remote computer  428  communicate with a WAN  432  through modems (not shown). The modem, which can be internal or external, is connected to the system bus  412 . In a networked environment, program modules depicted relative to the computer  402 , or portions thereof, can be stored in the remote computer  428 . 
   Computer  402  also includes power supply  438 . Each power supply can be a battery. 
     FIG. 5  is a flowchart of a method  500  performed by a client according to an embodiment. Method  500  meets the need in the art for selection of window width and window level with minimal user interaction. 
   Method  500  begins in action  502  by downloading images from an image warehouse or digital repository. This collection is a dataset of images that are used to derive scaling factors for achieving image consistency on the desired display terminal. The source digital repository stores various images that are available for retrieval. This repository can be a digital library, a database, a website, or any other information repository. The digital repository may be an “off-the-shelf” software product such as an Oracle database software developed and sold by Oracle Corporation of Redwood City, Calif. 
   In action  504  clinical images from the image warehouse are categorized in accordance to imaging site, processing type, anatomy and view, or any other form of categorization of clinical images. Table I is an example of a categorization based on an embodiment having the category labels in the first row. 
   
     
       
         
             
           
             
               TABLE I 
             
           
          
             
                 
             
             
               Image Categorization 
             
          
         
         
             
             
             
             
             
             
             
          
             
                 
               Anatomy and 
               Tissue 
               Imaging 
                 
               Processing 
               Number 
             
             
               Image Set 
               View 
               Thickness 
               Site 
               Patient Age 
               Type 
               of Images 
             
             
                 
             
             
               1 
               Hand PA 
               Thin 
               Site A 
               Pediatric 
               Custom 4 
               14 
             
             
               2 
               Hand PA 
               Thin 
               Site A 
               Adult 
               Custom 4 
               25 
             
             
               3 
               C-spine 
               Thin 
               Site A 
               Adult 
               Custom 2 
               38 
             
             
                 
               Lateral 
             
             
               4 
               Chest PA 
               Medium 
               Site A 
               Pediatric 
               Custom 4 
               12 
             
             
               5 
               Chest PA 
               Medium 
               Site A 
               Adult 
               Custom 4 
               21 
             
             
               6 
               Chest PA 
               Medium 
               Site B 
               Adult 
               Custom 
               49 
             
             
                 
                 
                 
                 
                 
               site B 
             
             
               7 
               Chest Lateral 
               Thick 
               Site A 
               Pediatric 
               Custom 4 
               11 
             
             
               8 
               Chest Lateral 
               Thick 
               Site A 
               Adult 
               Custom 4 
               14 
             
             
               9 
               Chest Lateral 
               Thick 
               Site B 
               Adult 
               Custom 
               40 
             
             
                 
                 
                 
                 
                 
               site B 
             
             
                 
             
          
         
       
     
   
   After the images have been categorized, an analysis is performed to derive optimal set of scaling factors  508  based on a particular image analysis method  506 . 
   The derivation of optimal set of scaling factors begins with action  510 . In action  510  and image category is selected. Examples of an image category would be imaging site, anatomy and view, and processing type. After selecting the image category  510  an image set  518  is selected for analysis. The selected image category with the appropriate image set is subjected to segmentation analysis  520 . After the image set  518  has been segmented  520  an analysis method is selected to get display attribute settings  522  for the image set. 
   Image segmentation refers to the removal of unwanted portions of an image. Apart from the pixels corresponding to the body part being imaged, an x-ray image also has a considerable number of pixels belonging to the raw radiation and pixels corresponding to the collimator edges. If the window settings are determined by evaluating all the pixel values in image matrix including those corresponding to raw radiation and collimation edges. The resulting image brightness and contrast may not be satisfactory because the selected WW —L  and WW —W  will include these undesired image pixels. This is especially true for the images of body parts such as hand, which generally have a large area corresponding to raw radiation, and hence large number of pixels considered for analysis will be undesired. 
   In action  522 , a formulation of window level (WL) and window width (WW) by use of mathematical techniques such as histogramming. For example, in determining window level the mean of a selected range such 90 th -10 th  percentile. The difference between the selected range could be used to determine window width. The formulation of window level (WL) and window width (WW) are the default display attribute settings  522  while the observer window settings are the desired display attribute settings  514 . 
   In actions  512  and  514 , the display attribute settings are obtained from the observer reviewer. The observer is one or more consumer of the images that will provide settings for brightness and contrast for a set of images at a display terminal. The observer reviews all the images from a particular image set simultaneously and adjusts the window level and window width for the particular image set to achieve a consistent appearance across all the images in that image set and not for the best image appearance for any individual image. For example, using the expertise of two domain experts for this purpose both observers review the images and adjust the WW —L  and WW —W  independently producing two sets of consistent window settings for each image set. These display attribute settings  514  are referred to as the desired display attribute settings (gold standard) for that image set. 
   In action  516  scaling factors are calculated. The scaling factors are used to adjust the default display attribute settings (action  522 ) to match the desired window settings (action  514 ) for consistency. For each image set, brightness scaling factor (Badj) and contrast scaling factor (Cadj) is determined for WL and WW adjustments respectively. Thus, for each image set the scaling factors tuned the window settings of each set according to the overall preferred image appearance for that particular image set obtained by the observer. Mathematically the adjustments can be expressed as: 
   
     
       
         
           
             Badj 
             = 
             
               
                 mean 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 of 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 the 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 desired 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 WL 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 for 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 the 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 particular 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 image 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 set 
               
               
                 mean 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 of 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 the 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 formulated 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 WL 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 for 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 that 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 set 
               
             
           
           ; 
         
       
     
     
       
         
           Cadj 
           = 
           
             
               mean 
               ⁢ 
               
                   
               
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               the 
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               WW 
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               image 
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               WW 
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   In action  524  estimate display attribute for window width (WW) and window level (WL) are determined from the scaling factors  516  and the formulated, WL_f and WW_f, display attribute settings  522  for selected image category. Mathematical this can be expressed as: WL_est=WL_f*Badj; WW_est=WW_f*Cadj. 
   In action  526 , error metrics for window width (E_ww) and window level (E_wl) are calculated. E_wl provided a cumulative error over all the images in an image set by calculating the deviation between estimated and desired window level for each image in that set. The mathematical representation for this error is: 
             E_w   ⁢           ⁢   1     =       (         ∑     i   =   1     N     ⁢       (         Badj   *   WL_f   ⁢     (   i   )         WL_d   ⁢     (   i   )         -   1     )     2       N     )             
Where N is the total number of images in the particular set; WL_f is the formulated WL;
         WL_d is the corresponding WL value set by observer; and Badj is the brightness adjustment factor.       
   E_ww generated an aggregate mismatch error between estimated and desired window width over all the images in an image set. The mathematical representation for this error is: 
   
     
       
         
           E_ww 
           = 
           
             
               ( 
               
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     N 
                   
                   ⁢ 
                   
                     
                       ( 
                       
                         
                           
                             Cadj 
                             * 
                             WW_f 
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                               i 
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                             WW_d 
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   Where N is the total number of images in the particular set; WW_f is the formulated WW; WW_d is the corresponding value of WW set by observer.; and Cadj is the contrast adjustment factor. 
   In action  528 , actions  510 - 526  are repeated for other analysis methods and other image categories. However, at a minimum other analysis methods are different combinations of image histogram statistics used to predict new window settings or calculated window settings. These image histograms are obtained from the segmented images that have portions like raw radiations and collimation edges removed. 
     FIG. 6  is a flowchart of a method  600  performed by a client according to an embodiment. Method  600  is ranking and selecting best analysis method for the image dataset  610 . Method  600  meets the need in the art for selection of window width and window level with minimal user interaction. 
   Method  600  begins with action  602 . In action  602  the WL and WW estimations are ranked by using E_wl and E_ww for each dataset category as shown in Table I. Each WL and WW candidate analysis was evaluated over a dataset to select the best estimation analysis for that dataset. Each dataset consists of nine (9) image sets as shown in TABLE I. The ranking is carried out using E_wl for window length estimation analysis and E_ww for window width estimation analysis. For a selected dataset, each candidate analysis was ranked considering a single image set at a time such that the technique with the minimum error receives the highest ranking. For each image in the set the window settings were adjusted by four different methods such as same image with default window settings, with the two optimum estimation analysis methods and with the analysis method giving best results for that image set. The ten images are arranged in a single column based on the window settings. That is, there is a column with a default window setting, a column with giving the best result window setting, etcetera. The observer (two observers) reviews the images and rates the consistency along each column. Each column represents the consistency of an image set when window settings are adjusted with a specific analysis method. An example rating is a scale 0 to 4. Where zero (0) corresponding to highly inconsistent image appearance while four (4) is very consistent image appearance. Each observer evaluates and rates the results independently by comparing the consistency across the rows. Since each column represents a different estimation analysis method, when an observer a preference for an image in a given row it is selecting the best analysis method for that image set. In this way the selection of each observer becomes the best technique and the window settings (brightness and contrast) that should be used by the system to automate the selection of window width and window level with minimal user interaction 
   In action  604 , the best category technique is selected. This is accomplished by mean ranking for each technique over all the nine images sets producing nine rankings for each dataset. An array of five best estimation techniques, with the highest mean rankings, was selected for each dataset. 
   In action  606 , cumulative ranking is performed. While action  608  selects the best technique for dataset based on the cumulative ranking. Cumulative ranking is the globally optimum technique from the arrays of best estimation techniques corresponding to each dataset. The technique with the highest cumulative ranking was the globally optimum estimation technique. 
   In some embodiments, the methods are implemented as a computer data signal embodied in a carrier wave, that represents a sequence of instructions which, when executed by a processor, such as processor  404  in  FIG. 4 , cause the processor to perform the respective method. In other embodiments, the methods are implemented as a computer-accessible medium having executable instructions capable of directing a processor, such as processor  404  in  FIG. 4 , to perform the respective method. In varying embodiments, the medium is a magnetic medium, an electronic medium, or an optical medium. 
   Components for image processing, and methods for determining optimal scaling factor and optimal display attributes can be embodied as computer hardware circuitry or as a computer-readable program, or a combination of both. 
   More specifically, in the computer-readable program embodiment, the programs can be structured in an object-orientation using an object-oriented language such as Java, Smalltalk or C++, and the programs can be structured in a procedural-orientation using a procedural language such as COBOL or C. The software components communicate in any of a number of means that are well-known to those skilled in the art, such as application program interfaces (API) or interprocess communication techniques such as remote procedure call (RPC), common object request broker architecture (CORBA), Component Object Model (COM), Distributed Component Object Model (DCOM), Distributed System Object Model (DSOM) and Remote Method Invocation (RMI). The components execute on as few as one computer as in computer  306  in  FIG. 4 , or on at least as many computers as there are components. 
   The image warehouse is one or more collection of images, image repository, or a series of images that have been received from an imaging system. The images are then stored in a storage device for easy access by the image processor  402  and display terminal  422 . The image processor is an aptly programmed general-purpose computer that is capable of implementing the above methods. These methods are embodied in a memory device and use with computed scale factors  110  to shift the determined brightness and contrast of an image to the desired settings for the observer without the need of human interaction. The scale factor in addition to the adjustment data can have a unique code that identifies the observer, a department in a medical institution, or any other categorization that can be uniquely defined. The images are displayed at terminal  312  to be view by an observer after they have been received and processed in accordance to optimizing algorithm in image processor  402 . 
   Conclusion 
   A system and method has been described. Although specific embodiments have been illustrated and described herein, it will be appreciated by those of ordinary skill in the art that any arrangement which is calculated to achieve the same purpose may be substituted for the specific embodiments shown. This application is intended to cover any adaptations or variations. For example, although described in object-oriented terms, one of ordinary skill in the art will appreciate that implementations can be made in a procedural design environment or any other design environment that provides the required relationships. 
   In particular, one of skill in the art will readily appreciate that the names of the methods and apparatus are not intended to limit embodiments. Furthermore, additional methods and apparatus can be added to the components, functions can be rearranged among the components, and new components to correspond to future enhancements and physical devices used in embodiments can be introduced without departing from the scope of embodiments. One of skill in the art will readily recognize that embodiments are applicable to future communication devices, different file systems, and new data types.