Abstract:
A method for directed machine learning includes receiving features including intensity data and location data of an image, condensing the intensity data and the location data into a feature vector, processing the feature vector by a plurality of classifiers, each classifier trained for a respective trained class among a plurality of classes, outputting, from each classifier, a probability of the feature vector belong to the respective trained class, and assigning the feature vector a label according to the probabilities of the classifiers, wherein the assignment produces a segmentation of the image.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of Provisional Application No. 60/983,587 filed on Oct. 30, 2007 in the United States Patent and Trademark Office, the content of which is herein incorporated by reference in its entirety. 
    
    
     BACKGROUND 
     1. Technical Field 
     The present disclosure is directed machine learning, and more particularly to machine learning for tissue labeling segmentation. 
     2. Discussion of Related Art 
     Statistical based segmentation approaches for multiple organs and tissues within medical images make use of relative organ locations and intensities to define and refine the segmentation. These approaches use the creation of several tissue models based on different images features such as location and intensity levels. 
     An exemplary approach for automated labeling of tissues within abdominal CT scan data uses three different models to obtain label probabilities: intensity models, spatial models, and neighbor probability models. The probability models are chosen and the probability output crafted to adequately account for the probabilities from the three models. The segmentation occurs by initializing the image with the intensity probabilities and then applying iterative conditional modes (ICM) or simulated annealing methods to refine the initialization into the final segmentation. Other improved approaches may also be used, such as belief propagation. FIGS.  1 A-B illustrate the creation of the probabilities from the intensity ( FIG. 1A ) and spatial ( FIG. 1B ) models for n different labels using this method. 
     Referring more particularly to  FIGS. 1A-B , the two models (intensity and spatial) must be manually created for each label. Given the intensity value ( 101 ), it is given to the models ( 102  to  104 ) and probabilities are created ( 105  to  107 ). Similarly, given the spatial value ( 108 ), it is given to the models ( 109  to  111 ) and probabilities are created ( 112  to  114 ) with the given location. The highest intensity and spatial probabilities are taken as factors in determining the assigned label. Further manual design is necessary to determine the proper way to combine the probabilities to obtain the best segmentation. The proposed methods allow for a more scientific and automated approach to model creation and the combination of probabilities. 
     These approaches can be used in labeling of MR brain images with maximum likelihood estimation. A statistical approach may be used using an assumed Gibbs distribution. In another technique, spatially-variant mixture model estimates are used for pixel labeling of clinical MR brain images, wherein densities are modeled by univariate Gaussian functions. 
     In the above examples, models are created and combined for the observed distributions. Although the model chosen is based upon knowledge of the problem and an idea of the general distribution, no quantitative evidence is given as to suggest why a particular model is optimal for the problem. Possible over-fitting and requirements for a large among of training data appear in a histogram modeled distribution. For a parametric model such a Gaussian function the distribution may not be properly modeled under any situation. Testing other distribution or weightings for existing models can be a tedious procedure. 
     Therefore, a need exists for a machine learning approach, viewing the input locations, intensities, etc. as features and the distributions as classifier outputs, a more methodological approach can be taken to develop and evaluate an improved distribution model for given training datasets. 
     BRIEF SUMMARY 
     According to an embodiment of the present disclosure, a method for directed machine learning includes receiving features including intensity data and location data of an image, condensing the intensity data and the location data into a feature vector, processing the feature vector by a plurality of classifiers, each classifier trained for a respective trained class among a plurality of classes, outputting, from each classifier, a probability of the feature vector belong to the respective trained class, and assigning the feature vector a label according to the probabilities of the classifiers, wherein the assignment produces a segmentation of the image. 
     According to an embodiment of the present disclosure, a method for directed machine learning includes receiving features including intensity data and location data of an image, determining intensity probabilities and spatial probabilities for the intensity data and the location data, respectively, condensing the intensity probabilities and the spatial probabilities into a feature vector, processing the feature vector by a plurality of classifiers, each classifier trained for a respective trained class among a plurality of classes, outputting, from each classifier, a probability of the feature vector belong to the respective trained class, and assigning the feature vector a label according to the probabilities of the classifiers, wherein the assignment produces a segmentation of the image. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Preferred embodiments of the present disclosure will be described below in more detail, with reference to the accompanying drawings: 
         FIG. 1A  illustrates the creation of the probabilities from an intensity model for n different labels; 
         FIG. 1B  illustrates the creation of the probabilities from a spatial model for n different labels; 
         FIG. 2  is a flow diagram of a method for machine learning taking the intensity and location data as features according to an embodiment of the present disclosure; 
         FIG. 3  is a flow diagram of a method for machine learning taking the intensity and location based probabilities as features according to an embodiment of the present disclosure; 
         FIGS. 4A-G  are spatial and intensity initializations for tissue segmentation according to an embodiment of the present disclosure; and 
         FIG. 5  is a diagram of an exemplary computer-system for supporting directed machine learning according to an embodiment of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
     According to an embodiment of the present disclosure, a machine learning approach creates and combines spatial and intensity models for image segmentation. More specifically, the intensity and spatial features of sets of images are taken as features for a machine learning problem. According to an embodiment of the present disclosure, such a treatment of features enables an image segmentation problem to be taken from an image processing domain to a machine learning domain. 
     The machine learning may take any of a variety of methods that are quantitatively evaluated to demonstrate a model. Herein, examples using a Support Vector Machines (SVM) are compared to an elliptical model. Other approaches such as Gaussian mixture models and Bayesian Networks are also possible. A machine learning approach offers a methodological approach to creating and developing models. The incorporation and combination of features to develop new models is also described. 
     Referring to  FIG. 2 , intensity and location data are viewed as features for a machine learning method. By using these features, the problem of weighting different probabilities becomes an optimization problem in the machine learning field. Additionally the models are created automatically through the training data. In this example, intensity data (block  201 ), location data (block  202 ), and additional features (block  203 ), for example, the distance from the patient&#39;s skin, are condensed into a feature vector (block  204 ). The feature vector (block  204 ) is passed to multiple trained SVMs (blocks  205  to  207 ). Each SVM is a two-class classifier trained on the classes label versus all other labels. Each SVM accepts a feature vector (e.g., series of numerical inputs) and produces a single numerical output. The output of each SVM is used directly as a probability (blocks  208  to  210 ). Although SVMs do not directly output a probability, they can be configured to do so. The feature vector is assigned a label according to the probabilities of the classifiers (block  211 ), wherein the assignment produces a segmentation of the image. The assignment can be based on, for example, a highest probability for the feature vector among the outputs of the SVMs. 
     Through the use of intensity and location data as features, the creation of the SVM models may be automated and a scientific approach may be taken in the combination of the probabilities. In the example of  FIG. 2 , the SVM model processes all features so the combination is already included in the model specification. Additionally, the model can be compared against other classifiers via sensitivity and specificity evaluations. 
     The effectiveness of an exemplary machine learning approach using multiple SVMs for determining tissue label probabilities in abdominal CT scans is demonstrated as against an approach using selected parametric or non-parametric models for the spatial and intensity probabilities without quantitative evidence of the optimality.  FIG. 1  shows a flowchart of this general model. 
     According to an embodiment of the present disclosure, qualitatively evaluated classifiers are used as probability models. Although classifiers typically output binary decisions, their output may be viewed as a probability distribution. As an example of one possible model, the intensity, location, and additional features are combined into a single feature vector as shown in  FIG. 2 . Multiple binary SVMs are used with radial basis functions trained to identify the vector as either tissue or not tissue. In the exemplary approach of machine learning, the output of the SVM is used as the probability vector. Although SVMs do not directly report probability, it is adequate for the example to compare the values directly. Since the intensity, location, or any additional features are part of the feature vector, the only adaptive design concern is in a weighting of each feature in the SVM training. 
     Multiple probability models, as in  FIG. 3 , can be introduced by constructing a feature vector (block  301 ) of intensity and/or location information from the outputs (block  3 )  302 ). This arrangement uses a second series of SVMs for any remaining features. In this example, the advantage of a methodological model design still holds. The decision of combining the probability values is made within the SVM. Since these values are again viewed as features, a machine learning approach can be used. 
     The use of SVMs to develop models is shown in  FIGS. 4A-G . In  FIG. 4A , the labeled output an elliptical spatial models is shown. In this case, parametric ellipsoidal function was used to model the spatial locations of organs. Different models may be selected for different organs. Instead of designing the model on a per organ basis on intuition of the features and problem, according to an embodiment of the present disclosure, a quantitative methodology is use to select a best model (e.g., according to probabilities). Toolkits for analyzing features in machine learning may be used for these purposes. As shown in  FIGS. 4B and 4C , using the given ground truth data, improved spatial distributions are possible in this case. 
     In  FIGS. 4D to 4F , an additional spatial feature of the distance from the skin was readily added. The training of the SVMs simply incorporated this additional feature. In contrast to the elliptical model, this additional feature would needed a model re-design. For example, a 4D ellipsoidal function to account for the fourth spatial feature is necessary. Another possibility would involve two spatial models per label whose outputs must be properly combined. These issues quantitatively addressed in proposed method. The results of the use of intensity feature combines with the spatial features as outlined in  FIG. 2  is shown in  FIG. 4G . The impact of the intensity into the model is significant over the previous figures. 
     Again,  FIGS. 4A-G  demonstrate the initialization of segmentation and not the final result. The SVM models can also be used with ICM or belief propagation to achieve the final results. A sufficient number of ground truth or training datasets are needed to ensure a valid design (e.g., 95% confidence—one of ordinary skill in the art would recognize that a verity of thresholds and a measures may be used to measure or determine a valid design) and substantially avoid over-fitting the data. This is expected since the problem relies less on intuition from an image processing stand point and more on optimizing a classifier for a given set of features. 
     According to an embodiment of the present disclosure, models are established to predict probability values for tissue labels in statistical segmentation of organs. The use of machine learning methods substantially eliminates the need to manually created ad hoc probability models for different aspects of the system. It also substantially eliminates the need to balance different probability models to ensure a valid result. 
     In  FIGS. 4A-G , an exemplary method according to the present is disclosure is demonstrated using a multiple SVM approach to establish a spatial initialization of the segmentation against a parametric elliptical model. The addition of features allows for customization of the spatial regions. After initialization, these models can be used with ICM (Iterated Conditional Modes) or belief propagation to produce the results. 
     The limitations of this method are the same as those within any machine classification approach. A larger amount of training data would be more beneficial. Given a sufficiently large number of training datasets available, a method according to an embodiment of the present disclosure offers a scientific and predictable pathway to the development of the models needed for statistical segmentation methods. 
     Referring to  FIGS. 4A-G , spatial and intensity initializations are shown for tissue segmentation in a three-dimensional (3D) abdomen computed tomography (CT) image. In each case, different segmentation shading or colors can represent different tissue regions. The lungs and inner air portions of the colon are pre-determined and therefore hold fixed shading/color labels; first and second colors ( 401  and  402 ). For example, a third color ( 403 ) represents fat and light ( 404 ) represents the liver. In  FIGS. 4A ,  4 B,  4 D and  4 G, only the maximal probability tissue label is shown.  FIGS. 4C ,  4 E and  4 F blend tissue labels based on the probability values of each label are shown.  FIG. 4A  shows the initialization result of the manually created spatial distribution models. The process involves manually evaluating the segmented ground truth images and determining a model. The models in the remaining images are automatically created from the ground truth images using the SVM classifiers.  FIGS. 4B and 4C  are the spatial initialization models based on the 3D location within the dataset.  FIGS. 4D and 4F  shows the same initialization, but with the distance from the skin added as an additional spatial feature. In  FIG. 4F , the original data is shown.  FIG. 4G  shows the initialization with added intensity features. The use of the SVM classifier allows for more appropriate shapes to be used without additional complexity in the design process. 
     It is to be understood that embodiments of the present disclosure may be implemented in various forms of hardware, software, firmware, special purpose processors, or a combination thereof. In one embodiment, a software application program is tangibly embodied on a program storage device. The application program may be uploaded to, and executed by, a machine comprising any suitable architecture. 
     Referring now to  FIG. 5 , according to an embodiment of the present disclosure, a computer system (block  501 ) for directed machine learning includes, inter alia, a central processing unit (CPU) (block  502 ), a memory (block  503 ) and an input/output (I/O) interface (block  504 ). The computer system (block  501 ) is generally coupled through the I/O interface (block  504 ) to a display (block  505 ) and various input devices (block  506 ) such as a mouse and keyboard. The support circuits can include circuits such as cache, power supplies, clock circuits, and a communications bus. The memory (block  503 ) can include random access memory (RAM), read only memory (ROM), disk drive, tape drive, etc., or a combination thereof. The present invention can be implemented as a routine (block  507 ) that is stored in memory (block  503 ) and executed by the CPU (block  502 ) to process the signal from the signal source (block  508 ). As such, the computer system (block  501 ) is a general purpose computer system that becomes a specific purpose computer system when executing the routine (block  507 ) of the present disclosure. 
     The computer platform (block  501 ) also includes an operating system and micro instruction code. The various processes and functions described herein may either be part of the micro instruction code or part of the application program (or a combination thereof) which is executed via the operating system. In addition, various other peripheral devices may be connected to the computer platform such as an additional data storage device and a printing device. 
     It is to be further understood that, because some of the constituent system components and method steps depicted in the accompanying figures may be implemented in software, the actual connections between the system components (or the process steps) may differ depending upon the manner in which the system is programmed. Given the teachings of the present disclosure provided herein, one of ordinary skill in the related art will be able to contemplate these and similar implementations or configurations of the present disclosure. 
     Having described embodiments for directed machine learning, it is noted that modifications and variations can be made by persons skilled in the art in light of the above teachings. It is therefore to be understood that changes may be made in embodiments of the present disclosure that are within the scope and spirit thereof.