Histopathological Image Analysis: A Review <s> 1) A Support Vector Machines (SVM): <s> The classification of normal and malginant colon tissue cells is crucial to the diagnosis of colon cancer in humans. Given the right set of feature vectors, Support Vector Machines (SVMs) have been shown to perform reasonably well for the classification [4,13]. In this paper, we address the following question: how does the choice of a kernel function and its parameters affect the SVM classification performance in such a system? We show that the Gaussian kernel function combined with an optimal choice of parameters can produce high classification accuracy. <s> BIB001 </s> Histopathological Image Analysis: A Review <s> 1) A Support Vector Machines (SVM): <s> Summary: We report a novel, proof-of-concept, computational method that models a type of brain cancer (glioma) only by using the topological properties of its cells in the tissue image. From low-magnification (80×) tissue images of 384 × 384 pixels, we construct the graphs of the cells based on the locations of the cells within the images. We generate such cell graphs of 1000--3000 cells (nodes) with 2000--10 000 links, each of which is calculated as a decaying exponential function of the Euclidean distance between every pair of cells in accordance with the Waxman model. At the cellular level, we compute the graph metrics of the cell graphs, including the degree, clustering coefficient, eccentricity and closeness for each cell. Working with a total of 285 tissue samples surgically removed from 12 different patients, we demonstrate that the self-organizing clusters of cancerous cells exhibit distinctive graph metrics that distinguish them from the healthy cells and the unhealthy inflamed cells at the cellular level with an accuracy of at least 85%. At the tissue level, we accomplish correct tissue classifications of cancerous, healthy and non-neoplastic inflamed tissue samples with an accuracy of 100% by requiring correct classification for the majority of the cells within the tissue sample. <s> BIB002 </s> Histopathological Image Analysis: A Review <s> 1) A Support Vector Machines (SVM): <s> In this paper we present a computer-aided diagno- sis (CAD) system to automatically detect prostatic adenocarci- noma from high resolution digital histopathological slides. This is especially desirable considering the large number of tissue slides that are currently analyzed manually - a laborious and time-consuming task. Our methodology is novel in that texture- based classification is performed using a hierarchical classifier within a multi-scale framework. Pyramidal decomposition is used to reduce an image into its constituent scales. The cascaded image analysis across multiple scales is similar to the manner in which pathologists analyze histopathology. Nearly 600 different image texture features at multiple orientations are extracted at every pixel at each image scale. At each image scale the classifier only analyzes those image pixels that have been determined to be tumor at the preceding lower scale. Results of quantitative evaluation on 20 patient studies indicate (1) an overall accuracy of over 90% and (2) an approximate 8-fold savings in terms of computational time. Both the AdaBoost and Decision Tree classifiers were considered and in both cases tumor detection sensitivity was found to be relatively constant across different scales. Detection specificity was however found to increase at higher scales reflecting the availability of additional discrimi- natory information. Index Terms—Hierarchical classifier, decision trees, Ad- aBoost, prostate cancer, digitized histology. <s> BIB003 </s> Histopathological Image Analysis: A Review <s> 1) A Support Vector Machines (SVM): <s> The current method of grading prostate cancer on histology uses the Gleason system, which describes five increasingly malignant stages of cancer according to qualitative analysis of tissue architecture. The Gleason grading system has been shown to suffer from inter- and intra-observer variability. In this paper we present a new method for automated and quantitative grading of prostate biopsy specimens. A total of 102 graph-based, morphological, and textural features are extracted from each tissue patch in order to quantify the arrangement of nuclei and glandular structures within digitized images of histological prostate tissue specimens. A support vector machine (SVM) is used to classify the digitized histology slides into one of four different tissue classes: benign epithelium, benign stroma, Gleason grade 3 adenocarcinoma, and Gleason grade 4 adenocarcinoma. The SVM classifier was able to distinguish between all four types of tissue patterns, achieving an accuracy of 92.8% when distinguishing between Gleason grade 3 and stroma, 92.4% between epithelium and stroma, and 76.9% between Gleason grades 3 and 4. Both textural and graph-based features were found to be important in discriminating between different tissue classes. This work suggests that the current Gleason grading scheme can be improved by utilizing quantitative image analysis to aid pathologists in producing an accurate and reproducible diagnosis <s> BIB004 </s> Histopathological Image Analysis: A Review <s> 1) A Support Vector Machines (SVM): <s> The inherent complexity and non-homogeneity of texture makes classification in medical image analysis a challenging task. In this paper, we propose a combined approach for meningioma subtype classification using subband texture (macro) features and micro-texture features. These are captured using the Adaptive Wavelet Packet Transform (ADWPT) and Local Binary Patterns (LBPs), respectively. These two different textural features are combined together and used for classification. The effect of various dimensionality reduction techniques on classification performance is also investigated. We show that high classification accuracies can be achieved using ADWPT. Although LBP features do not provide higher overall classification accuracies than ADWPT, it manages to provide higher accuracy for a meningioma subtype that is difficult to classify otherwise. <s> BIB005
SVMs project a set of training data representing two different classes into a highdimensional space by means of a kernel function . The algorithm then generates a discriminating hyper-plane to separate out the classes in such a way to maximize a cost function. Testing data is then projected into the high-dimensional space via , and the test data is classified based on where it falls with respect to the hyper-plane. The kernel function defines the method in which data is projected into the high-dimensional space. A commonly used kernel known as the radial basis function has been employed to distinguish between three different prostate tissue classes BIB004 . Radial basis functions with a grid search for their parameters have also been used to differentiate colon adenocarcinoma histopathology images from benign histopathology images BIB001 and to classify four different subtypes of meningioma BIB005 . 2) Adaboost: The AdaBoost is an adaptive algorithm in the sense it combines a number of weak classifiers to generate a strong classifier. Image pixels determined as diseased by a pathologist during the training stage are used to generate probability density functions (pdfs) for each of the individual texture features , for which are considered as weak classifiers BIB003 . Bayes Theorem is then used to generate likelihood scenes for each which constitute the weak learners. These are combined by the AdaBoost algorithm into a strong classifier where for every pixel , is the combined likelihood that pixel belongs to cancer class , is the weight determined during training for feature , and is the number of iterations. In BIB003 a hierarchical boosted cascade scheme for detecting suspicious areas on digitized prostate histopathology, inspired by the work of Viola and Jones  was presented. Efficient and accurate analysis is performed by first only detecting those areas that are found to be suspicious at lower scales. Analysis at subsequent higher magnifications is limited to those regions deemed to be suspicious at lower scales. Pixels classified as "nontumor" at a lower magnification (scale) are discarded at the subsequent higher scale, reducing the number of pixels needed for analysis at higher scales. The process is repeated using an increasingly larger number of image features and an increasing classification threshold at each iteration. Qualitative results with a hierarchical Boosted classifier at three different image resolutions are shown in Fig. 14 . Table III shows the average accuracy and corresponding standard deviation over 20 runs of randomized cross validation based classification of the diseased-healthy-damaged studies by using five different approaches: 1) the cell graph approach; 2) the cell-distribution approach in which features are extracted from the spatial distribution of cells without considering their pair-wise spatial relations (i.e., without considering the edge information); 3) the textural approach in which features are derived from the gray-level interdependency of pixels in a tissue image; 4) the intensity-based approach in which features are derived from the intensity values of pixels in a tissue image; and 5) the Voronoi diagram-based approach in which features are extracted by making use of the spatial interrelationships of adjacent cells  , BIB002 .