Linear Kernel Based Support Vector Research Articles

Hybrid segmentation, characterization and classification of basal cell nuclei from histopathological images of normal oral mucosa and oral submucous fibrosis

This work presents a quantitative microscopic approach for discriminating oral submucous fibrosis (OSF) from normal oral mucosa (NOM) in respect to morphological and textural properties of the basal cell nuclei. Practically, basal cells constitute the proliferative compartment (called basal layer) of the epithelium. In the context of histopathological evaluation, the morphometry and texture of basal nuclei are assumed to vary during malignant transformation according to onco-pathologists. In order to automate the pathological understanding, the basal layer is initially extracted from histopathological images of NOM ( n = 341) and OSF ( n = 429) samples using fuzzy divergence, morphological operations and parabola fitting followed by median filter-based noise reduction. Next, the nuclei are segmented from the layer using color deconvolution, marker-controlled watershed transform and gradient vector flow (GVF) active contour method. Eighteen morphological, 4 gray-level co-occurrence matrix (GLCM) based texture features and 1 intensity feature are quantized from five types of basal nuclei characteristics. Afterwards, unsupervised feature selection method is used to evaluate significant features and hence 18 are obtained as most discriminative out of 23. Finally, supervised and unsupervised classifiers are trained and tested with 18 features for the classification between normal and OSF samples. Experimental results are obtained and compared. It is observed that linear kernel based support vector machine (SVM) leads to 99.66% accuracy in comparison with Bayesian classifier (96.56%) and Gaussian mixture model (90.37%).

Analysis of SPECT brain images for the diagnosis of Alzheimer's disease using moments and support vector machines

This paper presents a computer-aided diagnosis technique for improving the accuracy of diagnosing the Alzheimer's type dementia. The proposed methodology is based on the calculation of the skewness for each m-by- m-by- m sliding block of the SPECT brain images. The center pixel in this m-by- m-by- m block is replaced by the skewness value to build a new 3-D brain image which is used for classification purposes. After that, voxels which present a Welch's t-statistic between classes, Normal and Alzheimer's images, higher (or lower) than a threshold are selected. The mean, standard deviation, skewness and kurtosis are calculated for these selected voxels and they are subjected as features to linear kernel based support vector machine classifier. The proposed methodology reaches accuracy higher than 99% in the classification task.