COMPARISON OF SPARSE CODING AND KERNEL METHODS FOR HISTOPATHOLOGICAL CLASSIFICATION OF GLIOBASTOMA MULTIFORME

Abstract

This paper compares performance of redundant representation and sparse coding against classical kernel methods for classifying histological sections. Sparse coding has been proven to be an effective technique for restoration, and has recently been extended to classification. The main issue with classification of histology sections is inherent heterogeneity as a result of technical and biological variations. Technical variations originate from sample preparation, fixation, and staining from multiple laboratories, where biological variations originate from tissue content. Image patches are represented with invariant features at local and global scales, where local refers to responses measured with Laplacian of Gaussians, and global refers to measurements in the color space. Experiments are designed to learn dictionaries, through sparse coding, and to train classifiers through kernel methods with normal, necorotic, apoptotic, and tumor with with characteristics of high cellularity. Two different kernel methods of support vector machine (SVM) and kernel discriminant analysis (KDA) are used for comparative analysis. Preliminary investigation on histological samples of Glioblastoma multiforme (GBM) indicates that kernel methods perform as good if not better than sparse coding with redundant representation.

Fig. 1

Four classes of patch-level histology from Glioblastoma multiforme (GBM), which is the one of the most aggressive type of primary brain tumor: first row - apoptotic regions, second row - necrotic regions, third row - high cellularity regions, and fourth row - normal regions. Note that there is a significant amount of heterogeneity in the signature of these samples, which originate from variations in sample preparation among multiple clinics.