Khalid Masood and Nasir Rajpoot, Hyperspectral Colon Biopsy Classification into Normal and Malignant Categories
Diagnosis and cure of colon cancer can be improved by efficiently classifying the colon tissue cells from biopsy slides into normal and malignant classes. This report presents the classification of hyperspectral colon tissue cells using morphology of gland nuclei of cells. The application of hyperspectral imaging techniques in medical image analysis is a new domain for researchers. The main advantage of using hyperspectral imaging is the increased spectral resolution and detailed subpixel information. The proposed classification algorithm is based on the subspace projection techniques. Support vector machine, with 3rd degree polynomial kernel, is employed in final set of experiments. Dimensionality reduction and tissue segmentation is achieved by Independent Component Analysis (ICA) and k-means clustering. Morphological features, which describe the shape, orientation and other geometrical attributes, are extracted in one set of experiments. Grey level co-occurrence matrices are also computed for the second set of experiments. For classification, linear discriminant analysis (LDA) with co-occurrence features gives comparable classification accuracy to SVM using a 3rd degree polynomial kernel. The algorithm is tested on a limited set of samples containing only ten biopsy slides and its applicability is demonstrated with the help of measures such as classification accuracy rate and the area under the convex hull of ROC curves.