Khalid Masood

Texture based classification of hyperspectral colon biopsy samples using CLBP

Computer aided diagnosis (CAD) is aimed at supporting the pathologists in their diagnosis. In this paper, we present an algorithm for texture-based classification of colon tissue patterns. In this method, a single band is selected from its hyperspectral cube and spatial analysis is performed using circular local binary pattern (CLBP) features. A novel method for feature selection is presented resulting in the best feature set without actually running the classifier. Classification results using Gaussian kernel SVM, with an accuracy of 90%, demonstrate that texture analysis based on CLBP features is able to distinguish the benign and malignant patterns.

Hyperspectral Colon Tissue Classification using Morphological Analysis

Diagnosis and cure of colon cancer can be improved by efficiently classifying the colon tissue cells into normal and malignant classes. This paper presents the classification of hyperspectral colon tissue cells using morphological analysis of gland nuclei cells. The application of hyperspectral imaging technique in medical image analysis is a new domain for researchers. The main advantage in using hyperspectral imaging is the increased spectral resolution and detailed subpixel information. Biopsy slides with several microdots, where each microdot is from a distinct patient, are illuminated with a tuned light source and magnification is performed up to 400times. The proposed classification algorithm combines the hyperspectral imaging technique with linear discriminant analysis. Dimensionality reduction and cellular segmentation is achieved by independent component analysis (ICA) and k-means clustering. Morphological features, which describe the shape, orientation and other geometrical attributes, are next to be extracted. For classification, LDA is employed to discriminate tissue cells into normal and malignant classes. Implementation of LDA is simpler than other approaches; it saves the computational cost, while maintaining the performance. The algorithm is tested on a number of samples 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

Hyperspectral imaging with wavelet transform for classification of colon tissue biopsy samples

Automatic classification of medical images is a part of our computerised medical imaging programme to support the pathologists in their diagnosis. Hyperspectral data has found its applications in medical imagery. Its usage is increasing significantly in biopsy analysis of medical images. In this paper, we present a histopathological analysis for the classification of colon biopsy samples into benign and malignant classes. The proposed study is based on comparison between 3D spectral/spatial analysis and 2D spatial analysis. Wavelet textural features in the wavelet domain are used in both these approaches for classification of colon biopsy samples. Experimental results indicate that the incorporation of wavelet textural features using a support vector machine, in 2D spatial analysis, achieve best classification accuracy.