Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrated spatial and feature image query
Multimedia Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Bioinformatics
IEEE Transactions on Information Technology in Biomedicine
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Lymphoma is a cancer affecting lymph nodes. A reliable and precise classification of malignant lymphoma is essential for successful treatment. Current methods for classifying the malignancies rely on a variety of morphological, clinical and molecular variables. In spite of recent progress, there are still uncertainties in diagnosis. Automatic classification of images taken from slides with hematoxylin and eosin stained biopsy samples can allow more consistent and less labor-consuming diagnosis of this disease. In this paper, three well-known texture feature extraction methods including local binary patterns (LBP), Gabor filtering and Gray Level Coocurrence Matrix (GLCM) have been applied to efficiently represent the three types of malignancies, namely, Chronic Lymphotic Leukemia(CLL), Follicular Lymphoma (FL) cells, and Mantle Cell Lymphoma (MCL). Three classifiers of k-Nearest Neighbor, multiple-layer perceptron and Support Vector Machine have been experimented and the simple classifier ensemble scheme majority-voting demonstrated obvious improvement in the classification performance.