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Proceedings Paper

Comparative analysis of codeword representation by clustering methods for the classification of histological tissue types
Author(s): Ahmet Saygili; Gunalp Uysal; Gokhan Bilgin
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Paper Abstract

In this study, the classification of several histological tissue types, i.e., muscles, nerves, connective and epithelial tissue cells, is studied in high resolutional histological images. In the feature extraction step, bag of features method is utilized to reveal distinguishing features of each tissue cell types. Local small blocks of sub-images/patches are extracted to find discriminative patterns for followed strategy. For detecting points of interest in local patches, Harris corner detection method is applied. Afterwards, discriminative features are extracted using the scale invariant feature transform method using these points of interests. Several code word representations are obtained by clustering approach (using k-means fuzzy c-means, expectation maximization method, Gaussian mixture models) and evaluated in comparative manner. In the last step, the classification of the tissue cells data are performed using k-nearest neighbor and support vector machines methods.

Paper Details

Date Published: 8 December 2015
PDF: 6 pages
Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 98750U (8 December 2015); doi: 10.1117/12.2228526
Show Author Affiliations
Ahmet Saygili, Namik Kemal Univ. (Turkey)
Gunalp Uysal, Yildiz Technical Univ. (Turkey)
Gokhan Bilgin, Yildiz Technical Univ. (Turkey)

Published in SPIE Proceedings Vol. 9875:
Eighth International Conference on Machine Vision (ICMV 2015)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev, Editor(s)

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