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Proceedings Paper

Combinational feature optimization for classification of lung tissue images
Author(s): Ravi K. Samala; Tatyana Zhukov; Jianying Zhang; Melvyn Tockman; Wei Qian
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Paper Abstract

A novel approach to feature optimization for classification of lung carcinoma using tissue images is presented. The methodology uses a combination of three characteristics of computational features: F-measure, which is a representation of each feature towards classification, inter-correlation between features and pathology based information. The metadata provided from pathological parameters is used for mapping between computational features and biological information. Multiple regression analysis maps each category of features based on how pathology information is correlated with the size and location of cancer. Relatively the computational features represented the tumor size better than the location of the cancer. Based on the three criteria associated with the features, three sets of feature subsets with individual validation are evaluated to select the optimum feature subset. Based on the results from the three stages, the knowledgebase produces the best subset of features. An improvement of 5.5% was observed for normal Vs all abnormal cases with Az value of 0.731 and 74/114 correctly classified. The best Az value of 0.804 with 66/84 correct classification and improvement of 21.6% was observed for normal Vs adenocarcinoma.

Paper Details

Date Published: 9 March 2010
PDF: 8 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76240Z (9 March 2010); doi: 10.1117/12.844509
Show Author Affiliations
Ravi K. Samala, The Univ. of Texas at El Paso (United States)
Tatyana Zhukov, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Jianying Zhang, The Univ. of Texas at El Paso (United States)
Melvyn Tockman, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Wei Qian, The Univ. of Texas at El Paso (United States)


Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

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