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Proceedings Paper

Mammographic mass classification based on possibility theory
Author(s): Marwa Hmida; Kamel Hamrouni; Basel Solaiman; Sana Boussetta
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Paper Abstract

Shape and margin features are very important for differentiating between benign and malignant masses in mammographic images. In fact, benign masses are usually round and oval and have smooth contours. However, malignant tumors have generally irregular shape and appear lobulated or speculated in margins. This knowledge suffers from imprecision and ambiguity. Therefore, this paper deals with the problem of mass classification by using shape and margin features while taking into account the uncertainty linked to the degree of truth of the available information and the imprecision related to its content. Thus, in this work, we proposed a novel mass classification approach which provides a possibility based representation of the extracted shape features and builds a possibility knowledge basis in order to evaluate the possibility degree of malignancy and benignity for each mass. For experimentation, the MIAS database was used and the classification results show the great performance of our approach in spite of using simple features.

Paper Details

Date Published: 17 March 2017
PDF: 5 pages
Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103411X (17 March 2017); doi: 10.1117/12.2268700
Show Author Affiliations
Marwa Hmida, Univ. de Tunis (Tunisia)
Univ. Européenne de Bretagne, Institut Telecom, Telecom Bretagne (France)
Kamel Hamrouni, Univ. de Tunis (Tunisia)
Basel Solaiman, Univ. Européenne de Bretagne, Institut Telecom, Telecom Bretagne (France)
Sana Boussetta, Hopital regional Ben Arous (Tunisia)

Published in SPIE Proceedings Vol. 10341:
Ninth International Conference on Machine Vision (ICMV 2016)
Antanas Verikas; Petia Radeva; Dmitry P. Nikolaev; Wei Zhang; Jianhong Zhou, Editor(s)

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