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Proceedings Article

Mammographic mass classification based on possibility theory

[+] Author Affiliations
Marwa Hmida, Kamel Hamrouni

Univ. de Tunis (Tunisia)

Basel Solaiman

Univ. Européenne de Bretagne, Institut Telecom, Telecom Bretagne (France)

Sana Boussetta

Hopital regional Ben Arous (Tunisia)

Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103411X (March 17, 2017); doi:10.1117/12.2268700
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From Conference Volume 10341

  • Ninth International Conference on Machine Vision (ICMV 2016)
  • Antanas Verikas; Petia Radeva; Dmitry P. Nikolaev; Wei Zhang; Jianhong Zhou
  • Nice, France | November 18, 2016

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. © (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Topics

Cancer ; Databases
Citation

Marwa Hmida ; Kamel Hamrouni ; Basel Solaiman and Sana Boussetta
" Mammographic mass classification based on possibility theory ", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 103411X (March 17, 2017); doi:10.1117/12.2268700; http://dx.doi.org/10.1117/12.2268700


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