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Abdel-Dayem A, El-Sakka M. Fuzzy entropy based detection of suspicious masses in digital mammogram images. Conf Proc IEEE Eng Med Biol Soc. 2005;2005:4017-22doi: 10.1109/IEMBS.2005.1615343.
Abdel-Dayem, A., & El-Sakka, M. (2005). Fuzzy entropy based detection of suspicious masses in digital mammogram images. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 20054017-22. https://doi.org/10.1109/IEMBS.2005.1615343
Abdel-Dayem, Amr, and El-Sakka, Mahmoud. "Fuzzy entropy based detection of suspicious masses in digital mammogram images." Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference vol. 2005 (2005): 4017-22. doi: https://doi.org/10.1109/IEMBS.2005.1615343
Abdel-Dayem A, El-Sakka M. Fuzzy entropy based detection of suspicious masses in digital mammogram images. Conf Proc IEEE Eng Med Biol Soc. 2005;2005:4017-22. doi: 10.1109/IEMBS.2005.1615343. PMID: 17281113.
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Conf Proc IEEE Eng Med Biol Soc. 2005;2005:4017-22. doi: 10.1109/IEMBS.2005.1615343.

Fuzzy entropy based detection of suspicious masses in digital mammogram images.

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

Amr Abdel-Dayem, Mahmoud El-Sakka

Affiliations

  1. Computer Science Department, The University of Western Ontario, London, Ontario, Canada. [email protected].

PMID: 17281113 DOI: 10.1109/IEMBS.2005.1615343

Abstract

Mammography is the standard method for screening and detecting breast abnormalities. In this paper, we propose a novel scheme for suspicious lesion detection in digital mammograms. The proposed scheme is based on image thresholding. The optimal threshold is determined by minimizing the fuzzy entropy of the image. Moreover, the paper introduces a new block-based performance criterion to compare between the computer generated and the radiologist segmented images. Experimental results over a set of sample images showed that the proposed scheme produces accurate segmentation results when compared with the manual results produced by radiologists. Hence the proposed scheme can be used as an effective tool in monitoring and detecting suspicious lesions on digital mammogram images.

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