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A Comparative Study of Fuzzy Thresholding Techniques for Mass Detection in Digital Mammography |
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| Author(s) |
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Alharbi, Hajar Mohammedsaleh H |
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| Editor |
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Editor(s): Brendan McCane, Steven Mills, Jeremiah D Deng |
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| Publisher |
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Association for Computing Machinery (ACM) |
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New York, United States of America |
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International Conference Proceedings Series (ICPS) |
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| Abstract |
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Segmenting suspicious regions in mammographic images that may contain tumours from the background parenchyma of the breast is a highly challenging task. This is made difficult by factors including the complicated structure of breast tissues, unclear boundaries between normal tissues and tumours, and the low contrast between masses and surrounding regions in the images. In recent years, many researchers have discovered that fuzzy-logic based techniques have a number of advantages over conventional crisp approaches in segmenting masses in mammographic images. To this end, we compare five representative fuzzy thresholding techniques for this task in this paper using the recall and precision metrics. Experimental results revealed that fuzzy similarity thresholding achieves higher segmentation accuracy over a test set of 54 mammographic images selected from the mini-MIAS database. |
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| Citation |
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IVCNZ '12: Proceedings of the 27th International Conference on Image and Vision Computing New Zealand, p. 330-334 |
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