Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/11826
Title: A Comparative Study of Fuzzy Thresholding Techniques for Mass Detection in Digital Mammography
Contributor(s): Alharbi, Hajar Mohammedsaleh H (author); Kwan, Paul H  (author); Sajeev, Abudulkadir  (author)
Publication Date: 2012
Handle Link: https://hdl.handle.net/1959.11/11826
Abstract: 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.
Publication Type: Conference Publication
Conference Name: IVCNZ 2012: 27th International Conference on Image and Vision Computing New Zealand, Dunedin, New Zealand, 26th - 28th November, 2012
Conference Details: IVCNZ 2012: 27th International Conference on Image and Vision Computing New Zealand, Dunedin, New Zealand, 26th - 28th November, 2012
Source of Publication: IVCNZ '12: Proceedings of the 27th International Conference on Image and Vision Computing New Zealand, p. 330-334
Publisher: ACM: Association for Computing Machinery
Place of Publication: online
Field of Research (FOR): 080108 Neural, Evolutionary and Fuzzy Computation
111202 Cancer Diagnosis
080106 Image Processing
Socio-Economic Outcome Codes: 890201 Application Software Packages (excl. Computer Games)
920102 Cancer and Related Disorders
970108 Expanding Knowledge in the Information and Computing Sciences
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Other Links: http://www.cs.otago.ac.nz/ivcnz2012/
Series Name: International Conference Proceedings Series (ICPS)
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Appears in Collections:Conference Publication
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