A Computational Investigation of Breast Tumour on Mammogram Based on Pattern of Grey Scale Distribution

Abstract:

Breast cancer is the utmost female tumor and the primary cause of deaths among female. Computer-Aided Detection (CAD) systems are widely used as a tool to detect and classify the abnormalities found in the mammographic images. A detection of breast tumor in a mammogram has been a challenge due to the different intensity distribution which leads to the misdiagnosis of breast cancer. This research proposes a dectection system that is capable to detect the presence of mass tumor from a mammogram image. A total of 160 mammogram images are acquired from Mammographic Image Analysis Society (MIAS) databse, which are 80 normal and 80 abnormal images. The mammogram images are rescaled to 300 x 300 resolution. The noise in the mammogram is suppressed by using a Wiener filter. The images are enhanced by using Power Law (Gamma) Transformation, ɣ = 2 for a better image quality. The greyscale information that contain tumor mass is extracted and used to model the proposed detection system by using 80% or 128 and of the total 160 mammogram images. The rest 20% or 32 mammogram images are used to test the performance of the proposed detection system. The experimental results show that performance of the proposed detection system has 90.93% accuracy.

• Periodical: Journal of Biomimetics, Biomaterials and Biomedical Engineering (Volume 43)
• Pages: 67-73
• DOI: https://doi.org/10.4028/www.scientific.net/JBBBE.43.67
• Online since: November 2019
• Authors: M.K. Lim, Wan Khairunizam*, Wan Azani Mustafa
• Keywords: Breast, Detection, Mammography, Tumour

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