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Segmentation of tumor in digital mammograms using wavelet transform modulus maxima on a low cost parallel computing system

Sulaiman, Hanifah and Ibrahim, Arsmah and Alias, Norma (2011) Segmentation of tumor in digital mammograms using wavelet transform modulus maxima on a low cost parallel computing system. In: 5th Kuala Lumpur International Conference on Biomedical Engineering, BIOMED 2011, 20-23 June 2011, Conjunction with the 8th Asian Pacific Conference on Medical and Biological Engineering, APCMBE 2011;Kuala Lumpur.

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Official URL: https://link.springer.com/chapter/10.1007/978-3-64...

Abstract

Parallel Computing System (PCS) is currently used widely in many applications of complex problems involving high computations. This is because it has the capability to process computations efficiently using a parallel scheme. ARS cluster is a low-cost PCS developed to implement processing of full-field digital mammograms. In this system eight processors are used to communicate via the Ethernet network using LINUX which is Fedora 7 as the operating system and Matlab Distributed Computing Server (MDCS) as a platform to process the digital mammograms. In this paper the Wavelet Transforms Modulus Maxima (WTMM) method is used to detect the edge of tumor in digital mammogram implemented on the ARS cluster. The study involved 80 digitized mammographic images obtained from the Malaysian National Cancer Center (NCC). The performance of the PCS in detecting the edge of tumors in digital mammograms using WTMM on the ARS cluster is reported. The experimental results showed that the speedup of the PCS improves when the number of processors is increased. © 2011 Springer-Verlag.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Breast Tumor, Digital Mammograms, Edge Detection, Segmentation Matlab Distributed Computing Server, Parallel Processing Wavelet Transform Modulus Maxima
Subjects:Q Science > QA Mathematics
Divisions:Science
ID Code:26332
Deposited By: Dr Norma Alias
Deposited On:10 Jul 2012 08:42
Last Modified:17 Jul 2017 06:46

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