Khairulnizam Othman, Afandi Ahmad, "Quantification and Segmentation of Breast Cancer Diagnosis: Efficient Hardware Accelerator Approach," WSEAS Transactions on Computers, vol. 12, pp. -, 2013, DOI:
Khairulnizam Othman, Afandi Ahmad. Quantification and Segmentation of Breast Cancer Diagnosis: Efficient Hardware Accelerator Approach.
WSEAS Transactions on Computers. 2013;12:-.
WSEAS Transactions on Computers
Print ISSN: 1109-2750, E-ISSN: 2224-2872
Volume 12, 2013
Quantification and Segmentation of Breast Cancer Diagnosis: Efficient Hardware Accelerator Approach
Authors: ,
Abstract: This research presents VLSI architecture for image segmentation. The architecture is based on the fuzzy c-means algorithm with spatial constraint for reducing the misclassification rate. In the architecture, the usual iterative operations for updating the homogeneus membership matrix and cluster centroid are merged into one single updating process to evade the large storage requirement. In addition, an efficient pipelined circuit is used for the updating process for accelerating the computational speed. Experimental results show that the proposed circuit is an effective alternative for real-time image segmentation with low area cost (time) and low misclassification rate.
Pages: - WSEAS Transactions on Computers, ISSN / E-ISSN: 1109-2750 / 2224-2872, Volume 12, 2013DOI: *As the DOI is a unique identifier, it is already available in the pdf version.
**The DOI link will be activated in the first midst of January 2026.
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