International Journal of Soft Computing
ISSN: OnlineISSN: Print 1816-9503
Abstract
This study presents an algorithm which aims to assist the radiologist towards fast detection and early diagnosis of breast cancer. It combines several image processing techniques like region splitting and region filling with the Discrete Wavelet Transform (DWT), artificial intelligence techniques and artificial neural networks for detection of masses in mammograms. The AI techniques include fractal dimension analysis, dogs and rabbits algorithm and others. The fractal dimension analysis is used to find the roughness value, which is used to locate the region suspicious for cancer in the mammogram. The dogs-and-rabbits algorithm initiates the clustering. Region splitting and filling are used to segment the suspicious region. The Back Propagation neural network is applied at the end to determine whether a given mammogram is suspicious for cancer. The algorithm is verified by using mammograms from Mammographic Image Analysis Society database.
How to cite this article:
Y. Ireaneus Anna Rejani and S. Thamarai Selvi . Digital Mammogram Segmentation and Tumour Detection Using Artificial Neural Networks.
DOI: https://doi.org/10.36478/ijscomp.2008.112.119
URL: https://www.makhillpublications.co/view-article/1816-9503/ijscomp.2008.112.119