Edges Detection of Clusters of Microcalcifications with SOM and Coordinate Logic Filters
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Detection of Microcalcifications Using Coordinate Logic Filters and Artificial Neural Networks
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Identification of tiny and large calcification in breast: a study on mammographic image analysis
International Journal of Bioinformatics Research and Applications
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Breast cancer is one of the leading causes of death for women. Small clusters of microcalcifications appearing as collection of white spots on mammograms show an early warning of breast cancer. In present paper a novel approach of segmentation implemented on X-ray mammograms for more accurate detection of microcalcification clusters has been introduced. The method is based on discrete wavelet transform due to its multiresolution properties. Morphological tophat algorithm is applied for contrast enhancement of the calcification clusters. Finally fuzzy c-means clustering (FCM) algorithm has been implemented for intensity-based segmentation. The proposed technique is compared with conventional global thresholding method and experimental results show the good properties of the proposed technique.