Title :
Mammogram image segmentation using granular computing based on rough entropy
Author :
Roselin, R. ; Thangavel, K.
Author_Institution :
Comput. Sci., Sri Sarada Coll. for Women, Salem, India
Abstract :
The mammography is the most effective procedure for to diagnosis the breast cancer at an early stage. A granule is a mass of objects, in the universe of discourse, put together by indistinguishability, similarity, proximity, or functionality. In mammograms, it is quite difficult to identify the suspicious region which is a mass of calcification on the breast tissue. This paper proposes rough entropy based granular computing to segment mammogram images. The proposed method is evaluated by classification algorithms which are available in WEKA.
Keywords :
biological tissues; cancer; entropy; granular computing; image classification; image segmentation; learning (artificial intelligence); mammography; medical image processing; rough set theory; WEKA; breast cancer diagnosis; breast tissue; calcification; classification algorithm; granular computing; mammogram image segmentation; rough entropy; suspicious region identify; Accuracy; Approximation methods; Classification algorithms; Entropy; Feature extraction; Image segmentation; Pattern recognition; Haralick Features; Mammogram; Pulse Coupled Neural Network; Rough entropy;
Conference_Titel :
Pattern Recognition, Informatics and Medical Engineering (PRIME), 2012 International Conference on
Conference_Location :
Salem, Tamilnadu
Print_ISBN :
978-1-4673-1037-6
DOI :
10.1109/ICPRIME.2012.6208365
