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

fYear

2012

fDate

21-23 March 2012

Firstpage

318

Lastpage

323

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, Informatics and Medical Engineering (PRIME), 2012 International Conference on

Conference_Location

Salem, Tamilnadu

Print_ISBN

978-1-4673-1037-6

Type

conf

DOI

10.1109/ICPRIME.2012.6208365

Filename

6208365