DocumentCode :
2215468
Title :
Mammogram image segmentation using fuzzy clustering
Author :
Boss, R. Subash Chandra ; Thangavel, K. ; Daniel, D. Arul Pon
Author_Institution :
Dept. of Comput. Sci., Periyar Univ., Salem, India
fYear :
2012
fDate :
21-23 March 2012
Firstpage :
290
Lastpage :
295
Abstract :
This paper proposes mammogram image segmentation using Fuzzy C-Means (FCM) clustering algorithm. The median filter is used for pre-processing of image. It is normally used to reduce noise in an image. The 14 Haralick features are extracted from mammogram image using Gray Level Co-occurrence Matrix (GLCM) for different angles. The features are clustered by K-Means and FCM algorithms inorder to segment the region of interests for further classification. The performance of segmentation result of the proposed algorithm is measured according to the error values such as Mean Square Error (MSE) and Root Means Square Error (RMSE). The Mammogram images used in our experiment are obtained from MIAS database.
Keywords :
cancer; feature extraction; fuzzy set theory; image classification; mammography; matrix algebra; mean square error methods; median filters; medical image processing; pattern clustering; FCM; GLCM; Haralick feature extraction; MIAS database; RMSE; classification; fuzzy c-means clustering algorithm; gray level co-occurrence matrix; image preprocessing; k-means clustering; mammogram image segmentation; median filter; root means square error; Arrays; Classification algorithms; Clustering algorithms; Digital filters; Error analysis; Feature extraction; Image segmentation; Data mining; Feature Extraction; Fuzzy C-Means Clustering; Image Processing; K-Means clustering and Image Segmentation; Mammogram;
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.2012.6208360
Filename :
6208360
Link To Document :
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