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