NDLI: A wavelet based morphological mass detection and classification in mammograms

Content Provider	IEEE Xplore Digital Library
Author	Anitha, J. Peter, J.D.
Copyright Year	2012
Description	Author affiliation: Department of IT, Karunya University, Coimbatore, India (Peter, J.D.) \|\| Department of CSE, Karunya University, Coimbatore, India (Anitha, J.)
Abstract	This paper presents an efficient mass detection and classification in mammogram images with the use of features extracted from the mass regions obtained by the automatic morphological based segmentation method. In this approach, the mammogram images are preprocessed to extract the breast profile and improve the contrast. The segmentation is done with combination of various morphological operations. In this approach, the wavelet features are extracted from the detected mass regions and is compared with feature extracted using Gray Level Co-occurrence Matrix (GLCM) to differentiate the TP and FP regions. Classifications of the mass regions are carried out through the Support Vector Machine (SVM) to separate the segmented regions into masses and non-masses based on the features. The methodology achieves 95% of accuracy.
Starting Page	25
Ending Page	28
File Size	1094478
Page Count	4
File Format	PDF
ISBN	9781467323192
e-ISBN	9781467323222
DOI	10.1109/MVIP.2012.6428752
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-14
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Image segmentation Accuracy mass wavelet Feature extraction mathematical morphology Breast cancer Classification algorithms SVM GLCM
Content Type	Text
Resource Type	Article

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