Effective statistical texture features for segmenting mammogram images based on M-ARKFCM with multi-ROI segmentation method
by Ramayanam Suresh; A. Nagaraja Rao; B. Eswara Reddy
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 28, No. 3/4, 2024

Abstract: Mammogram segmentation using multi-region of interest is one of the most emerging research areas in the field of medical image analysis. In recent decades, mammogram segmentation is well-developed, but still the feature extraction algorithms are facing problems like poor outcome in severe lighting variations, illuminance, etc. To address these difficulties, an effective methodology is proposed in this research paper. Initially, the mammogram images were collected from mammographic image analysis society (MIAS) dataset, which was enhanced by using Laplacian filtering. Then, the pre-processed mammogram images were used for segmentation by applying modified adaptively regularised kernel based fuzzy C means (M-ARKFCM). After segmentation, feature extraction was performed by using statistical features for distinguishing the patterns of cancer and non-cancer regions in mammogram images. The experimental outcome shows that the M-ARKFCM improves the segmentation performance by means of area under curve (AUC), dice coefficient and Jaccard coefficient.

Online publication date: Wed, 24-Jul-2024

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Advanced Intelligence Paradigms (IJAIP):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com