Tri-texture feature extraction and region growing-level set segmentation in breast cancer diagnosis
by S.L. Aarthy; S. Prabu
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 26, No. 3/4, 2018

Abstract: Computer Aided Diagnosis (CAD) systems utilises the computer technology to detect and classify the normal and abnormal levels in breast cancer. This paper employs the series of feature extraction and the novel segmentation methods to improve the performance of cancer detection in breast region. Tri-texture feature extraction method such as grey level co-occurrence matrix (GLCM), Gabor and wavelet texture features are extracted from the segmented output. This paper employs the hybrid Genetic Algorithm (GA)-Particle Swarm Optimisation (PSO) for relevant features for classification. Besides, the proposed work employs the two classifiers such as Support Vector Machine (SVM) (to classify normal and abnormal level) and Neural Network (NN) (to label the architectural distortion, asymmetry, masses and micro calcification). The hybrid Region Growing and Level (RGL) set methods provides the segmented output to analyse the abnormal categories. The utilisation of multiple methods improves the abnormality analysis of breast cancer diagnosis applications.

Online publication date: Thu, 15-Feb-2018

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 Biomedical Engineering and Technology (IJBET):
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