Microcalcification Detection by Morphology, Singularities of Contourlet Transform and Neural Network
Journal: Bonfring International Journal of Networking Technologies and Applications (Vol.01, No. 1)Publication Date: 2012-09-30
Authors : Rekha Lakshmanan; Vinu Thomas;
Page : 14-19
Keywords : Breast Cancer; Back Propagation Neural Network; Contourlet Transform; Morphology;
Abstract
The proposed method presents a new classification approach to microcalcification detection in mammograms using morphology, Contourlet Transform and Artificial Neural Network. Early detection of breast cancer is possible by enhancing microcalcification features obtained using morphology and singularities of Contourlet Transform. The significant edge information indicating the relevant features in various decomposition levels are preserved while removing the artifacts. These features are utilized to detect microcalcifications by classification employing the Back Propagation Neural Network. Target to background contrast ratio, Contrast and Peak Signal to Noise ratio are considered for performance evaluation of the enhancement algorithm. The accuracy of the classification algorithm is 95%. The mini-MIAS mammographic database is employed for testing the accuracy of the proposed method and the results are promising.
Other Latest Articles
- A Novel Artificial Intelligent System for Milk Conservation Using Wireless Sensor Networks
- Cognitive Radio: A New Standard in Wireless Communication Technology
- Detection of Hearing Disorders Using Bragged Tree Algorithm
- OPTICAL TEXTURES IN LIQUID CRYSTAL MIXTURES
- A MODEL TO STUDY THE EFFECT OF BOILING ON WATER BORNE BACTERIAL
Last modified: 2013-09-24 20:56:32