Author(s): J.Subhash Chandra bose, Marcus Karnan, and R.Sivakumar
Journal: International Journal of Computer and Network Security
ISSN 2076-2739
Volume: 2;
Issue: 2;
Start page: 78;
Date: 2010;
Original page
Keywords: Breast boarder | nipple identification | Genetic Algorithm (GA) | Particle Swarm Optimization (PSO) | Asymmetry | Texture Segmentation | Receiver Operating Characteristics (ROC).
ABSTRACT
Mammography is at present the best available technique for early detection of breast cancer. The most common breast abnormalities that may indicate breast cancer are masses and calcifications. The challenge is to quickly and accurately overcome the development of breast cancer which affects more and more women through the world. Microcalcifications appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. Mammogram is one of the best technologies currently being used for diagnosing breast cancer. Breast cancer is diagnosed at advanced stages with the help of the mammogram image. In this paper an intelligent system is designed to diagnose breast cancer through mammograms, using image processing techniques along with intelligent optimization tools such as GA and PSO. The suspicious region is extracted or segmented using two different approaches such as asymmetry approach and Markov Random Field (MRF) hybrid with Particle Swarm Optimization (PSO) algorithm. 161 pairs of digitized mammograms obtained from the Mammography Image Analysis Society (MIAS) database are used to design the proposed diagnosing system.
Journal: International Journal of Computer and Network Security
ISSN 2076-2739
Volume: 2;
Issue: 2;
Start page: 78;
Date: 2010;
Original page
Keywords: Breast boarder | nipple identification | Genetic Algorithm (GA) | Particle Swarm Optimization (PSO) | Asymmetry | Texture Segmentation | Receiver Operating Characteristics (ROC).
ABSTRACT
Mammography is at present the best available technique for early detection of breast cancer. The most common breast abnormalities that may indicate breast cancer are masses and calcifications. The challenge is to quickly and accurately overcome the development of breast cancer which affects more and more women through the world. Microcalcifications appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. Mammogram is one of the best technologies currently being used for diagnosing breast cancer. Breast cancer is diagnosed at advanced stages with the help of the mammogram image. In this paper an intelligent system is designed to diagnose breast cancer through mammograms, using image processing techniques along with intelligent optimization tools such as GA and PSO. The suspicious region is extracted or segmented using two different approaches such as asymmetry approach and Markov Random Field (MRF) hybrid with Particle Swarm Optimization (PSO) algorithm. 161 pairs of digitized mammograms obtained from the Mammography Image Analysis Society (MIAS) database are used to design the proposed diagnosing system.
