BROWSE
Detail
Conference Paper
SVM-Based Harris Corner Detection for Breast Mammogram Image Normal/Abnormal Classification
Cited 7 time in 
Share 
Share
- Authors
- Hyun I. Kim, Sung Shin, Wei Wang, Soon I. Jeon
- Issue Date
- 2013-10
- Citation
- Research in Adaptive and Convergent Systems (RACS) 2013, pp.187-191
- Language
- English
- Type
- Conference Paper
- Abstract
- The breast mammogram image is one of the most important materials of the Computer-Aided Diagnosis (CAD) system to support diagnosis of breast cancer. In the CAD system, intensity value is a widely used feature for medical image processing. In this paper, we propose develop improved Harris Corner Detection with improved input training data set for Support Vector Machine (SVM) to classify a breast mammogram image as normal or abnormal. In the proposed approach, corner pixels from improved Harris Corner Detection are used as a training input feature for SVM. The results demonstrate that the proposed approach can significantly improve both the accuracy and the performance of computational speed to classify the breast mammogram image as normal or abnormal, when compared with the data set from traditional methods. © 2013 ACM.
- KSP Keywords
- Breast Cancer, CAD system, Computational Speed, Data sets, Harris Corner Detection, Image processing(IP), Mammogram Images, Medical Image Processing, Support VectorMachine(SVM), Support diagnosis, Traditional methods