Title :
Texture-Based Feature Extraction for the Microcalcification from Digital Mammogram Images
Author :
AbuBaker, Ayman ; Qahwaji, Rami ; Ipson, Stan
Abstract :
This paper describes our ongoing efforts to provide efficient and accurate classification of microcalcification clusters in mammogram images. In this paper, a study of the characteristics of true microcalcifications compared to falsely detected microcalcifications is carried out using first and second order statistical texture analysis techniques. These features are generated in order to reduce the false positive (FP) ratio for the mammogram images. The statistical method presented here can successfully reduce the ratio of false positives (FP) by 18% without affecting the ratio of true positives (TP) which is currently at 98%.
Keywords :
feature extraction; image classification; image texture; mammography; medical image processing; statistical analysis; digital mammogram images; first order statistical texture analysis technique; microcalcification cluster classification; second order statistical texture analysis technique; texture-based feature extraction; Biomedical imaging; Brightness; Feature extraction; Histograms; Image analysis; Image segmentation; Image texture analysis; Neural networks; Shape measurement; Statistical analysis; False Positive; Feature Extraction; Statistical texture analysis; mammogram images;
Conference_Titel :
Signal Processing and Communications, 2007. ICSPC 2007. IEEE International Conference on
Conference_Location :
Dubai
Print_ISBN :
978-1-4244-1235-8
Electronic_ISBN :
978-1-4244-1236-5
DOI :
10.1109/ICSPC.2007.4728464
