Title :
Automatic detection of abnormal tissue in mammography
Author :
Christoyianni, I. ; Dermatas, E. ; Kokkinakis, G.
Author_Institution :
Dept. of Electr. & Comput. Eng., Patras Univ., Greece
Abstract :
A novel method for accurate detection of regions of interest (ROIs) that contain circumscribed lesions in mammograms is presented. The mammograms are segmented using a statistical threshold and a number of candidate regions are extracted. Then a set of qualification criteria is employed to filter these regions retaining the most suspicious for which a radial-basis function neural network makes the final decision marking them as ROIs that contain abnormal tissue. The proposed method detects the exact location of the circumscribed lesions with an accuracy of 90.9%, and a very low number of false positive regions per image (2.1 ROIs per image) in the MIAS database
Keywords :
biological tissues; cancer; feature extraction; image classification; image segmentation; mammography; medical image processing; radial basis function networks; MIAS database; X-ray mammography; automatic abnormal tissue detection; breast cancer; circumscribed lesions; image region extraction; mammographic image segmentation; neural network classifier; qualification criteria; radial-basis function neural network; regions of interest detection; segmented image post-processing; statistical threshold; Breast cancer; Diseases; Filters; Image databases; Image segmentation; Lesions; Mammography; Neural networks; Qualifications; X-ray imaging;
Conference_Titel :
Image Processing, 2001. Proceedings. 2001 International Conference on
Conference_Location :
Thessaloniki
Print_ISBN :
0-7803-6725-1
DOI :
10.1109/ICIP.2001.958634
