Title :
A modified Neyman-Pearson technique for radiodense tissue estimation in digitized mammograms
Author :
Neyhart, J.T. ; Eckert, R.E. ; Polikar, R. ; Mandayam, S. ; Tseng, M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Rowan Univ., Glassboro, NJ, USA
Abstract :
The percentage of radiodense tissue in the breast has been shown to be a reliable marker for breast cancer risk. In this paper, we present an image processing technique for estimating radiodense tissue in digitized mammograms. First, the mammogram is segmented into tissue and nontissue regions. This segmentation process involves the generation of a segmentation mask that is developed using a radial basis function neural network. Subsequently, the image is processed for estimating the amount of radiodense tissue. The estimation process involves the generation of a modified Neyman-Pearson threshold to segment the radiodense and radiolucent tissue. Typical research results are presented-these have been independently validated by a radiologist.
Keywords :
biological tissues; cancer; image segmentation; mammography; medical image processing; radial basis function networks; breast cancer detection; digitized mammograms; mammogram segmentation; medical diagnostic imaging; modified Neyman-Pearson technique; nontissue regions; radial basis function neural network; radiodense tissue estimation; radiolucent tissue; tissue regions; Breast cancer; Breast tissue; Image processing; Image segmentation; Neural networks; Prediction algorithms; Radial basis function networks; Random variables; Reliability engineering; X-ray imaging;
Conference_Titel :
Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint
Print_ISBN :
0-7803-7612-9
DOI :
10.1109/IEMBS.2002.1106243
