Title :
A neural network based technique to locate and classify microcalcifications in digital mammograms
Author_Institution :
Sch. of Inf. Technol., Griffith Univ., Brisbane, Qld., Australia
Abstract :
This paper proposes a technique that extracts suspicious areas containing microcalcifications in digital mammograms and classifies them into two categories whether they contain benign or malignant clusters. The centroids and radiuses provided by expert radiologist are being used to locate and extract suspicious areas. Neural network´s generalisation abilities are used to classify them into benign or malignant. The technique has been implemented in C++ on the SP2 supercomputer. The database from the Department of Radiology at the University of Nijmegen and Lawrence Livermore National Laboratory has been used for the experiments. The preliminary results are very promising. Some of them are presented in this paper
Keywords :
diagnostic radiography; generalisation (artificial intelligence); image classification; medical image processing; neural nets; C++; SP2 supercomputer; benign clusters; breast screening; digital mammograms; generalisation; malignant clusters; microcalcification classification; microcalcification location; neural network based technique; radiology; Australia; Breast cancer; Cancer detection; Image databases; Information technology; Intelligent networks; Laboratories; Mammography; Neural networks; Radiology;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.687128