Share Email Print

Proceedings Paper

Classification of mammographic microcalcifications using wavelets
Author(s): Yateen S. Chitre; Atam P. Dhawan; Myron Moskowitz; Alok Sarwal; Christine Bonasso; Suresh B. Narayan
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Breast cancer is the leading cause of death among women. Breast cancer can be detected earlier by mammography than any other non-invasive examination. About 30% to 50% of breast cancers demonstrate tiny granulelike deposits of calcium called microcalcifications. It is difficult to distinguish between benign and malignant cases based on an examination of calcification regions, especially in hard-to-diagnose cases. We investigate the potential of using energy and entropy features computed from wavelet packets for their correlation with malignancy. Two types of Daubechies discrete filters were used as prototype wavelets. The energy and entropy features were computed for 128 benign and 63 malignant cases and analyzed using a multivariate cluster analysis and a univariate statistical analysis to reduce the feature set to a `five best set of features.' The efficacy of the reduced feature set to discriminate between the malignant and benign categories was evaluated using different multilayer perceptron architectures. The multilayer perceptron was trained using the backpropagation algorithm for various training and test set sizes. For each case 40 partitions of the data set were used to set up the training and test sets. The performance of the features was evaluated by computing the best area under the relative operating characteristic (ROC) curve and the average area under the ROC curve. The performance of the features computed from the wavelet packets was compared to a second set of features consisting of the wavelet packet features, image structure features and cluster features. The classification results are encouraging and indicate the potential of using features derived from wavelet packets in discriminating microcalcification regions into benign and malignant categories.

Paper Details

Date Published: 12 May 1995
PDF: 8 pages
Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995); doi: 10.1117/12.208678
Show Author Affiliations
Yateen S. Chitre, Univ. of Cincinnati (United States)
Atam P. Dhawan, Univ. of Cincinnati (United States)
Myron Moskowitz, Univ. of Cincinnati (United States)
Alok Sarwal, Univ. of Cincinnati (United States)
Christine Bonasso, Univ. of Cincinnati (United States)
Suresh B. Narayan, Univ. of Cincinnati (United States)

Published in SPIE Proceedings Vol. 2434:
Medical Imaging 1995: Image Processing
Murray H. Loew, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?