Analysis of mammographic microcalcifications using gray-level image structure features

Journal Article · Sat Jun 01 04:00:00 EDT 1996 · IEEE Transactions on Medical Imaging

DOI:https://doi.org/10.1109/42.500063· OSTI ID:260427

Dhawan, A P; Chitre, Y; Kaiser-Bonasso, C; Moskowitz, M ^[1]

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1. Univ. of Cincinnati, OH (United States)

Most of the techniques used in the computerized analysis of mammographic microcalcifications use shape features on the segmented regions of microcalcifications extracted from the digitized mammograms. Since mammographic images usually suffer from poorly defined microcalcification features, the extraction of shape features based on a segmentation process may not accurately represent microcalcifications. In this paper, the authors define a set of image structure features for classification of malignancy. Two categories of correlated gray-level image structure features are defined for classification of difficult-to-diagnose cases. The first category of features includes second-order histogram statistics-based features representing the global texture and the wavelet decomposition-based features representing the local texture of the microcalcification area of interest. The second category of features represents the first-order gray-level histogram-based statistics of the segmented microcalcification regions and the size, number, and distance features of the segmented microcalcification cluster. Various features in each category were correlated with the biopsy examination results of 191 difficult-to-diagnose cases for selection of the best set of features representing the complete gray-level image structure information. The selection of the best features was performed using the multivariate cluster analysis as well as a genetic algorithm (GA)-based search method. The selected features were used for classification using backpropagation neural network and parametric statistical classifiers. Receiver operating characteristic (ROC) analysis was performed to compare the neural network-based classification with linear and k-nearest neighbor (KNN) classifiers.

• OSTI ID: 260427
• Journal Information: IEEE Transactions on Medical Imaging, Journal Name: IEEE Transactions on Medical Imaging Journal Issue: 3 Vol. 15; ISSN 0278-0062; ISSN ITMID4
• Country of Publication: United States
• Language: English

Related Subjects

55 BIOLOGY AND MEDICINE
BASIC STUDIES
ACCURACY
BIOMEDICAL RADIOGRAPHY
DIAGNOSIS
IMAGE PROCESSING
MAMMARY GLANDS
MULTIVARIATE ANALYSIS
NEOPLASMS
X RADIATION