Analysis of temporal change of mammographic features for computer-aided characterization of malignant and benign masses

Lubomir M. Hadjiiski; Berkman Sahiner; Heang-Ping Chan; Nicholas Petrick; Mark A. Helvie M.D.; Metin Nafi Gurcan

doi:10.1117/12.431142

3 July 2001 Analysis of temporal change of mammographic features for computer-aided characterization of malignant and benign masses

Lubomir M. Hadjiiski, Berkman Sahiner, Heang-Ping Chan, Nicholas Petrick, Mark A. Helvie M.D., Metin Nafi Gurcan

Author Affiliations +

Proceedings Volume 4322, Medical Imaging 2001: Image Processing; (2001) https://doi.org/10.1117/12.431142
Event: Medical Imaging 2001, 2001, San Diego, CA, United States

Abstract

A new classification scheme was developed to classify mammographic masses as malignant and benign by using interval change information. The masses on both the current and the prior mammograms were automatically segmented using an active contour method. From each mass, 20 run length statistics (RLS) texture features, 3 spiculation features, and mass size were extracted. Additionally, 20 difference RLS features were obtained by subtracting the prior RLS features from the corresponding current RLS features. The feature space consisted of the current RLS features, the difference RLS features, the current and prior spiculation features, and the current and prior mass sizes. Stepwise feature selection and linear discriminant analysis classification (LDA) were used to select and merge the most useful features. A leave-one-case-out resampling scheme was applied to train and test the classifier using 140 temporal image pairs (85 malignant, 55 benign) obtained from 57 biopsy-proven masses (33 malignant, 24 benign) in 56 patients. An average of 10 features were selected from the 56 training subsets: 4 difference RLS features, 4 RLS features and 1 spiculation feature from the current image, and 1 spiculation feature from the prior, were most often chosen. The classifier achieved an average training A_z of 0.92 and a test A_z of 0.88. For comparison, a classifier was trained and tested using features extracted from the 120 current single images. This classifier achieved an average training A_z of 0.90 and a test A_z of 0.82. The information on the prior image significantly (p=0.01) improved the accuracy for classification of the masses.

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Lubomir M. Hadjiiski, Berkman Sahiner, Heang-Ping Chan, Nicholas Petrick, Mark A. Helvie M.D., and Metin Nafi Gurcan "Analysis of temporal change of mammographic features for computer-aided characterization of malignant and benign masses", Proc. SPIE 4322, Medical Imaging 2001: Image Processing, (3 July 2001); https://doi.org/10.1117/12.431142

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