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Analysis of temporal changes of mammographic features: Computer‐aided classification of malignant and benign breast masses
Author(s) -
Hadjiiski Lubomir,
Sahiner Berkman,
Chan HeangPing,
Petrick Nicholas,
Helvie Mark A.,
Gurcan Metin
Publication year - 2001
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.1412242
Subject(s) - artificial intelligence , pattern recognition (psychology) , linear discriminant analysis , classifier (uml) , linear classifier , feature extraction , computer science , contextual image classification , feature selection , feature vector , mathematics , image (mathematics)
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 speculation features, and 12 morphological features 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 speculation features, and the current and prior mass sizes. Stepwise feature selection and linear discriminant analysis classification were used to select and merge the most useful features. A leave‐one‐case‐out resampling scheme was used 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 speculation feature from the current image, and 1 speculation feature from the prior, were most often chosen. The classifier achieved an average training A zof 0.92 and a test A zof 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 zof 0.90 and a test A zof 0.82. The information on the prior image significantly ( p = 0.015 ) improved the accuracy for classification of the masses.