Title :
Texture based classification of mass abnormalities in mammograms
Author :
Baeg, S. ; Kehtarnavaz, N.
Author_Institution :
Dept. of Electr. Eng., Texas A&M Univ., College Station, TX, USA
Abstract :
This paper presents a scheme for the classification of mass abnormalities in digitized or digital mammograms based on two novel image texture features. The first texture feature provides a measure of smoothness/denseness and is obtained by applying a morphological operator to maxima/minima image points. The second texture feature reflects a measure of architectural distortion and is derived from image gradients. A three-layer backpropagation neural network is used as the classifier. The performance of the classification scheme is evaluated by carrying out a receiver operating characteristic (ROC) analysis. Classification of 150 biopsy proven masses into benign and malignant classes resulted in a ROC area of 0.91. The results obtained demonstrate the potential of using this scheme as an electronic second opinion to lower the number of unnecessary biopsies
Keywords :
backpropagation; cancer; diagnostic radiography; feature extraction; image classification; image texture; mammography; medical image processing; neural nets; architectural distortion; benign classes; biopsy proven masses; denseness measure; digital mammograms; digitized mammograms; electronic second opinion; image gradients; malignant classes; mass abnormalities; maxima/minima image points; morphological operator; receiver operating characteristic analysis; smoothness measure; texture based classification; three-layer backpropagation neural network; Aging; Biopsy; Cancer; Distortion measurement; Image databases; Image texture; Mammography; Spatial databases; Surgery; Tellurium;
Conference_Titel :
Computer-Based Medical Systems, 2000. CBMS 2000. Proceedings. 13th IEEE Symposium on
Conference_Location :
Houston, TX
Print_ISBN :
0-7695-0484-1
DOI :
10.1109/CBMS.2000.856894