Automatic shape analysis and classification of mammographic calcifications

The morphology and distribution of mammographic calcifications and the way these elements vary within a cluster are valuable in distinguishing between benign and malignant calcifications. The specific aims of this study were (a) the development of an automatic tool that differentiates between benign and malignant clustered calcifications based on their morphological properties and (b) the determination of the effects of image spatial resolution on the classification process. The long term aim of the project is to use this tool to categorize detected clusters into the various types described in the breast imaging reporting and data systems of the American College of Radiology and assist the radiologists in their diagnosis.

[1]  L P Clarke,et al.  Tree-structured non-linear filter and wavelet transform for microcalcification segmentation in digital mammography. , 1994, Cancer letters.

[2]  L P Clarke,et al.  Interpretation of calcifications in screen/film, digitized, and wavelet-enhanced monitor-displayed mammograms: a receiver operating characteristic study. , 1996, Academic radiology.

[3]  Rangaraj M. Rangayyan,et al.  Application of shape analysis to mammographic calcifications , 1994, IEEE Trans. Medical Imaging.

[4]  Akira Hasegawa,et al.  Classification of microcalcifications in radiographs of pathological specimen for the diagnosis of breast cancer , 1994, Medical Imaging.

[5]  L W Bassett,et al.  Breast imaging for the 1990s. , 1991, Seminars in oncology.

[6]  K Doi,et al.  Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography. , 1987, Medical physics.

[7]  D B Kopans,et al.  Discriminating analysis uncovers breast lesions. , 1991, Diagnostic imaging.

[8]  J Y Lo,et al.  Computer-aided diagnosis of breast cancer: artificial neural network approach for optimized merging of mammographic features. , 1995, Academic radiology.

[9]  Rangaraj M. Rangayyan,et al.  Automatic detection and classification system for calcifications in mammograms , 1993, Electronic Imaging.

[10]  Atam P. Dhawan,et al.  Artificial-neural-network-based classification of mammographic microcalcifications using image structure features , 1993, Electronic Imaging.

[11]  Kevin W. Bowyer,et al.  Automated image analysis techniques for digital mammography , 1994 .

[12]  L. Tabár,et al.  Teaching atlas of mammography. , 1983, Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin. Erganzungsband.