Statistical modeling of oriented line patterns in mammograms

Tim C. Parr; Christopher J. Taylor; Susan M. Astley; Caroline R. M. Boggis

doi:10.1117/12.274159

25 April 1997 Statistical modeling of oriented line patterns in mammograms

Tim C. Parr, Christopher J. Taylor, Susan M. Astley, Caroline R. M. Boggis

Proceedings Volume 3034, Medical Imaging 1997: Image Processing; (1997) https://doi.org/10.1117/12.274159
Event: Medical Imaging 1997, 1997, Newport Beach, CA, United States

Abstract

Malignant breast lesions in x-ray mammograms are often characterized by abnormal patterns of linear structures. Architectural distortions and stellate lesions are examples of patterns frequently presenting with an appearance of radiating linear structures. Attempts to automatically detect these abnormalities have generally concentrated on features of known importance, such as radiating linear structure concurrency, spread of focus and radial distance. We present an alternative statistically based representation that is both complete and uncommitted. Our representation places no emphasis on the features known to be important, yet clearly incorporates them. We present results for an experiment in which 92% of 9600 lesion/non-lesion pixels were classified correctly. Using a set of 150 high resolution digitized mammograms a lesion detection sensitivity of 80% was obtained at a specificity of 0.38 false positives per image.

Citation Download Citation

Tim C. Parr, Christopher J. Taylor, Susan M. Astley, and Caroline R. M. Boggis "Statistical modeling of oriented line patterns in mammograms", Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); https://doi.org/10.1117/12.274159

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