Automated detection of spiculated lesions and architectural distortions in digitized mammograms

Ming Zhang; Maryellen Lissak Giger

doi:10.1117/12.208760

12 May 1995 Automated detection of spiculated lesions and architectural distortions in digitized mammograms

Ming Zhang, Maryellen Lissak Giger

Proceedings Volume 2434, Medical Imaging 1995: Image Processing; (1995) https://doi.org/10.1117/12.208760
Event: Medical Imaging 1995, 1995, San Diego, CA, United States

Abstract

Mammographic stellate lesions and architectural distortions are usually associated with malignancy, which makes them important signs in the screening for breast cancer. This paper proposes a novel method for the automatic detection of these lesions with the use of a Hough spectrum based geometric texture analysis. The Hough spectrum technique is developed from the traditional Hough transform, which is effective in the description of geometric structures. The Hough spectrum inherits such an ability and extends it into texture analysis for the description of those texture features geometric in nature, such as the spicula of a stellate lesion. When applied in mammographic image processing, an ROI is first transformed into its Hough spectrum, and then thresholding is conducted with its threshold level based on the statistical properties of the spectrum. Those ROIs with strong signals of spiculation are then identified as regions of potential lesions. Our preliminary studies, using only the Hough spectrum based technique without further feature analyses to reduce false positive detection, have yielded sensitivities of 81% for spiculated masses and 67% for architectural distortions at false positive rates of 0.97 and 2.2 per image, respectively. This manifests clearly the potential of the developed technique in the detection of such types of mammographic lesions.

Citation Download Citation

Ming Zhang and Maryellen Lissak Giger "Automated detection of spiculated lesions and architectural distortions in digitized mammograms", Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995); https://doi.org/10.1117/12.208760

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