Automated detection of breast masses on digital mammograms using adaptive density-weighted contrast-enhancement filtering

Nicholas Petrick; Heang-Ping Chan; Berkman Sahiner; Datong Wei; Mark A. Helvie M.D.; Mitchell M. Goodsitt; Dorit D. Adler

doi:10.1117/12.208731

12 May 1995 Automated detection of breast masses on digital mammograms using adaptive density-weighted contrast-enhancement filtering

Nicholas Petrick, Heang-Ping Chan, Berkman Sahiner, Datong Wei, Mark A. Helvie M.D., Mitchell M. Goodsitt, Dorit D. Adler

Author Affiliations +

Proceedings Volume 2434, Medical Imaging 1995: Image Processing; (1995) https://doi.org/10.1117/12.208731
Event: Medical Imaging 1995, 1995, San Diego, CA, United States

Abstract

This paper presents a novel approach for segmentation of suspicious mass regions in digitized mammograms using a new adaptive density-weighted contrast enhancement (DWCE) filter in conjunction with Laplacian-Gaussian (LG) edge detection. The new algorithm processes a mammogram in two stages. In the first stage the entire mammogram is filtered globally using a DWCE adaptive filter which enhances the local contrast of the image based on its local mean pixel values. The enhanced image is then segmented with an LG edge detector into isolated objects. In the second stage of processing, the DWCE adaptive filter and the edge detector are applied locally to each of the segmented object regions detected in the first stage. The number of objects is then reduced based on morphological features. ROIs are selected from the remaining object set based on the centroid locations of the individual objects. The selected ROIs are then input to either a linear discriminant analysis (LDA) classifier or a convolution neural network (CNN) to further differentiate true-positives and false-positives. In this study ROIs obtained from a set of 84 images were used to train the LDA and CNN classifiers. The DWCE algorithm was then,used to extract ROIs from a set of 84 test images. The trained LDA and CNN classifiers were subsequently applied to the extracted ROIs, and the dependence of the detection system's accuracy on the feature extraction and classification techniques was analyzed.

Citation Download Citation

Nicholas Petrick, Heang-Ping Chan, Berkman Sahiner, Datong Wei, Mark A. Helvie M.D., Mitchell M. Goodsitt, and Dorit D. Adler "Automated detection of breast masses on digital mammograms using adaptive density-weighted contrast-enhancement filtering", Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995); https://doi.org/10.1117/12.208731

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