Mammography segmentation with maximum likelihood active contours

We present a computer-aided approach to segmenting suspicious lesions in digital mammograms, based on a novel maximum likelihood active contour model using level sets (MLACMLS). The algorithm estimates the segmentation contour that best separates the lesion from the background using the Gamma distribution to model the intensity of both regions (foreground and background). The Gamma distribution parameters are estimated by the algorithm. We evaluate the performance of MLACMLS on real mammographic images. Our results are compared to those of two leading related methods: The adaptive level set-based segmentation method (ALSSM) and the spiculation segmentation using level sets (SSLS) approach, and show higher segmentation accuracy (MLACMLS: 86.85% vs. ALSSM: 74.32% and SSLS: 57.11%). Moreover, our results are qualitatively compared with those of the Active Contour Without Edge (ACWOE) and show a better performance. Further, the suitability of using ML as the objective function as opposed to the KL divergence and to the energy functional of the ACWOE is also demonstrated. Our algorithm is also shown to be robust to the selection of a required single seed point.

The segmentation algorithm consists of two basic stages: preprocessing and segmentation. In the preprocessing block, the original image is smoothed and denoised using a nonlinear fuzzy filter; then the filtered image is fed to the subsequent segmentation block to be delineated using a level set based active contour model.Figure optionsDownload high-quality image (128 K)Download as PowerPoint slideHighlights
► We show a novel mammography segmentation method for disease monitoring purposes.
► We propose maximum likelihood active contour method using level set (MLACMLS).
► The MLACMLS was evaluated on 100 test mammograms.
► The MLACMLS is compared quantitatively and qualitatively with the state of the art methods.
► The MLACMLS outperforms the comparison methods with an avg. success rate of 86.85%.