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Bruno Goes

at Univ Federal de Uberlândia

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Proceedings Article | 10 December 2021 Paper

Analysis of breast lesions segmentation by watershed with variation of structuring elements

Bruno A. Goes, Ana C. Patrocínio

Proceedings Volume 12088, 120880E (2021) https://doi.org/10.1117/12.2606250

KEYWORDS: Image segmentation, Diamond, Breast, Wavelets, Filtering (signal processing), Visualization, Image processing, Denoising, Breast cancer, Image processing algorithms and systems

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Mammography is the main exam used to perform early detection of breast cancer. Using digital mammographic images processing, it’s possible to improve visualization of structures and facilitate diagnosis, using noise-reduction filters, contrast enhancement and segmentation. Segmentation divides the image in regions of interest (lesions) and background. Watershed segmentation performs a topographic analysis of the image and object’s boundaries extraction. Watershed demands using markers, that limit the regions where processing will be applied, avoiding issues such as over-segmentation. The markers can be defined using morphological operators, which use structuring elements to identify interest pixels. Therefore, the aim of this work is to evaluate watershed segmentation of breast lesions, by changing the pre-processing and structuring elements. There were applied disk and diamond-shaped structuring elements, with 25, 50 and 100 pixels as size, in addition to noise-reduction Wiener filter and contrast enhancement with Wavelet transform and contrast limited adaptive histogram equalization (CLAHE). The results were evaluated by using accuracy, ROC curve and visual analysis of the images, allowing to more easily compare the efficiency of each processing. For the disk format obtained AUC equal to 0.891, while the diamond format has 0.909, it was possible to identify the relationship between size and pre-processing with the number of detected regions. Moreover, small structuring elements are more effective in watershed segmentation, requiring research with different denoising methods and marker definition algorithms. Finally, the results allowed us to establish how breast density and shape, size and type of lesion interfere in watershed segmentation results.

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