Document Type

Journal Article

Publication Title

Journal of King Saud University - Computer and Information Sciences

Volume

34

Issue

2

First Page

86

Last Page

94

Publisher

Elsevier

School

School of Science

Comments

Hossain, M. S. (2022). Microc alcification segmentation using modified u-net segmentation network from mammogram images. Journal of King Saud University-Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2019.10.014

Abstract

Breast cancer is the most common aggressive cancer in women while the early detection of this cancer can reduce the aggressiveness. But it is challenging to identify breast cancer features such as micro-calcification from mammogram images by the human eye because of its size and appearance. Therefore, the automatic detection of micro-calcification is essential for diagnosis and proper treatment. This work introduces an automated approach and segments any micro-calcification in the mammogram images. At first, the preprocessing applications of images are applied to enhance the image. After that, the breast region is segmented from the pectoral region. The suspicious regions are detected using fuzzy C-means clustering algorithm and divided them into negative and positive patches. This procedure eliminates the manual labelling of the region of interest. The positive patches which contain micro-calcification pixels are taken to train a modified U-net segmentation network. Finally, the trained network is utilised to segment the micro-calcification area automatically from the mammogram images. This process can help as an assistant to the radiologist for early diagnosis and increase the segmentation accuracy of the micro-calcification regions. The proposed system is trained up with a Digital Database for Screening Mammography (DDSM), which is prepared by the University of South Florida, USA. We obtain 98.5% F-measure and 97.8% Dice score respectively. Besides, Jaccard index is 97.4%. The average accuracy of the proposed method is 98.2% which provides better performance than state-of-the-art methods. This work can be embedded with the real-time mammography system.

DOI

10.1016/j.jksuci.2019.10.014

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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