Ms. Xue Jiang Profile

Xue Jiang

at Sun Yat-sen Univ.

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Proceedings Article | 6 May 2019 Paper

A novel region growing approach using similarity set score and homogeneity based on neutrosophic set for ultrasound image segmentation

Xue Jiang, Yanhui Guo, Yao Lu

Proceedings Volume 11069, 110692C (2019) https://doi.org/10.1117/12.2524144

KEYWORDS: Image segmentation, Tumors, Ultrasonography, Breast, Computer aided diagnosis and therapy, Image processing algorithms and systems, Computing systems

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Breast tumor segmentation is an essential step in a computer aided diagnosis (CAD) system. Due to the speckle noise, various size and locations of lesions, it is a challenging task to segment the tumors on breast ultrasound (BUS) image accurately. In this paper, we propose a region growing method which applies the similarity score and homogeneity based on the neutrosophic set (NS) domain to segment and detect tumors. At first, the original BUS image is transferred to the NS domain and calculate the different NS elements to obtain the NS image. Then, a thresholding method and morphology method are used to locate seed regions, and after that each seed region grows separately. The direction of region growing depends on the homogeneity vector, similarity set score vector and distance vector between the candidate points and seed regions. The suspensive condition also lies on the homogeneity vector and similarity set score vector between the seed region and the NS image. Finally, a pretrained deep learning network and transfer learning scheme are used for false positive reduction. Experiment on various clinic BUS images has suggested that the proposed method is able to segment the BUS image and extract tumor accurately.

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