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A novel region growing approach using similarity set score and homogeneity based on neutrosophic set for ultrasound image segmentation
Author(s): Xue Jiang; Yanhui Guo; Yao Lu
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Paper Abstract

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.

Paper Details

Date Published: 6 May 2019
PDF: 6 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110692C (6 May 2019); doi: 10.1117/12.2524144
Show Author Affiliations
Xue Jiang, Sun Yat-Sen Univ. (China)
Yanhui Guo, Univ. of Illinois at Springfield (United States)
Yao Lu, Sun Yat-Sen Univ. (China)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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