Title: Segmentation of Liver Cyst in Ultrasound Image Based on Adaptive Threshold Algorithm and Particle Swarm Optimization
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| Authors: Zhu, HJ; Zhuang, ZH; Zhou, JL; Zhang, F; Wang, XJ; Wu, YH
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| Author Full Names: Zhu, Haijiang; Zhuang, Zhanhong; Zhou, Jinglin; Zhang, Fan; Wang, Xuejing; Wu, Yihong
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| Source: MULTIMEDIA TOOLS AND APPLICATIONS, 76 (6):8951-8968; 10.1007/s11042-016-3486-z MAR 2017
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| Language: English
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| Abstract: To find the optimum threshold of an image is still an important research topic in the recent years. This paper presents a segmentation of liver cyst for ultrasound image through combining Wellner's thresholding algorithm with particle swarm optimization (PSO). The proposed method firstly obtains an optimal parameter, which expressed as a percentage or fixed amount of dark objects against a white background in a gray image, of Wellner's thresholding algorithm by PSO method. And then the gray image is binarized according to the optimized parameter. Finally, a semi-automatic method for locating and identifying multiple liver cysts or single liver cyst of ultrasound images is performed. For a validation, the results of the proposed technique are compared with those of other segmented methods. We also tested 92 ultrasound images of the liver cysts by our software. The corrected identification rate of the single liver cysts is 97.7%, and that of multiple liver cysts is 87.5 %. Experimental results demonstrate that the proposed technique is reliable on segmenting the contour of liver cyst and identifying single or multiple liver cysts.
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| ISSN: 1380-7501
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| eISSN: 1573-7721
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| IDS Number: ER7TZ
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| Unique ID: WOS:000399017800059
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