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http://dx.doi.org/10.6109/jicce.2017.15.1.49

Ganglion Cyst Region Extraction from Ultrasound Images Using Possibilistic C-Means Clustering Method  

Suryadibrata, Alethea (Department of Information Technology, Multimedia Nusantara University)
Kim, Kwang Baek (Division of Computer Software Engineering, Silla University)
Publication Information
Journal of information and communication convergence engineering / v.15, no.1, 2017 , pp. 49-52 More about this Journal
Abstract
Ganglion cysts are benign soft tissues usually encountered in the wrist. In this paper, we propose a method to extract a ganglion cyst region from ultrasonography images by using image segmentation. The proposed method using the possibilistic c-means (PCM) clustering method is applicable to ganglion cyst extraction. The methods considered in this thesis are fuzzy stretching, median filter, PCM clustering, and connected component labeling. Fuzzy stretching performs well on ultrasonography images and improves the original image. Median filter reduces the speckle noise without decreasing the image sharpness. PCM clustering is used for categorizing pixels into the given cluster centers. Connected component labeling is used for labeling the objects in an image and extracting the cyst region. Further, PCM clustering is more robust in the case of noisy data, and the proposed method can extract a ganglion cyst area with an accuracy of 80% (16 out of 20 images).
Keywords
Clustering; Cyst; Fuzzy stretching; Possibilistic c-means; Ultrasonography;
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