NDLI: Osteosarcoma segmentation in CT images based on hybrid relative fuzzy connectedness

Content Provider	IEEE Xplore Digital Library
Author	Chunxiao Chen Shaowei Ding Qiuyi Wu Ning Li Sujia Wu
Copyright Year	2012
Description	Author affiliation: Dept. of Inf. & Commun. Eng., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China (Ning Li) \|\| Dept. of Orthopedics, Nanjing Gen. Hosp. of Nanjing Mil. Command, Nanjing, China (Sujia Wu) \|\| Dept. of Biomed. Eng., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing, China (Chunxiao Chen; Shaowei Ding) \|\| Rice Univ., Houston, TX, USA (Qiuyi Wu)
Abstract	Osteosarcoma is very harmful and difficult to diagnose, which occurs most often in children and adolescents. Aiming at the difficulties in extracting tumor from Osteosarcoma CT images, which have few gray differences with the surrounding tissues, this paper proposes an interactive hybrid segmentation method combining modified relative fuzzy connectedness and confidence connected algorithm for Osteosarcoma CT images. In this paper, we select seed points from object and background areas interactively, and produce a rough segmentation from Osteosarcoma CT image by using the confidence connected method. The mean and variance values obtained from the rough regions are the initial values for fuzzy connectedness algorithm. In addition, we have developed a novel fuzzy spel affinity function, and compute fuzzy connectedness from each point to the seeds in object and background areas, then divide Osteosarcoma CT image into tumor tissue region and non-tumor tissue region by comparing the values of these two fuzzy connectedness. Finally, the segmented tumor tissue is rendered in 3D by the ray casting algorithm based on Graphics Processing Unit. According to the experiment results, the interactive hybrid segmentation method has a higher similarity index with the expert's manual segmentation compared to the original fuzzy connectedness algorithm. Also, it can segment Osteosarcoma tumor tissue more effectively and accurately, reducing the influence by the manual choices of the threshold and other parameters.
Starting Page	324
Ending Page	328
File Size	411884
Page Count	5
File Format	PDF
ISBN	9781467311830
e-ISBN	9781467311847
DOI	10.1109/BMEI.2012.6512993
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	confidence connected volume rendering Osteosarcoma relative fuzzy connectedness segmentation
Content Type	Text
Resource Type	Article

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