A Novel Intuitionistic Fuzzy Set Approach for Segmentation of Kidney MR Images

被引：0

作者：

Mushrif, Shreyas ^{[1
]}

Morales, Aldo ^{[1
]}

Sica, Christopher ^{[2
]}

Yang, Qing X. ^{[2
]}

Eskin, Susan ^{[1
]}

Sinoway, Lawrence ^{[3
]}

机构：

[1] Penn State Univ, Dept Elect Engn, Harrisburg, PA 16801 USA

[2] Penn State Univ, Dept Radiol, Coll Med, Hershey, PA USA

[3] Penn State Univ, Inst Heart & Vasc, Coll Med, Hershey, PA USA

来源：

PROCEEDINGS OF 2016 IEEE SIGNAL PROCESSING IN MEDICINE AND BIOLOGY SYMPOSIUM (SPMB) | 2016年

关键词：

Hamming distance; kidney MR image; intuitionistic fuzzy set; histon; restricted equivalence function; segmentation; ALGORITHM;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

This paper presents a novel algorithm, which uses intuitionistic fuzzy sets and rough set theory to segment the renal components in kidney MR images. A new membership function is proposed and then is used to obtain an intuitionistic fuzzy model of the image to compensate the inherent heterogeneity present among the different renal tissue classes. In addition, a new method, which uses Hamming distance is proposed to calculate the histon. The histon is then used to compute intuitionistic fuzzy roughness measure which yields optimum valley points for image segmentation. The proposed algorithm segments the kidney MR images into medulla, cortex, and blood vessels. The quantitative performance evaluation indicates better performance of the proposed algorithm over a competing technique.

引用

页数：6

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