Medical Image Segmentation Using Improved Mountain Clustering Approach

被引：3

作者：

Verma, Nishchal K. ^{[1
]}

Gupta, Payal ^{[2
]}

Agrawal, Pooja ^{[1
,3
]}

Hanmandlu, M. ^{[3
]}

Vasikarla, Shantaram ^{[4
]}

Cui, Yan ^{[1
]}

机构：

[1] Univ Tennessee, Ctr Integrat & Translat Genom, Dept Mol Sci, Memphis, TN 38163 USA

[2] UP Tech Univ, KEC Ghaziabad, Dept Comp Sci, Lucknow, Uttar Pradesh, India

[3] IIT, Dept Elect Engn, Comp Technol Grp, Delhi 110016, India

[4] Amer Inter Continental Univ, Sch Informat Technol, Los Angeles, CA 90066 USA

来源：

PROCEEDINGS OF THE 2009 SIXTH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY: NEW GENERATIONS, VOLS 1-3 | 2009年

关键词：

D O I：

10.1109/ITNG.2009.238

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents Improved Mountain Clustering (IMC) based medical image segmentation. Proposed technique is a more powerful approach for X-Ray image based diagnosing diseases like lung cancer and tuberculosis. The IMC based segmentation approach was applied on lung X-Ray images and compared with some existing techniques such as K-Means and FCM based segmentation approaches. The performance of all these segmentation approaches is compared in terms of cluster entropy as a measure of information. The segments obtained from the methods have been verified visually.

引用

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页码：1307 / +

页数：2

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