Non-subsampled Contourlet Based Deformable Model For Medical Image Segmentation

被引：0
|
作者：
Mewada, Hiren [1 ]
Patnaik, Suprava [2 ]
机构：
[1] Charotar Univ Sci & Technol Changa, Dept Elect & Commun Engn, Ta Anand, Gujarat, India
[2] St Xavier Coll Engn, Dept Elect, Mumabai, Maharastra, India
来源：
2014 INTERNATIONAL CONFERENCE ON INFORMATION COMMUNICATION AND EMBEDDED SYSTEMS (ICICES) | 2014年
关键词：
Deformable Model; Non-subsampled Contourlet transform; Segmentation;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Artifacts involved in medical images like noise and intensity inhomogeneity makes an automated approach of segmentation failure. This paper proposed deformable model based automatic image segmentation model with above said constraints. The deformable models are sensitive to the noise and image intensities. Therefore use of non-subsampled contourlet transform is proposed to tackle these artifacts and to enhance images. These enhanced images are utilized along with region based deformable model for successful segmentation. The illustrated results on various types of medical images show the advantages of proposed model.
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页数：5
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