DocumentCode
2889941
Title
Coupled Directional Level Set for MR Image Segmentation
Author
Xianjing Qin ; Yang Liu ; Hongbing Lu ; Xuelong Li ; Pingkun Yan
Author_Institution
State Key Lab. of Transient Opt. & Photonics, Xi´an Inst. of Opt. & Precision Mech., Xi´an, China
Volume
1
fYear
2012
fDate
12-15 Dec. 2012
Firstpage
185
Lastpage
190
Abstract
Segmenting bladder wall for thickness measuring is a fundamental operation in bladder magnetic resonance (MR) image analysis since thickening of the bladder wall may indicate abnormality. Active contours have been used for bladder wall segmentation, which can be broadly divided into gradient-based and region-based methods, according to the used image features. However, the artifacts in MR images and the complex background outside the bladder lead to significant challenges for segmentation. In this paper, a coupled directional level set model is proposed to segment the outer and inner boundaries simultaneously by exploiting the directional gradient, region information and thickness prior of the bladder wall. With our proposed method, the influence of the artifacts in the bladder lumen and the complicated intensity distribution of soft tissues surrounding the bladder can be appreciably reduced. Promising results on 119 bladder MR images have demonstrated the performance of the presented method.
Keywords
biomedical MRI; feature extraction; gradient methods; image segmentation; medical image processing; set theory; thickness measurement; MR image segmentation; bladder lumen artifacts; bladder magnetic resonance image analysis; bladder wall segmentation; complicated soft tissue intensity distribution; coupled directional level set; directional gradient; gradient-based methods; image features; inner boundary segmentation; outer boundary segmentation; region information; region-based methods; thickness measurement; thickness prior; Biological tissues; Bladder; Cancer; Image edge detection; Image segmentation; Level set; Mathematical model; Coupled level set; Directional gradient; Image segmentation; Thickness constraint;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Applications (ICMLA), 2012 11th International Conference on
Conference_Location
Boca Raton, FL
Print_ISBN
978-1-4673-4651-1
Type
conf
DOI
10.1109/ICMLA.2012.39
Filename
6406610
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