Title :
An Adaptive Window-Setting Scheme for Segmentation of Bladder Tumor Surface via MR Cystography
Author :
Duan, Chaijie ; Yuan, Kehong ; Liu, Fanghua ; Xiao, Ping ; Lv, Guoqing ; Liang, Zhengrong
Author_Institution :
Dept. of Biomed. Eng., Tsinghua Univ., Beijing, China
fDate :
7/1/2012 12:00:00 AM
Abstract :
This paper proposes an adaptive window-setting scheme for noninvasive detection and segmentation of bladder tumor surface in T_1 -weighted magnetic resonance (MR) images. The inner border of the bladder wall is first covered by a group of ball-shaped detecting windows with different radii. By extracting the candidate tumor windows and excluding the false positive (FP) candidates, the entire bladder tumor surface is detected and segmented by the remaining windows. Different from previous bladder tumor detection methods that are mostly focusing on the existence of a tumor, this paper emphasizes segmenting the entire tumor surface in addition to detecting the presence of the tumor. The presented scheme was validated by ten clinical T1-weighted MR image datasets (five volunteers and five patients). The bladder tumor surfaces and the normal bladder wall inner borders in the ten datasets were covered by 223 and 10 491 windows, respectively. Such a large number of the detecting windows makes the validation statistically meaningful. In the FP reduction step, the best feature combination was obtained by using receiver operating characteristics or ROC analysis. The validation results demonstrated the potential of this presented scheme in segmenting the entire tumor surface with high sensitivity and low FP rate. This study inherits our previous results of automatic segmentation of the bladder wall and will be an important element in our MR-based virtual cystoscopy or MR cystography system.
Keywords :
biomedical MRI; image segmentation; medical image processing; tumours; FP reduction step; MR cystography; ROC analysis; T1 weighted magnetic resonance images; adaptive window setting scheme; bladder tumor surface segmentation; false positive candidates; noninvasive detection; receiver operating characteristics; Bladder; Feature extraction; Geometry; Image segmentation; Level set; Silicon; Tumors; Adaptive window setting; bladder tumor surface segmentation; computer-aided detection and diagnosis (CAD); Case-Control Studies; Databases, Factual; Diagnostic Techniques, Urological; Humans; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; ROC Curve; Reproducibility of Results; Urinary Bladder; Urinary Bladder Neoplasms;
Journal_Title :
Information Technology in Biomedicine, IEEE Transactions on
Conference_Location :
5/22/2012 12:00:00 AM
DOI :
10.1109/TITB.2012.2200496