title: Volume-based features for detection of bladder wall abnormal regions via MR cystography.
abstract: This paper proposes a framework for detecting the suspected abnormal region of the bladder wall via magnetic resonance (MR) cystography. Volume-based features are used. First, the bladder wall is divided into several layers, based on which a path from each voxel on the inner border to the outer border is found. By using the path length to measure the wall thickness and a bent rate (BR) term to measure the geometry property of the voxels on the inner border, the seed voxels representing the abnormalities on the inner border are determined. Then, by tracing the path from each seed, a weighted BR term is constructed to determine the suspected voxels, which are on the path and inside the bladder wall. All the suspected voxels are grouped together for the abnormal region. This work is significantly different from most of the previous computer-aided bladder tumor detection reports on two aspects. First of all, the T (1)-weighted MR images are used which give better image contrast and texture information for the bladder wall, comparing with the computed tomography images. Second, while most previous reports detected the abnormalities and indicated them on the reconstructed 3-D bladder model by surface rendering, we further determine the possible region of the abnormality inside the bladder wall. This study aims at a noninvasive procedure for bladder tumor detection and abnormal region delineation, which has the potential for further clinical analysis such as the invasion depth of the tumor and virtual cystoscopy diagnosis. Five datasets including two patients and three volunteers were used to test the presented method, all the tumors were detected by the method, and the overlap rates of the regions delineated by the computer against the experts were measured. The results demonstrated the potential of the method for detecting bladder wall abnormal regions via MR cystography.
authors.affiliation: Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China. duan.chaijie@sz.tsinghua.edu.cn
authors.forename: Chaijie
authors.initials: C
authors.lastname: Duan
authors.forename: Kehong
authors.initials: K
authors.lastname: Yuan
authors.forename: Fanghua
authors.initials: F
authors.lastname: Liu
authors.forename: Ping
authors.initials: P
authors.lastname: Xiao
authors.forename: Guoqing
authors.initials: G
authors.lastname: Lv
authors.forename: Zhengrong
authors.initials: Z
authors.lastname: Liang
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D055672:Static Electricity; D001743:Urinary Bladder / Q000033:anatomy & histology / Q000473:pathology*; D001749:Urinary Bladder Neoplasms / Q000175:diagnosis / Q000473:pathology*