Paper Info
Title
Segmentation Of Prostate In Diffusion Mr Images Via Clustering
Abstract
Automatic segmentation of prostate gland in magnetic resonance (MR) images is a challenging task due to large variations of prostate shapes and indistinct boundaries with adjacent tissues. In this paper, we propose an automatic pipeline to segment prostate gland in diffusion magnetic resonance images (dMRI). The most common approach for segmenting prostate in MR images is based on image registration, which is computationally expensive and solely relies on the pre-segmented images (also known as atlas). In contrast, the proposed method uses a clustering method applied to the dMRI to separate prostate gland from the surrounding tissues followed by a post processing stage via active contours. The proposed pipeline was validated on prostate MR images of 25 patients and the segmentation results were compared to manually delineated prostate contours. The proposed method achieves an overall accuracy with mean Dice Similarity Coefficient (DSC) of 0.84 +/- 0.04, while being the most effective in the middle prostate gland producing a mean DSC of 0.91 +/- 0.03. The proposed method has the potential to be integrated into clinical decision support systems that aid radiologists in monitoring prostate cancer.
Year
DOI
Venue
2017
10.1007/978-3-319-59876-5_52
IMAGE ANALYSIS AND RECOGNITION, ICIAR 2017
Keywords
Field
DocType
Prostate segmentation, Diffusion magnetic resonance imaging (dMRI), Gaussian Mixture Model (GMM), Active contour
Active contour model,Computer vision,Pattern recognition,Segmentation,Computer science,Prostate segmentation,Prostate,Prostate cancer,Artificial intelligence,Cluster analysis,Image registration,Magnetic resonance imaging
Conference
Volume
ISSN
Citations 
10317
0302-9743
0
PageRank 
References 
Authors
0.34
3
5
Name
Order
Citations
PageRank
Junjie Zhang193.65
Sameer Baig200.34
Alexander Wong335169.61
Masoom Haider417222.45
Farzad Khalvati542.62