Segmentation of prostate using interactive finsler active contours and shape prior

Authors:
Foued Derraz;Abdelmalik Taleb-Ahmed;Azzeddine Chikh;Christina Boydev;Laurent Peyrodie;Gerard Forzy
Affiliations:
Faculté Libre de Médicine, Institut Catholique de Lille, France, LAMIH UMR CNRS 8201, Valenciennes, France;LAMIH UMR CNRS 8201, Valenciennes, France;Biomedical Engineering Laboratory, Technology College, Abou Bekr Belkaid University, Algeria;LAMIH UMR CNRS 8201, Valenciennes, France;HEI, LAGIS UMR CNRS 3304, Lille, France;Faculté Libre de Médicine, Institut Catholique de Lille, France
Venue:
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
Year:
2012

Citing 5
Cited 0

Affine-Invariant Geometric Shape Priors for Region-Based Active Contours

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Global Minimization of the Active Contour/Snake Model

Journal of Mathematical Imaging and Vision
Finsler Active Contours

IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow

IEEE Transactions on Image Processing
Evaluation of Optimization Methods for Nonrigid Medical Image Registration Using Mutual Information and B-Splines

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a new interactive segmentation framework to segment the prostate from MR prostate imagery. We first explicitly address the segmentation problem based on fast globally Finsler Active Contours (FAC) by incorporating both statistical and geometric shape prior knowledge. In doing so, we are able to exploit the more global aspects of segmentation by incorporating user feedback in segmentation process. In addition, once the prostate shape has been segmented, a cost functional is designed to incorporate both the local image statistics as user feedback and the learned shape prior. We provide experimental results, which include several challenging clinical data sets, to highlight the algorithm's capability of robustly handling supine/prone prostate segmentation task.