Texture guided active appearance model propagation for prostate segmentation

Authors:
Soumya Ghose;Arnau Oliver;Robert Martí;Xavier Lladó;Jordi Freixenet;Joan C. Vilanova;Fabrice Meriaudeau
Affiliations:
Computer Vision and Robotics Group, University of Girona, Girona, Spain and Laboratoire Le2I, UMR, CNRS, Université de Bourgogne, Le Creusot, France;Computer Vision and Robotics Group, University of Girona, Girona, Spain;Computer Vision and Robotics Group, University of Girona, Girona, Spain;Computer Vision and Robotics Group, University of Girona, Girona, Spain;Computer Vision and Robotics Group, University of Girona, Girona, Spain;Girona Magnetic Resonance Imaging Center, Girona, Spain;Laboratoire Le2I, UMR, CNRS, Université de Bourgogne, Le Creusot, France
Venue:
MICCAI'10 Proceedings of the 2010 international conference on Prostate cancer imaging: computer-aided diagnosis, prognosis, and intervention
Year:
2010

Citing 5
Cited 3

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Integrating the Validation Incremental Neural Network and Radial-Basis Function Neural Network for Segmenting Prostate in Ultrasound Images

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Automatic segmentation of the prostate from ultrasound data using feature-based self organizing map

SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis

Multiple mean models of statistical shape and probability priors for automatic prostate segmentation

MICCAI'11 Proceedings of the 2011 international conference on Prostate cancer imaging: image analysis and image-guided interventions
A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images

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Rotational-Slice-Based prostate segmentation using level set with shape constraint for 3d end-firing TRUS guided biopsy

MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I

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Abstract

Fusion of Magnetic Resonance Imaging (MRI) and Trans Rectal Ultra Sound (TRUS) images during TRUS guided prostate biopsy improves localization of the malignant tissues. Segmented prostate in TRUS and MRI improve registration accuracy and reduce computational cost of the procedure. However, accurate segmentation of the prostate in TRUS images can be a challenging task due to low signal to noise ratio, heterogeneous intensity distribution inside the prostate, and imaging artifacts like speckle noise and shadow. We propose to use texture features from approximation coefficients of Haar wavelet transform for propagation of a shape and appearance based statistical model to segment the prostate in a multi-resolution framework. A parametric model of the propagating contour is derived from Principal Component Analysis of prior shape and texture informations of the prostate from the training data. The parameters are then modified with prior knowledge of the optimization space to achieve optimal prostate segmentation. The proposed method achieves a mean Dice Similarity Coefficient value of 0.95 ± 0.01, and mean segmentation time of 0.72 ± 0.05 seconds when validated on 25 TRUS images, grabbed from video sequences, in a leave-one-out validation framework. Our proposed model performs computationally efficient accurate prostate segmentation in presence of intensity heterogeneity and imaging artifacts.