Prostate segmentation with local binary patterns guided active appearance models

Soumya Ghose; Arnau Oliver; Robert Martí; Xavier Lladó; Jordi Freixenet; Joan C. Vilanova; Fabrice Meriaudeau

doi:10.1117/12.877955

11 March 2011 Prostate segmentation with local binary patterns guided active appearance models

Soumya Ghose, Arnau Oliver, Robert Martí, Xavier Lladó, Jordi Freixenet, Joan C. Vilanova, Fabrice Meriaudeau

Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 796218 (2011) https://doi.org/10.1117/12.877955
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States

Abstract

Real-time fusion of Magnetic Resonance (MR) and Trans Rectal Ultra Sound (TRUS) images aid in the localization of malignant tissues in TRUS guided prostate biopsy. Registration performed on segmented contours of the prostate reduces computational complexity and improves the multimodal registration accuracy. However, accurate and computationally efficient segmentation of the prostate in TRUS images could be challenging in the presence of heterogeneous intensity distribution inside the prostate gland, and other imaging artifacts like speckle noise, shadow regions and low Signal to Noise Ratio (SNR). In this work, we propose to enhance the texture features of the prostate region using Local Binary Patterns (LBP) for the 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 (PCA) of the prior shape and texture information of the prostate from the training data. The estimated parameters are then modified with the prior knowledge of the optimization space to achieve an optimal segmentation. The proposed method achieves a mean Dice Similarity Coefficient (DSC) value of 0.94±0.01 and a mean segmentation time of 0.68±0.02 seconds when validated with 70 TRUS images of 7 datasets in a leave-one-patient-out validation framework. Our method performs computationally efficient and accurate prostate segmentation in the presence of intensity heterogeneities and imaging artifacts.

Citation Download Citation

Soumya Ghose, Arnau Oliver, Robert Martí, Xavier Lladó, Jordi Freixenet, Joan C. Vilanova, and Fabrice Meriaudeau "Prostate segmentation with local binary patterns guided active appearance models", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 796218 (11 March 2011); https://doi.org/10.1117/12.877955

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