Mr. Robert S. Xu Profile

Robert S. Xu

at Univ of Waterloo

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Proceedings Article | 13 March 2010 Paper

A probability tracking approach to segmentation of ultrasound prostate images using weak shape priors

Robert Xu, Oleg Michailovich, Igor Solovey, Magdy Salama

Proceedings Volume 7623, 76234E (2010) https://doi.org/10.1117/12.844570

KEYWORDS: Image segmentation, Prostate, Ultrasonography, Cancer, Signal to noise ratio, Image processing algorithms and systems, Detection and tracking algorithms, Shape analysis, Curium, Diffusion

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Prostate specific antigen density is an established parameter for indicating the likelihood of prostate cancer. To this end, the size and volume of the gland have become pivotal quantities used by clinicians during the standard cancer screening process. As an alternative to manual palpation, an increasing number of volume estimation methods are based on the imagery data of the prostate. The necessity to process large volumes of such data requires automatic segmentation algorithms, which can accurately and reliably identify the true prostate region. In particular, transrectal ultrasound (TRUS) imaging has become a standard means of assessing the prostate due to its safe nature and high benefit-to-cost ratio. Unfortunately, modern TRUS images are still plagued by many ultrasound imaging artifacts such as speckle noise and shadowing, which results in relatively low contrast and reduced SNR of the acquired images. Consequently, many modern segmentation methods incorporate prior knowledge about the prostate geometry to enhance traditional segmentation techniques. In this paper, a novel approach to the problem of TRUS segmentation, particularly the definition of the prostate shape prior, is presented. The proposed approach is based on the concept of distribution tracking, which provides a unified framework for tracking both photometric and morphological features of the prostate. In particular, the tracking of morphological features defines a novel type of "weak" shape priors. The latter acts as a regularization force, which minimally bias the segmentation procedure, while rendering the final estimate stable and robust. The value of the proposed methodology is demonstrated in a series of experiments.

Proceedings Article | 24 February 2010 Paper

A probabilistic framework for ultrasound image decomposition

Igor Solovey, Oleg Michailovich, Robert Xu

Proceedings Volume 7625, 76252O (2010) https://doi.org/10.1117/12.844367

KEYWORDS: Image segmentation, Ultrasonography, Feature extraction, Tissues, Speckle, Image processing algorithms and systems, Electroluminescence, Medical imaging, Image processing, Statistical analysis

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Image segmentation and tissue characterization are fundamental tasks of computer-aided diagnosis (CAD) in medical ultrasound imaging. As an initial step, such algorithms are usually based on extraction of pertinent features from the acquired ultrasound data. Typically, these features are computed directly from localized image segments, thereby representing local statistical properties of the image. However, the process of image formation of medical ultrasound suggests that such an approach could result in a variety of unwanted artifacts (such as excessively smooth segmentation boundaries or misclassification) at subsequent stages of the algorithm. In this work, we propose to first decompose the observed images into a number of their statistically distinct components. The decomposition is based on the maximum-a-posteriori (MAP) statistical framework which has been derived based on the signal and noise models appropriate for the ultrasound setting. Subsequently, each resulting component is used separately to extract a set of its corresponding features. When retrieved in this way (rather than directly from the observed image), the combined set of resulting features is shown to be capable of better discriminating between different tissue types. Examples of in silico simulations and in vivo experiments are provided to illustrate the practical usefulness of this technique for improving the results of ultrasound image segmentation.

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