&lt;title&gt;Quantitative three-dimensional transrectal ultrasound (TRUS) for prostate imaging&lt;/title&gt;

Pathak, Dev S.; Aarnink, R.G.; Rosette, Jean J.M.C.H. de la; Chalana, Vikram; Wijkstra, Hessel; Haynor, David R.; Debruyne, F.M.J.; Kim, Yongmin

doi:10.1117/12.312551

Cited by 21 publications

(26 citation statements)

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“…These terms are weighted by parameter functions   s  , which controls the tension along the spine (elasticity of the snake), and   s  , which controls the rigidity of the spine (stiffness of the snake). In practice, the parameter functions are often chosen to be constants [13]. The external force is a weighted combination of different functions and can be expressed as:…”

Section: Canny Filtermentioning

confidence: 99%

Boundary delineation for hepatic hemangioma in ultrasound images

Bahrami

Rezatofighi

Adeli

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Hemangioma is one of the most common benign congenital complications of the human body which can arise in interior organs and external limbs. The main aim of this work is to present a new method for automatic detection of liver hemangioma and its boundaries in ultrasound images, using image processing techniques. Overall there are two phases, the preprocessing procedure and the boundary delineation phase. The preprocessing phase includes three main stages: 1. Image contrast enhancement using Difference of Offset Gaussian (DoOG) method, 2. Applying Canny edge filtering, 3. Applying an adaptive threshold in order to detect the ROI (hemangioma). Following, the snake algorithm is used to segment the hemangioma region in the second phase. For the quantitative assessment of the proposed method for the segmentation stage, the results derived via the proposed algorithms have been compared with the corresponding segmented regions determined by an expert using three similarity criteria. The results showed 73 percent similarity without pre-processing and 90 percent similarity with pre-processing.

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Section: Canny Filtermentioning

confidence: 99%

Boundary delineation for hepatic hemangioma in ultrasound images

Bahrami

Rezatofighi

Adeli

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…These techniques either fail completely or require some kind of post-processing to remove false boundaries. To address these difficulties, deformable models have been used with improved results [18,23]. By constraining extracted boundaries to be smooth, deformable models offer increased robustness to both image noise and boundary gaps by integrating boundary elements into a coherent and consistent mathematical description.…”

Section: Introductionmentioning

confidence: 99%

Prostate segmentation in ultrasound images with deformable shape priors

Gong

Pathak

Haynor

et al. 2003

Medical Imaging 2003: Visualization, Image-Guided Procedures, and Display

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Automated prostate segmentation in ultrasound images is a challenging task due to speckle noise, missing edge segments, and complex prostate peripheral anatomy. In this paper, a Bayesian prostate segmentation algorithm is presented. It combines both prior shape and image information for robust segmentation. In this study, the prostate shape was efficiently modeled using deformable superellipse. A flexible graphical user interface has been developed to facilitate the validation of our algorithm in a clinical setting. This algorithm was applied to 66 ultrasound images collected from 8 patients. The resulting mean error between the computer-generated boundaries and the manuallyoutlined boundaries was 1.39 ± 0.60 mm, which is significantly less than the variability between human experts.

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“…Not only does this assessment allow surgeons to compare the quality of one surgical approach versus another, but also provides an evaluation of surgeons' surgical performances as related to a standard one [14]. Recent studies are focused more on a statistical modelbased segmentation algorithms [2,3,5,13,21] than deformable models [8][9][10][11]17].…”

Section: Introductionmentioning

confidence: 99%

Identifying ambiguous prostate gland contours from histology using capsule shape information and least squares curve fitting

Hussein

McKenzie

2007

Int J CARS

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Objective To obtain an accurate assessment of the percentage and depth of extra-capsular soft tissue removed with the prostate by the various surgical techniques in order to help surgeons in determining the appropriateness of different surgical approaches. This can be enhanced by an accurate and automated means of identifying the prostate gland contour. Materials and MethodsTo facilitate 3D reconstruction and, ultimately, more accurate analyses, it is essential for us to identify the capsule boundary that separates the prostate gland tissue from its extra-capsular tissue. However, the capsule is sometimes unrecognizable due to the naturally occurring intrusion of muscle and connective tissue into the prostate gland. At these regions where the capsule disappears, its contour can be arbitrarily created with a continuing contour line based on the natural shape of the prostate. We utilize an algorithm based on a least squares curve fitting technique that uses a prostate shape equation to merge previously detected capsule parts with the shape equation to produce an approximated curve that represents the prostate capsule. Results We have tested our algorithm using three different shapes on 13 histologic prostate slices that are cut at different locations from the apex. The best result shows a 90% average contour match when compared to pathologist-drawn contours. ConclusionWe believe that automatically identifying histologic prostate contours will lead to increased objective analyses of surgical margins and extracapsular spread of cancer. Our results show that this is achievable.

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<title>Quantitative three-dimensional transrectal ultrasound (TRUS) for prostate imaging</title>

Cited by 21 publications

References 0 publications

Boundary delineation for hepatic hemangioma in ultrasound images

Boundary delineation for hepatic hemangioma in ultrasound images

Prostate segmentation in ultrasound images with deformable shape priors

Identifying ambiguous prostate gland contours from histology using capsule shape information and least squares curve fitting

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