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Journal ArticleDOI

Outlining of the prostate using snakes with shape restrictions based on the wavelet transform (Doctoral Thesis: Dissertation)

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TLDR
A new technique for elastic deformation restriction to particular object shapes of any closed planar curve using localized multiscale contour parameterization based on the 1D dyadic wavelet transform is proposed.
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This article is published in Pattern Recognition.The article was published on 1999-10-01. It has received 90 citations till now. The article focuses on the topics: Wavelet transform & Template matching.

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Journal ArticleDOI

Ultrasound image segmentation: a survey

TL;DR: This paper reviews ultrasound segmentation methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images, and presents a classification of methodology in terms of use of prior information.
Journal ArticleDOI

Segmentation of prostate boundaries from ultrasound images using statistical shape model

TL;DR: A hierarchical deformation strategy is employed, in which the model adaptively focuses on the similarity of different Gabor features at different deformation stages using a multiresolution technique, i.e., coarse features first and fine features later.
Journal ArticleDOI

Deformable segmentation of 3-D ultrasound prostate images using statistical texture matching method

TL;DR: Experimental results by using both synthesized and real data show the good performance of the proposed model in segmenting prostates from ultrasound images.
Journal ArticleDOI

A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images

TL;DR: A new taxonomy for prostate segmentation strategies is defined that allows first to group the algorithms and then to point out the main advantages and drawbacks of each strategy, and a discussion on choosing the most appropriate segmentation strategy for a given imaging modality is provided.
Journal ArticleDOI

Parametric shape modeling using deformable superellipses for prostate segmentation

TL;DR: An efficient and robust Bayesian segmentation algorithm that was applied to 125 prostate ultrasound images collected from 16 patients and was shown to be fairly insensitive to the choice of the initial curve.
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