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Open AccessJournal ArticleDOI

Deformable organisms for automatic medical image analysis.

TLDR
A new approach to medical image analysis that combines deformable model methodologies with concepts from the field of artificial life is introduced, including a "corpus callosum worm" that can overcome noise, incomplete edges, considerable anatomical variation, and interference from collateral structures to segment and label the corpusCallosum in 2D mid-sagittal MR brain images.
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This article is published in Medical Image Analysis.The article was published on 2002-09-01 and is currently open access. It has received 106 citations till now.

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Citations
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Journal ArticleDOI

Use of maximum intensity projections (MIP) for target volume generation in 4DCT scans for lung cancer

TL;DR: MIPs are a reliable clinical tool for generating ITVs from 4DCT data sets, thereby permitting rapid assessment of mobility for both gated and nongated 4D radiotherapy in lung cancer.
Journal ArticleDOI

Watershed segmentation using prior shape and appearance knowledge

TL;DR: This work proposes a novel method for enhancing watershed segmentation by utilizing prior shape and appearance knowledge, which iteratively aligns a shape histogram with the result of an improved k-means clustering algorithm of the watershed segments.
Proceedings ArticleDOI

Approximation with active B-spline curves and surfaces

TL;DR: An active contour model for parametric curve and surface approximation is presented and it is indicated how the latter topic leads to the variational design of smooth motions which interpolate or approximate given positions.
Journal ArticleDOI

Characterization of the corpus callosum in very preterm and full-term infants utilizing MRI

TL;DR: This study characterizes callosal size, shape and diffusion in typically developing infants at term equivalent age, and reports macrostructural and microstructural abnormalities as a result of prematurity.
Journal ArticleDOI

Automatic segmentation of thoracic and pelvic CT images for radiotherapy planning using implicit anatomic knowledge and organ-specific segmentation strategies.

TL;DR: The design, algorithms and validation of new software for the automatic segmentation of CT images used for radiotherapy treatment planning, and the results indicate that the accuracy of the algorithms is within the bandwidth of manual segmentation by experts, except for specific erroneous situations.
References
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Journal ArticleDOI

Snakes : Active Contour Models

TL;DR: This work uses snakes for interactive interpretation, in which user-imposed constraint forces guide the snake near features of interest, and uses scale-space continuation to enlarge the capture region surrounding a feature.
Journal ArticleDOI

Active shape models—their training and application

TL;DR: This work describes a method for building models by learning patterns of variability from a training set of correctly annotated images that can be used for image search in an iterative refinement algorithm analogous to that employed by Active Contour Models (Snakes).
Journal ArticleDOI

Medical image analysis: progress over two decades and the challenges ahead

TL;DR: A look at progress in the field over the last 20 years is looked at and some of the challenges that remain for the years to come are suggested.
Journal ArticleDOI

Deformable models in medical image analysis: a survey

TL;DR: The rapidly expanding body of work on the development and application of deformable models to problems of fundamental importance in medical image analysis, including segmentation, shape representation, matching and motion tracking is reviewed.
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