Energy functions for the segmentation of ultrasound volume data using active rays

Martín, María del Carmen; Rodríguez, Enrique García; Tejada, D.; Alberola, C.; Ruiz, J. A.

doi:10.1109/icassp.2000.859293

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…Two previous approaches to our model have already been published. In [10] a sequence of echographies from an in vitro fetus were segmented using active contours with several new energy terms. The optimization approach was based on the relaxation labeling method [8] which provides suboptimal solutions.…”

Section: State Of the Artmentioning

confidence: 99%

A Bayesian Approach to in vivo Kidney Ultrasound Contour Detection Using Markov Random Fields

Martín

Alberola

2002

Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002

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Automatic detection of structures in medical images is of great importance for the implementation of tools that can obtain accurate measurements for an eventual diagnosis. In this paper, a new method for the creation of such tools is presented. We focus on in vivo kidney ultrasound, a target in which classical methods fail due to the inherent difficulty of such an imaging modality and organ. The proposed method operates on every slice by detecting kidney contours under a probabilistic Bayesian framework. We make use of Markov Random Fields ideas to model the problem and find the solution. A computer easy-to-use interface to the model is also presented.

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Section: State Of the Artmentioning

confidence: 99%

A Bayesian Approach to in vivo Kidney Ultrasound Contour Detection Using Markov Random Fields

Martín

Alberola

2002

Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002

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“…energy function selection an issue [14]; we are currently porting this module into the diSNei platform.…”

Section: Image Segmentation With Disneimentioning

confidence: 99%

diSNei: A Collaborative Environment for Medical Images Analysis and Visualization

Alberola

Cárdenes

Martín

et al. 2000

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2000

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Abstract. In this paper we describe our environment diSNei, a graphical tool for collaborative image analysis and visualization of models created out of slices of volume data; this application allows a number of users to simultaneous and coordinatedly analyze medical images, create graphical models, navigate through them and superimpose raw data onto the models. The application is intended to help physicians interpret data in the case that ambiguous situations may appear, by means of collaboration with other colleagues. It is therefore an integrated environment for expertise interchange among physicians and we believe that it is a powerful tool for academic purposes as well. Other outstanding application features are its being multiplatform, and, particularly, the fact that it can run on NT computers, and the support for stereo rendering so as to obtain a deep sensation of inmersion into the models.

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RD-Based Seeded Region Growing for Extraction of Breast Tumor in an Ultrasound Volume

Kwak

Kim

2005

Computational Intelligence and Security

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Energy functions for the segmentation of ultrasound volume data using active rays

Cited by 5 publications

References 7 publications

A Bayesian Approach to in vivo Kidney Ultrasound Contour Detection Using Markov Random Fields

A Bayesian Approach to in vivo Kidney Ultrasound Contour Detection Using Markov Random Fields

diSNei: A Collaborative Environment for Medical Images Analysis and Visualization

RD-Based Seeded Region Growing for Extraction of Breast Tumor in an Ultrasound Volume

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