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Proceedings Paper

Texture descriptor approaches to level set segmentation in medical images
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Paper Abstract

Medical image analysis has become an important tool for improving medical diagnosis and planning treatments. It involves volume or still image segmentation that plays a critical role in understanding image content by facilitating extraction of the anatomical organ or region-of-interest. It also may help towards the construction of reliable computer-aided diagnosis systems. Specifically, level set methods have emerged as a general framework for image segmentation; such methods are mainly based on gradient information and provide satisfactory results. However, the noise inherent to images and the lack of contrast information between adjacent regions hamper the performance of the algorithms, thus, others proposals have been suggested in the literature. For instance, characterization of regions as statistical parametric models to handle level set evolution. In this paper, we study the influence of texture on a level-set-based segmentation and propose the use of Hermite features that are incorporated into the level set model to improve organ segmentation that may be useful for quantifying left ventricular blood flow. The proposal was also compared against other texture descriptors such as local binary patterns, Image derivatives, and Hounsfield low attenuation values.

Paper Details

Date Published: 15 May 2014
PDF: 12 pages
Proc. SPIE 9138, Optics, Photonics, and Digital Technologies for Multimedia Applications III, 91380J (15 May 2014); doi: 10.1117/12.2054527
Show Author Affiliations
Jimena Olveres, Univ. Nacional Autónoma de México (Mexico)
Rodrigo Nava, Univ. Nacional Autónoma de México (Mexico)
Ernesto Moya-Albor, Univ. Panamericana (Mexico)
Boris Escalante-Ramírez, Univ. Nacional Autónoma de México (Mexico)
Jorge Brieva, Univ. Panamericana (Mexico)
Gabriel Cristóbal, Consejo Superior de Investigaciones Científicas (Spain)
Enrique Vallejo, Hospital Ángeles Pedregal (Mexico)

Published in SPIE Proceedings Vol. 9138:
Optics, Photonics, and Digital Technologies for Multimedia Applications III
Peter Schelkens; Touradj Ebrahimi; Gabriel Cristóbal; Frédéric Truchetet; Pasi Saarikko, Editor(s)

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