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Communication Dans Un Congrès Année : 2012

Embeddable Automatic Polyp Detection for Videoendoscopy and Wireless Videoendoscopy Images Analysis

Juan Silva
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ICI
Aymeric Histace
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ICI
Olivier Romain
Xavier Dray
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Bertrand Granado

Résumé

This article presents a new embeddable method for polyp detection in endoscopic video images and wireless capsule endoscopic images based on physician approach. This approach is twofold: the first step consists in a geometric approach to characterize the polyp's geometric features as size or shape. The second, a texture approach based on a learning process of texture features of polyps using the use of the co-occurrence matrix and their related statistics. For classification, we propose the boosting method which allows us to generate a strong classifier and also the possibility of permanent learning. The performance of the learning approach over a database of 300 images, is characterized by a sensibility of 90,6%, a specificity of 95,0% and a false detection rate of 4,8%. All the different steps of the algorithm were meticulously chosen to facilitate the future hardware implementation of the treatment on the capsule for wireless endoscopy.
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Dates et versions

hal-00765648 , version 1 (15-12-2012)

Identifiants

  • HAL Id : hal-00765648 , version 1

Citer

Juan Silva, Aymeric Histace, Olivier Romain, Xavier Dray, Bertrand Granado. Embeddable Automatic Polyp Detection for Videoendoscopy and Wireless Videoendoscopy Images Analysis. 27th Conference on Design of Circuits and Integrated Systems (DCIS), Special Session on Biomedical Systems, Nov 2012, Avignon, France. pp.177. ⟨hal-00765648⟩
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