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Endoscopic images do not contain sharp edges to segment using the traditional segmentation methods for obtaining edges. Therefore, the active contours or ‘snakes ’ using level set method with the energy minimization algorithm is adopted here to segment these images. The results obtained from the above segmentation process will be number of segmented regions. The boundary of each region is considered as a curve for further processing. The curvature for each point of this curve is computed considering the support region of each point. The possible presence of abnormality is identified, when curvature of the contour segment between two zero crossings has the opposite curvature signs to those of such neighboring contour segments on the same edge contours. The Knearest neighbor classifier is used to classify the images as normal or abnormal. The experiment based on the proposed method is carried out on 50 normal and 50 abnormal endoscopic images and the results are encouraging.

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Autor(es)

B. V. Dhandra -  Ravindra Hegadi - 

Id.: 48050719

Idioma: inglés  - 

Versión: 1.0

Estado: Final

Tipo:  application/pdf - 

Palabras claveActive Contours - 

Tipo de recurso: Texto Narrativo  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

: Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Formatos:  application/pdf - 

Requerimientos técnicos:  Browser: Any - 

Relación: [IsBasedOn] http://ijcss.org/Volume1/Issue1/V1_I1_019.pdf
[References] 10.1.1.46.1266
[References] 10.1.1.2.1828
[References] 10.1.1.18.3870

Fecha de contribución: 25-sep-2009

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