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A decision fusion strategy for polyp detection in capsule endoscopy.
Zhao, Qian; Dassopoulos, Themistocles; Mullin, Gerard E; Meng, Max Q-H; Kumar, Rajesh.
Afiliación
  • Zhao Q; Department of Electronic Engineering, The Chinese University of Hong Kong. qzhao@ee.cuhk.edu.hk
Stud Health Technol Inform ; 173: 559-65, 2012.
Article en En | MEDLINE | ID: mdl-22357058
Wireless capsule endoscopy (CE) is now routinely used for non-invasive diagnosis of small bowel diseases. But, it still requires manual assessment of the approximately 50,000 study images. Literature has recently investigated automated methods to detect and analyze various anomalies in CE images to improve reading efficiency and reduce variability. We propose such a computer aided diagnosis (CAD) approach to detect small bowel polyps. For supervised classification of polyps, we investigated fusing multiple statistical classifiers based on color, texture and edge features. The combined boosted classifier when evaluated using 1200 CE images outperformed all individual classifiers and achieved a ~90% classification accuracy.
Asunto(s)
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Bases de datos: MEDLINE Asunto principal: Pólipos / Diagnóstico por Computador / Endoscopía Capsular Tipo de estudio: Diagnostic_studies / Guideline Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2012 Tipo del documento: Article
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Bases de datos: MEDLINE Asunto principal: Pólipos / Diagnóstico por Computador / Endoscopía Capsular Tipo de estudio: Diagnostic_studies / Guideline Límite: Humans Idioma: En Revista: Stud Health Technol Inform Asunto de la revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Año: 2012 Tipo del documento: Article