Dental tissue classification using computational intelligence and digital image analysis

Learning techniques have shown high efficacy rates when applied to similar clinical problems (Jabarouti et al. 2011; Mazurowski et al. 2008; Nassar et al. 2007; Said et al. 2006; Lisboa 2002). Recent studies over biological tissue classification or similar applications subjects achieve tissue recogn...

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Veröffentlicht in:Biodental Engineering III S. 237 - 242
Format: Buchkapitel
Sprache:Englisch
Veröffentlicht: United Kingdom CRC Press 2014
Taylor & Francis Group
Schlagworte:
Biomedical engineering
Dentistry / oral & maxillofacial medicine
Specific Tissue Type
Shift Algorithm
Clinical Dentists
Sclerotic Dentin
Data Set
Dental Tissue
Computational Intelligence Techniques
Kappa Statistics Values
RGB
Dental Pieces
Execution Time
Detailed Visual Inspection
Segmented Region
Tissue Classification
Scaled Conjugate Gradient Algorithm
Computational Intelligence
Oral Mucosa
AOC
Digital Image Analysis
Dentin Caries
Supervised Machine Learning
Enamel Caries
Professional Dentists
Dental Photography
ISBN:9781138026711, 1138026719
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Learning techniques have shown high efficacy rates when applied to similar clinical problems (Jabarouti et al. 2011; Mazurowski et al. 2008; Nassar et al. 2007; Said et al. 2006; Lisboa 2002). Recent studies over biological tissue classification or similar applications subjects achieve tissue recognition using different segmentation approaches such as mean shift clustering, region growing, watersheds, and histogram thresholds (Koutsouri et al. 2013; Anuradha et al. 2012; Kang et al. 2010; Perez et al. 2001; Stelt et al. 1985). Furthermore, texture analysis can be used to separate different significant regions in digital images of human tissues or organs (Veredas et al. 2010).
ISBN:9781138026711
1138026719
DOI:10.1201/b17071-44