Collagen morphology and texture analysis : from statistics to classification

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DOIResolve DOI: http://doi.org/10.1038/srep02190
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TypeArticle
Journal titleScientific Reports
ISSN2045-2322
Volume3
Article number2190
Pages# of pages: 10
SubjectSHG, collagen, classification, texture, gray level co-occurance matrix, multiphoton microscopy
AbstractIn this study we present an image analysis methodology capable of quantifying morphological changes in tissue collagen fibril organization caused by pathological conditions. Texture analysis based on first-order statistics (FOS) and second-order statistics such as gray level co-occurrence matrix (GLCM) was explored to extract second-harmonic generation (SHG) image features that are associated with the structural and biochemical changes of tissue collagen networks. Based on these extracted quantitative parameters, multi-group classification of SHG images was performed. With combined FOS and GLCM texture values, we achieved reliable classification of SHG collagen images acquired from atherosclerosis arteries with >90% accuracy, sensitivity and specificity. The proposed methodology can be applied to a wide range of conditions involving collagen re-modeling, such as in skin disorders, different types of fibrosis and muscular-skeletal diseases affecting ligaments and cartilage.
Publication date
PublisherNature Publishing Group
LanguageEnglish
AffiliationNational Research Council Canada; Medical Devices
Peer reviewedYes
NPARC number21268602
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Record identifier58e777b9-73a7-484e-918d-49d5e07f0b9b
Record created2013-10-25
Record modified2017-03-23
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