Abstract:
:In 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.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Mostaço-Guidolin LB,Ko AC,Wang F,Xiang B,Hewko M,Tian G,Major A,Shiomi M,Sowa MGdoi
10.1038/srep02190subject
Has Abstractpub_date
2013-01-01 00:00:00pages
2190issn
2045-2322pii
srep02190journal_volume
3pub_type
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