Automated Detection of Collagen Bundles in Second Harmonic Generation Microscopy Images

Abstract

Collagen is one of the most abundant proteins in the body. It is essential for the structure, functionality, and strength of the connective tissue such as skin, bone, tendon, and cornea. It is known that a change in the arrangement or morphology of these fibrillar structures relates to multiple dysfunctions including corneal diseases and various cancer types. Due to their critical roles in wide-range abnormalities, there is an increasing interest in the pattern analysis of collagen arrangements. In recent years, Second Harmonic Generation (SHG) microscopy is proven to be an efficient imaging modality for visualizing unstained collagen fibrils. There are plenty of studies in the literature on the analysis of collagen distribution in SHG images. However, the majority of these methods are limited to detecting simple, statistical and non-local properties such as pixel intensity and orientation variance. There is a need for a method to detect the local structural properties of collagen bundles. This paper is to introduce an automated method to detect collagen bundles in 3-dimensional SHG microscopy images. The origin of the proposed method is based on multiscale directional representation systems. The proposed method detects the collagen bundles by measuring the dominant orientation of local regions and an orientation-based connected component analysis. Through more local analysis and the detection of collagen bundles separately, the proposed method would lead to the extraction of more detailed structural information on collagen bundle distribution.

Keywords

• collagen
• machine learning
• image analysis
• collagen detection
• cornea analysis
• SHG

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