Density analysis of collagen fibers based on enhanced frangi filter in Second Harmonic Generation virtual biopsy images

Cai, Cheng Syun; Chen, Chun Fu; Lee, Gwo Giun Chris; Lin, Guan Liang; Chou, Sin Yo; Tsai, Ming Rung; Liao, Yi‐Hua; Sun, Chi‐Kuang

doi:10.1109/chinasip.2014.6889286

Cited by 1 publication

(2 citation statements)

References 16 publications

(15 reference statements)

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“…For this matter, it is useful to apply a CLAHE on the SHG images. Cai et al (2014) enhanced the dermal layer of human skin SHG images using the CLAHE algorithm. Then, they applied the Frangi filter and a segmentation using Otsu's tresholding in order to capture a representation of both the fibers and the holes in the images.…”

Section: Scale Of Measurementioning

confidence: 99%

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Quantitative analysis of second harmonic generated images of collagen fibers: a review

et al. 2022

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Purpose The human body is a complex structure. Its strength is ensured by the collagen protein which exists under the form of fibers. The quantitative analysis of these fibers in biological tissues can be very interesting to establish a relationship between the microstructure and their functions. This analysis is usually performed using two-photon microscopy and second harmonic generated (SHG) images. Lately, more and more researchers focused on the use of SHG images since it is a non-invasive technique and allows the capture of collagen fibers only. Many image-processing techniques can be used to extract quantitative information from those images such as fiber orientations, dimensions, and density. Therefore, accurate measure extraction depends mainly on the used image processing methods and, thus, it is necessary to know what processing technique to use. Methods The main purpose of this article is to exhibit the most used techniques in collagen fiber quantitative analysis then categorize them according to the information to extract. A comparison of three most used methods in fiber orientation’s estimation is carried out. Result and conclusion Despite the considerable number of papers aiming to quantitatively analyze collagen fibers from SHG images, two main aspects were not deeply covered. First, the use of deep learning algorithms is still limited even for segmentation and denoizing applications. Second, most of the studies processed in this review focused on two-dimensional SHG images and did not take into consideration collagen fibers as a three-dimensional volume.

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Section: Scale Of Measurementioning

confidence: 99%

“…It depends on how the segmentation is performed (in 2D or 3D). Cai et al (2014) focused on 2D virtual biopsy images and not stacks. Therefore, they tested their approach only on single 2D images.…”

Section: Input Data Nature (2d/projected 3d/3d)mentioning

confidence: 99%