Blood Vessel Extraction Using Combination of Kirsch’s Templates and Fuzzy C-Means (FCM) on Retinal Images

Mustafa, Wan Azani; Mahmud, Ahmad Syauqi; Khairunizam, Wan; Razlan, Z. M.; Shahriman, A. B.; Zunaidi, I.

doi:10.1088/1757-899x/557/1/012009

Cited by 7 publications

(1 citation statement)

References 13 publications

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“…Enfield et al [ 28 ] presented capillary extraction from volumes of in vivo OCT images of human volar forearm using correlation mapping optical coherence tomography (cmOCT) techniques. Currently, benefitting from recent advances in deep learning techniques in medical image preprocessing, capillary segmentation via supervised deep learning has become a hot topic for many researchers [ 29 , 30 ], who are using it for automatic and accurate segmentation. Prior to training a deep learning model, a large, representative, and high-quality labeled dataset [ 31 ] (the training data) with the ground truth is required to obtain state-of-the-art performance.…”

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confidence: 99%

Generation of Augmented Capillary Network Optical Coherence Tomography Image Data of Human Skin for Deep Learning and Capillary Segmentation

et al. 2021

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The segmentation of capillaries in human skin in full-field optical coherence tomography (FF-OCT) images plays a vital role in clinical applications. Recent advances in deep learning techniques have demonstrated a state-of-the-art level of accuracy for the task of automatic medical image segmentation. However, a gigantic amount of annotated data is required for the successful training of deep learning models, which demands a great deal of effort and is costly. To overcome this fundamental problem, an automatic simulation algorithm to generate OCT-like skin image data with augmented capillary networks (ACNs) in a three-dimensional volume (which we called the ACN data) is presented. This algorithm simultaneously acquires augmented FF-OCT and corresponding ground truth images of capillary structures, in which potential functions are introduced to conduct the capillary pathways, and the two-dimensional Gaussian function is utilized to mimic the brightness reflected by capillary blood flow seen in real OCT data. To assess the quality of the ACN data, a U-Net deep learning model was trained by the ACN data and then tested on real in vivo FF-OCT human skin images for capillary segmentation. With properly designed data binarization for predicted image frames, the testing result of real FF-OCT data with respect to the ground truth achieved high scores in performance metrics. This demonstrates that the proposed algorithm is capable of generating ACN data that can imitate real FF-OCT skin images of capillary networks for use in research and deep learning, and that the model for capillary segmentation could be of wide benefit in clinical and biomedical applications.

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