Decorrelation stretch for enhancing colour fundus photographs affected by cataracts

IF 1.3 Q4 ENGINEERING, BIOMEDICAL

Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization Pub Date : 2023-11-02 DOI:10.1080/21681163.2023.2274948

Preecha Vonghirandecha, Supaporn Kansomkeat, Patama Bhurayanontachai, Pannipa Sae-Ueng, Sathit Intajag

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引用次数: 0

Abstract

ABSTRACTA method of enhancing colour fundus photographs is proposed to reduce the effect of cataracts. The enhancement method employs a decorrelation stretch (DS) technique in an LCC colour model. The initial designed technique embeds Hubbard’s colouration model into DS parameters to produce enhanced results in a standard form of age-related macular degeneration (AMD) reading centres. The colouration model could modify to enhance the colour of lesions observed in diabetic retinopathy (DR). The proposed algorithm could improve the effect of cataracts on fundus images and provided good results when the density of the cataract was less than grade 2. In the case of images taken through cataracts higher than or equal to grade 2, some output results could become unusable when the cataract was in line with the macula.KEYWORDS: Decorrelation stretchretinal image enhancementcataract Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research has received funding support from the NSRF via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation [grant number B04G640070].Notes on contributorsPreecha VonghirandechaPreecha Vonghirandecha is an assistant professor at the Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla, Thailand. His current research interests include data Science, image processing and artificial intelligence applied to medical image analysis. He received a PhD in computer engineering from Prince of Songkla University, Thailand, in 2019.Supaporn KansomkeatSupaporn Kansomkeat is an assistant professor at the Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla, Thailand. Her current research interests include software testing, test process improvement and artificial intelligence applied to medical image analysis. She received a PhD in computer engineering from Chulalongkorn University, Thailand, in 2007.Patama BhurayanontachaiPatama Bhurayanontachai (MD.) is an Associate Professor at the Department of Ophthalmology, Prince of Songkla University, Songkhla, Thailand. She received a certificate in Clinical Fellowship in vitreoretinal surgery from Flinders Medical Centre, Australia, in 2005. Her current research interests involve medical retina, surgical retina, and artificial intelligence applied to clinical diagnosis.Pannipa Sae-UengPannipa Sae-Ueng is a lecturer at the Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla, Thailand. She received her Ph.D. in Computer Science in 2022 at the Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Novi Sad, Serbia. Recently she has focused on research topics in data science and artificial intelligence.Sathit IntajagSathit Intajag received the M. Eng. and D. Eng. Degree in electrical engineering from the King Mongkut’s Institute of Technology Ladkrabang (KMITL), Thailand, in 1998 and 2005, respectively. He is an associate professor at the Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla, Thailand. His research interests include signal processing, statistical analysis, and artificial intelligence.

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去相关拉伸法增强白内障眼底彩色照片

摘要:提出了一种增强眼底彩色照片的方法，以减少白内障的影响。增强方法在LCC颜色模型中采用去相关拉伸(DS)技术。最初设计的技术将Hubbard的着色模型嵌入到DS参数中，以在标准形式的年龄相关性黄斑变性(AMD)阅读中心中产生增强的结果。在糖尿病视网膜病变(DR)中，该着色模型可以修饰以增强病变的颜色。该算法可以改善白内障对眼底图像的影响，在白内障密度小于2级的情况下具有较好的效果。如果通过高于或等于2级的白内障拍摄图像，当白内障与黄斑一致时，一些输出结果可能无法使用。关键词:去相关拉伸视网膜图像增强白内障披露声明作者未报告潜在利益冲突。本研究得到了国家自然科学基金人力资源与机构发展、研究与创新项目管理部门的资助[批准号B04G640070]。作者简介:preecha Vonghirandecha是泰国宋卡王子大学理学院计算科学系的助理教授。他目前的研究兴趣包括数据科学、图像处理和人工智能在医学图像分析中的应用。2019年获泰国宋卡王子大学计算机工程博士学位。Supaporn KansomkeatSupaporn Kansomkeat是泰国宋卡王子大学理学院计算科学系助理教授。她目前的研究兴趣包括软件测试，测试过程改进和应用于医学图像分析的人工智能。她于2007年获得泰国朱拉隆功大学计算机工程博士学位。Patama Bhurayanontachai(医学博士)是泰国宋卡王子大学眼科系副教授。2005年，她获得了澳大利亚弗林德斯医学中心颁发的玻璃体视网膜外科临床研究员证书。她目前的研究兴趣包括医学视网膜、外科视网膜和应用于临床诊断的人工智能。Pannipa Sae-UengPannipa Sae-Ueng是泰国宋卡王子大学理学院计算科学系的讲师。她于2022年在塞尔维亚诺维萨德大学理学院数学与信息系获得计算机科学博士学位。最近，她专注于数据科学和人工智能的研究课题。Sathit IntajagSathit Intajag获得了硕士学位。和工程博士。1998年和2005年分别获得泰国Ladkrabang国王蒙库特理工学院(KMITL)电气工程学位。他是泰国宋卡王子大学理学院计算科学系副教授。主要研究方向为信号处理、统计分析、人工智能等。

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来源期刊

Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization ENGINEERING, BIOMEDICAL-

CiteScore

2.80

自引率

6.20%

发文量

102

期刊介绍： Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization is an international journal whose main goals are to promote solutions of excellence for both imaging and visualization of biomedical data, and establish links among researchers, clinicians, the medical technology sector and end-users. The journal provides a comprehensive forum for discussion of the current state-of-the-art in the scientific fields related to imaging and visualization, including, but not limited to: Applications of Imaging and Visualization Computational Bio- imaging and Visualization Computer Aided Diagnosis, Surgery, Therapy and Treatment Data Processing and Analysis Devices for Imaging and Visualization Grid and High Performance Computing for Imaging and Visualization Human Perception in Imaging and Visualization Image Processing and Analysis Image-based Geometric Modelling Imaging and Visualization in Biomechanics Imaging and Visualization in Biomedical Engineering Medical Clinics Medical Imaging and Visualization Multi-modal Imaging and Visualization Multiscale Imaging and Visualization Scientific Visualization Software Development for Imaging and Visualization Telemedicine Systems and Applications Virtual Reality Visual Data Mining and Knowledge Discovery.