Non-invasive Dry Eye detection using Fuzzy c-means Clustering Algorithm

IF 1.3 4区 医学 Q4 MEDICINE, RESEARCH & EXPERIMENTAL Biomedical Research-tokyo Pub Date : 2021-01-01 DOI:10.35841/0970-938X.S1-S5
I.Ch, ra, N.Prabhakaran, V.Prabhu, S.Harshavardhan, N.Duraichi
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Abstract

The background includes the study area of the dry eye taken from 75 patients to analyze eye-related disease. This method suggested as earlier detection of eye detection and tries to diagnose the problem. Early detection of eye problem helps the patient to have a clear line of sight distance through fuzzy c means clustering algorithm.The tear film must be detected at an early stage to protect the person from death. The tear film should be a monitor at the initial stage and diagnose by the unique technique, whether it may be the invasive or non-invasive method of dry detection. The invasive approach is the time-consuming process, and many disadvantages in this method arise in tear film detection. The non- invasive technique is not said to be a slow process. So the author proposed the novel approach of tear film detection by fuzzy c means clustering algorithm. The sample of eye images are taken and processed with fuzzy c means clustering algorithm and finds the intensity in both the eye. The accuracy analysis carried by fuzzy c indicates a clustering algorithm of 82% in its efficiency.
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基于模糊c均值聚类算法的无创干眼检测
背景包括75例干眼患者的干眼研究区域,以分析眼相关疾病。这种方法建议早期检测眼睛,并试图诊断问题。通过模糊c均值聚类算法,早期发现眼部问题,帮助患者获得清晰的视线距离。必须在早期阶段检测到泪膜,以保护人免于死亡。泪膜应作为初期的监测,通过独特的技术进行诊断,无论是有创还是无创的干法检测。有创入路是一个耗时的过程,并且在泪膜检测中出现了许多缺点。这种非侵入性技术并不是一个缓慢的过程。为此,作者提出了一种基于模糊c均值聚类算法的泪膜检测新方法。采用模糊c均值聚类算法对眼睛图像样本进行处理,得到两眼的灰度值。模糊c进行的准确率分析表明,聚类算法的效率为82%。
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来源期刊
Biomedical Research-tokyo
Biomedical Research-tokyo 医学-医学:研究与实验
CiteScore
2.40
自引率
0.00%
发文量
19
审稿时长
>12 weeks
期刊介绍: Biomedical Research is peer-reviewed International Research Journal . It was first launched in 1990 as a biannual English Journal and later became triannual. From 2008 it is published in Jan-Apr/ May-Aug/ Sep-Dec..
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