Curvature-Based Active Region Segmentation for Improved Image Processing of Aspergillus Species

Nur Rodiatul Raudah Mohamed Radzuan, Haryati Jaafar, Farah Nabilah Zabani, Fatin Norazima Mohamad Ariff, Fatin Nadia Azman Fauzi
{"title":"Curvature-Based Active Region Segmentation for Improved Image Processing of Aspergillus Species","authors":"Nur Rodiatul Raudah Mohamed Radzuan, Haryati Jaafar, Farah Nabilah Zabani, Fatin Norazima Mohamad Ariff, Fatin Nadia Azman Fauzi","doi":"10.37934/araset.46.1.157174","DOIUrl":null,"url":null,"abstract":"Aspergillus is one of the most ubiquitous of the airborne saprophytic fungi that can withstand various climatic conditions and could cause multiple type of illness. It can be beneficial to humankind and also can be infectious to humans and animals. Direct microscopic is used by trained microscopist as one of the alternatives in identification process to any specimen that suspected of having fungal infection. Confirmation towards identification is often necessary as the structure of Aspergillus is complex and dissimilar in each cycle. In addition, the structure of some species of Aspergillus are the almost same, which can be incorrectly recognized. In prevention of misidentification, computer-based Aspergillus species identification is proposed. The detection process is the earliest and important process hence, this paper proposed an active region-based segmentation method in order to detect the presence of fungi. This method is literally not depending on the gradient or sharp edges of the object and implementing level set function for curve evolution which able to reduce the computational cost. Originally, this function was developed for tracking fluid interfaces but in this study, this function has been applied to fungi database. Two different methods were tested and compared to observe their ability to segment different 80 of Aspergillus images which included four species. Experiments conducted have been compared with the baseline technique and the proposed method is outperformed in terms of accuracy, specificity with average of 90% and PSNR value of greater than 40dB. Meanwhile the active contour (snake) was slightly underperformed but well performed particularly in terms of sensitivity with greater than 80% for all the species. Moreover, upon scrutinizing the dice coefficients provided in both tables, it becomes apparent that there is a lack of significant variance in the values, except in the instance of Aspergillus fumigatus (active region-based) that which produces a result below 36%.","PeriodicalId":506443,"journal":{"name":"Journal of Advanced Research in Applied Sciences and Engineering Technology","volume":"1 11","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advanced Research in Applied Sciences and Engineering Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37934/araset.46.1.157174","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Aspergillus is one of the most ubiquitous of the airborne saprophytic fungi that can withstand various climatic conditions and could cause multiple type of illness. It can be beneficial to humankind and also can be infectious to humans and animals. Direct microscopic is used by trained microscopist as one of the alternatives in identification process to any specimen that suspected of having fungal infection. Confirmation towards identification is often necessary as the structure of Aspergillus is complex and dissimilar in each cycle. In addition, the structure of some species of Aspergillus are the almost same, which can be incorrectly recognized. In prevention of misidentification, computer-based Aspergillus species identification is proposed. The detection process is the earliest and important process hence, this paper proposed an active region-based segmentation method in order to detect the presence of fungi. This method is literally not depending on the gradient or sharp edges of the object and implementing level set function for curve evolution which able to reduce the computational cost. Originally, this function was developed for tracking fluid interfaces but in this study, this function has been applied to fungi database. Two different methods were tested and compared to observe their ability to segment different 80 of Aspergillus images which included four species. Experiments conducted have been compared with the baseline technique and the proposed method is outperformed in terms of accuracy, specificity with average of 90% and PSNR value of greater than 40dB. Meanwhile the active contour (snake) was slightly underperformed but well performed particularly in terms of sensitivity with greater than 80% for all the species. Moreover, upon scrutinizing the dice coefficients provided in both tables, it becomes apparent that there is a lack of significant variance in the values, except in the instance of Aspergillus fumigatus (active region-based) that which produces a result below 36%.
基于曲率的主动区域分割改进曲霉菌种的图像处理
曲霉菌是空气中最普遍的吸附性真菌之一,它能抵御各种气候条件,并能引起多种疾病。它可以对人类有益,也可以传染给人类和动物。训练有素的显微镜专家在鉴定任何疑似真菌感染的标本时,都会使用直接显微镜。由于曲霉菌的结构复杂,每个周期的结构都不相同,因此通常需要进行鉴定确认。此外,某些曲霉菌种的结构几乎相同,也可能被误认。为防止错误识别,提出了基于计算机的曲霉菌种识别方法。检测过程是最早也是最重要的过程,因此本文提出了一种基于主动区域分割的方法来检测真菌的存在。这种方法不依赖于物体的梯度或尖锐边缘,并采用了用于曲线演化的水平集函数,从而降低了计算成本。该函数最初是为追踪流体界面而开发的,但在本研究中,该函数被应用于真菌数据库。对两种不同的方法进行了测试和比较,以观察它们分割不同的 80 种曲霉图像(包括四种)的能力。实验结果与基线技术进行了比较,发现所提出的方法在准确性、特异性(平均为 90%)和 PSNR 值(大于 40dB)方面均优于基线技术。与此同时,主动轮廓(蛇形)的表现略逊一筹,但在灵敏度方面表现出色,对所有物种的灵敏度都超过了 80%。此外,仔细观察两个表格中提供的骰子系数,可以明显看出,除了曲霉菌(基于活性区域)的结果低于 36% 之外,其他数值都没有明显差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
1.30
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信