Morphological Segmentation Analysis and Texture-based Support Vector Machines Classification on Mice Liver Fibrosis Microscopic Images

doi:10.2174/1574893614666190304125221

科研成果详情

题名	Morphological Segmentation Analysis and Texture-based Support Vector Machines Classification on Mice Liver Fibrosis Microscopic Images
作者	Wang, Yu 1; Shi, Fuqian1; Cao, Luying 1; Dey, Nilanjan 2; Wu, Qun 3; Ashour, Amira Salah 4; Sherratt, Robert Simon 5; Rajinikanth, Venkatesan 6; Wu, Lijun7
发表日期	2019
发表期刊	CURRENT BIOINFORMATICS 影响因子和分区
语种	英语
原始文献类型	Article
关键词	Morphological segmentation top-hat transformation threshold based Watershed segmentation texture feature extraction mice liver fibrosis microscopic images support vector machine
其他关键词	ELECTRON-MICROSCOPY ; WATERSHED ALGORITHM ; FEATURES ; LIGHT
摘要	Background: To reduce the intensity of the work of doctors, pre-classification work needs to he issued. In this paper, a novel and related liver microscopic image classification analysis method is proposed. Objective: For quantitative analysis, segmentation is carried out to extract the quantitative information of special organisms in the image for further diagnosis, lesion localization, learning and treating anatomical abnormalities and computer-guided surgery. Methods: In the current work, entropy-based features of microscopic fibrosis mice' liver images were analyzed using fuzzy c-cluster, k-means and watershed algorithms based on distance transformations and gradient. A morphological segmentation based on a local threshold was deployed to determine the fibrosis areas of images. Results: The segmented target region using the proposed method achieved high effective microscopy fibrosis images segmenting of mice liver in terms of the running time, dice ratio and precision. The image classification experiments were conducted using Gray Level Co-occurrence Matrix (GLCM). The best classification model derived from the established characteristics was GLCM which performed the highest accuracy of classification using a developed Support Vector Machine (SVM). The training model using 11 features was found to be accurate when only trained by 8 GLCMs. Conclusion: The research illustrated that the proposed method is a new feasible research approach for microscopy mice liver image segmentation and classification using intelligent image analysis techniques. It is also reported that the average computational time of the proposed approach was only 2.335 seconds, which outperformed other segmentation algorithms with 0.8125 dice ratio and 0.5253 precision.
资助项目	National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [81271663, 31471146]; Zhejiang Wenzhou Medical University Scientific Development Foundation of China [QTJ06012]; Zhejiang Provincial Natural Science FoundationNatural Science Foundation of Zhejiang Province [LY17F030014]
出版者	BENTHAM SCIENCE PUBL LTD
出版地	SHARJAH
ISSN	1574-8936
EISSN	2212-392X
卷号	14 期号:4 页码:282-294
DOI	10.2174/1574893614666190304125221
页数	13
WOS类目	Biochemical Research Methods ; Mathematical & Computational Biology
WOS研究方向	Biochemistry & Molecular Biology ; Mathematical & Computational Biology
WOS记录号	WOS:000463989600002
收录类别	SCIE ; SCOPUS
URL	查看原文
SCOPUSEID	2-s2.0-85067965055
通讯作者地址	[Shi, Fuqian]The College of Information and Engineering,Wenzhou Medical University,Wenzhou,325035,China
Scopus学科分类	Biochemistry;Molecular Biology;Genetics;Computational Mathematics
引用统计	被引频次：43[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/19756
专题	附属第一医院数字化医学与智能技术研究院
通讯作者	Shi, Fuqian
作者单位	1.The First Affiliated Hospital of Wenzhou Medical University,Wenzhou,China; 2.Department of Information Technology,Techno India College of Technology,West Bengal,India; 3.Universal Design Institute,Zhejiang Sci-Tech University,Hangzhou,China; 4.Department of Electronics and Electrical Communications Engineering,Faculty of Engineering,Tanta University,Tanta,Egypt; 5.Department of Biomedical Engineering,University of Reading,Reading,United Kingdom; 6.Department of EIE,St. Joseph’s College of Engineering,Chennai,India; 7.Institute of Digitized Medicine,Wenzhou Medical University,Wenzhou,China
第一作者单位	附属第一医院; 第一临床医学院（信息与工程学院）、附属第一医院
通讯作者单位	温州医科大学
第一作者的第一单位	附属第一医院
推荐引用方式 GB/T 7714	Wang, Yu,Shi, Fuqian,Cao, Luying,et al. Morphological Segmentation Analysis and Texture-based Support Vector Machines Classification on Mice Liver Fibrosis Microscopic Images[J]. CURRENT BIOINFORMATICS,2019,14(4):282-294.
APA	Wang, Yu., Shi, Fuqian., Cao, Luying., Dey, Nilanjan., Wu, Qun., ... & Wu, Lijun. (2019). Morphological Segmentation Analysis and Texture-based Support Vector Machines Classification on Mice Liver Fibrosis Microscopic Images. CURRENT BIOINFORMATICS, 14(4), 282-294.
MLA	Wang, Yu,et al."Morphological Segmentation Analysis and Texture-based Support Vector Machines Classification on Mice Liver Fibrosis Microscopic Images".CURRENT BIOINFORMATICS 14.4(2019):282-294.