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Wang Y, Shi F, Cao L, Dey N, Wu Q, Ashour AS, Sherratt RS, Rajinikanth V, Wu L. Morphological Segmentation Analysis and Texture-based Support Vector Machines Classification on Mice Liver Fibrosis Microscopic Images. Curr Bioinform 2019. [DOI: 10.2174/1574893614666190304125221] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background:
To reduce the intensity of the work of doctors, pre-classification work
needs to be 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.
</P><P>
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.</P>
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Affiliation(s)
- Yu Wang
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Fuqian Shi
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Luying Cao
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Nilanjan Dey
- Department of Information Technology, Techno India College of Technology, West Bengal, India
| | - Qun Wu
- Universal Design Institute, Zhejiang Sci-Tech University, Hangzhou, China
| | - Amira Salah Ashour
- Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt
| | - Robert Simon Sherratt
- Department of Biomedical Engineering, University of Reading, Reading, United Kingdom
| | | | - Lijun Wu
- Institute of Digitized Medicine, Wenzhou Medical University, Wenzhou, China
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