Human Epithelial Cell Image Analysis and Segmentation using Threshold Based Fusion Technique
Biomedical and Pharmacology Journal, ISSN: 2456-2610, Vol: 17, Issue: 1, Page: 443-452
2024
- 1Citations
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations1
- Citation Indexes1
Article Description
The most demanding aspect of digital image processing is segmenting an image efficiently. Cell segmentation or classifying cells in an image is essential while analyzing cell images in medical research, especially in spot diagnosis, cancer cell detection, and live-cell imaging segmentation forms a crucial component. This research examines existing segmentation algorithms and suggests a new segmentation technique that employs image filtering and thresholding. Thresholding is an essential part of image analysis and segmentation. Finally, the segmented image and the FCM (fuzzy C-means) based clustered image are merged. In terms of accuracy, sensitivity, dice-coefficient, and Jaccard-coefficient, the simulation coupled with ground truth data is proven to produce better segmentation outcomes.
Bibliographic Details
Oriental Scientific Publishing Company
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