ArticleOpen medicine (Warsaw, Poland)2018
Thresholding for Medical Image Segmentation for Cancer using Fuzzy Entropy with Level Set Algorithm.
Article in Open medicine (Warsaw, Poland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A deep learning approach to assess transendothelial cell trafficking performance.Scientific reports · 2026Article
- MDiagnostics (Basel, Switzerland) · 2025Article
- 3D imaging and morphometric descriptors of vascular networks on optically cleared organs.iScience · 2023Review
- Article
- Article
- Grayscale medical image segmentation method based on 2D&3D object detection with deep learning.BMC medical imaging · 2022Article
- Methods for the segmentation and classification of breast ultrasound images: a review.Journal of ultrasound · 2021Review
- Intelligent Segmentation Algorithm for Diagnosis of Meniere's Disease in the Inner Auditory Canal Using MRI Images with Three-Dimensional Level Set.Contrast media & molecular imaging · 2021Article
- Dermoscopy in China: current status and future prospective.Chinese medical journal · 2019Review
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Authors and funding
3 authors.
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Abstract
In this study, an effective means for detecting cancer region through different types of medical image segmentation are presented and explained. We proposed a new method for cancer segmentation on the basis of fuzzy entropy with a level set (FELs) thresholding. The proposed method was successfully utilized to segment cancer images and then efficiently performed the segmentation of test ultrasound image, brain MRI, and dermoscopy image compared with algorithms proposed in previous studies. Results showed an excellent performance of the proposed method in detecting cancer image segmentation in terms of accuracy, precision, specificity, and sensitivity measures.
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