Thresholding for medical image segmentation for cancer using fuzzy entropy with level set algorithm
| Autor: | Yahya E. A. Al-Salhi, Ismail Yaqub Maolood, Songfeng Lu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
level set algorithm 02 engineering and technology 030218 nuclear medicine & medical imaging Image (mathematics) 03 medical and health sciences 0302 clinical medicine Level set 0202 electrical engineering electronic engineering information engineering Medicine Segmentation Sensitivity (control systems) image segmentation Basis (linear algebra) business.industry Cancer Pattern recognition General Medicine Image segmentation medicine.disease Thresholding ComputingMethodologies_PATTERNRECOGNITION fuzzy entropy thresholding cancer segmentation 020201 artificial intelligence & image processing Artificial intelligence business Regular Articles |
| Zdroj: | Open Medicine, Vol 13, Iss 1, Pp 374-383 (2018) Open Medicine |
| ISSN: | 2391-5463 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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