Enhanced image similarity analysis system in digital pathology
| Autor: | Kyung-Chan Choi, Jae Gu Lee, Seung-Ho Yeon, Young Woong Ko, Jeong Won Kim |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
021110 strategic
defence & security studies Standard test image Computer Networks and Communications Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 0211 other engineering and technologies Cancer Digital pathology Image processing 02 engineering and technology medicine.disease Automatic image annotation Hardware and Architecture Feature (computer vision) Digital image processing 0202 electrical engineering electronic engineering information engineering Media Technology medicine 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Software Feature detection (computer vision) |
| Zdroj: | Multimedia Tools and Applications. 76:25477-25494 |
| ISSN: | 1573-7721 1380-7501 |
| DOI: | 10.1007/s11042-017-4773-z |
| Popis: | In digital pathology, image similarity algorithms are used to find cancer in tissue cells from medical images. However, it is very difficult to apply image similarity algorithms used in general purpose system. Because in the medical field, accuracy and reliability must be perfect when looking for cancer cells by using image similarity techniques to pathology images. To cope with this problem, this paper proposes an efficient similar image search algorithm for digital pathology by applying leveling and tiling scheme on OpenSlide format. Furthermore, we apply image sync method to extract feature key points during image similarity processing. In the experiment, to prove the efficiency of the proposed system, we conduct several experiments including algorithm performance, algorithm accuracy and computation time. The experiments result shows that the proposed system efficiently retrieves similar cell images from pathology images. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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