A HYBRID METHOD FOR MEDICAL IMAGES INDEXING USING PRINCIPAL COMPONENT ANALYSIS AND CDF BIORTHOGONAL WAVELET BASED ON LIFTING SCHEME
| Autor: | Ismail Boukli Hacene, Souad Meziane Tani, Abdelhafid Bessaid |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Lifting scheme
business.industry Search engine indexing ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Biomedical Engineering Wavelet transform Pattern recognition computer.software_genre 030218 nuclear medicine & medical imaging Image (mathematics) Euclidean distance 03 medical and health sciences 0302 clinical medicine Principal component analysis Artificial intelligence Data mining business Biorthogonal wavelet Image retrieval computer 030217 neurology & neurosurgery Mathematics |
| Zdroj: | Journal of Mechanics in Medicine and Biology. 17:1750036 |
| ISSN: | 1793-6810 0219-5194 |
| DOI: | 10.1142/s0219519417500361 |
| Popis: | Hospitals and clinics produce a great number of medical images that are stored in large databases. Content-based medical image retrieval systems (CBMIRs) is one of the solutions to access rapidly and efficiently to these databases using their visual content. In this paper, we propose a new algorithm for medical image indexing. It consists on a combination of bio-orthogonal CDF wavelet transform (WT) based on lifting scheme and principal component analysis (PCA). We use this WT to decompose images, then we apply the PCA method to reduce the number of features and select the pertinent components which represent image signature. Finally, Euclidean distance is used to retrieve most similar image from databases when query image is submitted. We have tested our algorithm on the retinal image database. The results obtained by our algorithm are compared with several published methods cited in the literature and shows an efficiency of 80%, which is significantly higher and much faster than recent methods in CBMIRs domain. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|