Al-Shamasneh, Ala'a R. and Jalab, Hamid Abdullah and Shivakumara, Palaiahnakote and Obaidellah, Unaizah Hanum and Ibrahim, Rabha Waell and El-Melegy, Moumen (2018) A New Local Fractional Entropy-Based Model for Kidney MRI Image Enhancement. Entropy, 20 (5). p. 344. ISSN 1099-4300, DOI https://doi.org/10.3390/e20050344.
Full text not available from this repository.Abstract
Kidney image enhancement is challenging due to the unpredictable quality of MRI images, as well as the nature of kidney diseases. The focus of this work is on kidney images enhancement by proposing a new Local Fractional Entropy (LFE)-based model. The proposed model estimates the probability of pixels that represent edges based on the entropy of the neighboring pixels, which results in local fractional entropy. When there is a small change in the intensity values (indicating the presence of edge in the image), the local fractional entropy gives fine image details. Similarly, when no change in intensity values is present (indicating smooth texture), the LFE does not provide fine details, based on the fact that there is no edge information. Tests were conducted on a large dataset of different, poor-quality kidney images to show that the proposed model is useful and effective. A comparative study with the classical methods, coupled with the latest enhancement methods, shows that the proposed model outperforms the existing methods.
| Item Type: | Article |
|---|---|
| Funders: | Postgraduate Research Grant (PPP) of University of Malaya, Malaysia. Grant no: PG019-2015B., A grant (USC17:253) from the Science and Technology Development Fund (STDF) in Egypt |
| Uncontrolled Keywords: | local fractional; entropy; MRI; image enhancement |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 24 Sep 2019 01:05 |
| Last Modified: | 24 Sep 2019 01:05 |
| URI: | http://eprints.um.edu.my/id/eprint/22518 |
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