A New Local Fractional Entropy-Based Model for Kidney MRI Image Enhancement
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 pixe...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Published: |
MDPI
2018
|
| Subjects: |
| _version_ | 1796961645208010752 |
|---|---|
| author | Al-Shamasneh, Ala'a R. Jalab, Hamid Abdullah Shivakumara, Palaiahnakote Obaidellah, Unaizah Hanum Ibrahim, Rabha Waell El-Melegy, Moumen |
| author_facet | Al-Shamasneh, Ala'a R. Jalab, Hamid Abdullah Shivakumara, Palaiahnakote Obaidellah, Unaizah Hanum Ibrahim, Rabha Waell El-Melegy, Moumen |
| author_sort | Al-Shamasneh, Ala'a R. |
| collection | UM |
| description | 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. |
| first_indexed | 2024-03-06T05:57:12Z |
| format | Article |
| id | um.eprints-22518 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:57:12Z |
| publishDate | 2018 |
| publisher | MDPI |
| record_format | dspace |
| spelling | um.eprints-225182019-09-24T01:05:18Z http://eprints.um.edu.my/22518/ A New Local Fractional Entropy-Based Model for Kidney MRI Image Enhancement Al-Shamasneh, Ala'a R. Jalab, Hamid Abdullah Shivakumara, Palaiahnakote Obaidellah, Unaizah Hanum Ibrahim, Rabha Waell El-Melegy, Moumen QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering 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. MDPI 2018 Article PeerReviewed 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 <https://doi.org/10.3390/e20050344>. https://doi.org/10.3390/e20050344 doi:10.3390/e20050344 |
| spellingShingle | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Al-Shamasneh, Ala'a R. Jalab, Hamid Abdullah Shivakumara, Palaiahnakote Obaidellah, Unaizah Hanum Ibrahim, Rabha Waell El-Melegy, Moumen A New Local Fractional Entropy-Based Model for Kidney MRI Image Enhancement |
| title | A New Local Fractional Entropy-Based Model for Kidney MRI Image Enhancement |
| title_full | A New Local Fractional Entropy-Based Model for Kidney MRI Image Enhancement |
| title_fullStr | A New Local Fractional Entropy-Based Model for Kidney MRI Image Enhancement |
| title_full_unstemmed | A New Local Fractional Entropy-Based Model for Kidney MRI Image Enhancement |
| title_short | A New Local Fractional Entropy-Based Model for Kidney MRI Image Enhancement |
| title_sort | new local fractional entropy based model for kidney mri image enhancement |
| topic | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering |
| work_keys_str_mv | AT alshamasnehalaar anewlocalfractionalentropybasedmodelforkidneymriimageenhancement AT jalabhamidabdullah anewlocalfractionalentropybasedmodelforkidneymriimageenhancement AT shivakumarapalaiahnakote anewlocalfractionalentropybasedmodelforkidneymriimageenhancement AT obaidellahunaizahhanum anewlocalfractionalentropybasedmodelforkidneymriimageenhancement AT ibrahimrabhawaell anewlocalfractionalentropybasedmodelforkidneymriimageenhancement AT elmelegymoumen anewlocalfractionalentropybasedmodelforkidneymriimageenhancement AT alshamasnehalaar newlocalfractionalentropybasedmodelforkidneymriimageenhancement AT jalabhamidabdullah newlocalfractionalentropybasedmodelforkidneymriimageenhancement AT shivakumarapalaiahnakote newlocalfractionalentropybasedmodelforkidneymriimageenhancement AT obaidellahunaizahhanum newlocalfractionalentropybasedmodelforkidneymriimageenhancement AT ibrahimrabhawaell newlocalfractionalentropybasedmodelforkidneymriimageenhancement AT elmelegymoumen newlocalfractionalentropybasedmodelforkidneymriimageenhancement |