Enhancement of Medical Images through an Iterative McCann Retinex Algorithm: A Case of Detecting Brain Tumor and Retinal Vessel Segmentation
Analyzing medical images has always been a challenging task because these images are used to observe complex internal structures of the human body. This research work is based on the study of the retinal fundus and magnetic resonance images (MRI) for the analysis of ocular and cerebral abnormalities...
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| Format: | Article |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/16/8243 |
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| author | Yassir Edrees Almalki Nisar Ahmed Jandan Toufique Ahmed Soomro Ahmed Ali Pardeep Kumar Muhammad Irfan Muhammad Usman Keerio Saifur Rahman Ali Alqahtani Samar M. Alqhtani Mohammed Awaji M. Hakami Alqahtani Saeed S Waleed A. Aldhabaan Abdulrahman Samir Khairallah |
| author_facet | Yassir Edrees Almalki Nisar Ahmed Jandan Toufique Ahmed Soomro Ahmed Ali Pardeep Kumar Muhammad Irfan Muhammad Usman Keerio Saifur Rahman Ali Alqahtani Samar M. Alqhtani Mohammed Awaji M. Hakami Alqahtani Saeed S Waleed A. Aldhabaan Abdulrahman Samir Khairallah |
| author_sort | Yassir Edrees Almalki |
| collection | DOAJ |
| description | Analyzing medical images has always been a challenging task because these images are used to observe complex internal structures of the human body. This research work is based on the study of the retinal fundus and magnetic resonance images (MRI) for the analysis of ocular and cerebral abnormalities. Typically, clinical quality images of the eyes and brain have low-varying contrast, making it challenge to diagnose a specific disease. These issues can be overcome, and preprocessing or an image enhancement technique is required to properly enhance images to facilitate postprocessing. In this paper, we propose an iterative algorithm based on the McCann Retinex algorithm for retinal and brain MRI. The foveal avascular zone (FAZ) region of retinal images and the coronal, axial, and sagittal brain images are enhanced during the preprocessing step. The High-Resolution Fundus (HRF) and MR brain Oasis images databases are used, and image contrast and peak signal-to-noise ratio (PSNR) are used to assess the enhancement step parameters. The average PSNR enhancement on images from the Oasis brain MRI database was about 3 dB with an average contrast of 57.4. The average PSNR enhancement of the HRF database images was approximately 2.5 dB with a contrast average of 40 over the database. The proposed method was also validated in the postprocessing steps to observe its impact. A well-segmented image was obtained with an accuracy of 0.953 and 0.0949 on the DRIVE and STARE databases. Brain tumors were detected from the Oasis brain MRI database with an accuracy of 0.97. This method can play an important role in helping medical experts diagnose eye diseases and brain tumors from retinal images and Oasis brain images. |
| first_indexed | 2024-03-09T10:01:42Z |
| format | Article |
| id | doaj.art-9a40903873414e3f82a3ae7f3c3fb34a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T10:01:42Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9a40903873414e3f82a3ae7f3c3fb34a2023-12-01T23:22:05ZengMDPI AGApplied Sciences2076-34172022-08-011216824310.3390/app12168243Enhancement of Medical Images through an Iterative McCann Retinex Algorithm: A Case of Detecting Brain Tumor and Retinal Vessel SegmentationYassir Edrees Almalki0Nisar Ahmed Jandan1Toufique Ahmed Soomro2Ahmed Ali3Pardeep Kumar4Muhammad Irfan5Muhammad Usman Keerio6Saifur Rahman7Ali Alqahtani8Samar M. Alqhtani9Mohammed Awaji M. Hakami10Alqahtani Saeed S11Waleed A. Aldhabaan12Abdulrahman Samir Khairallah13Division of Radiology, Department of Internal Medicine, Medical College, Najran University, Najran 61441, Saudi ArabiaOphthalmology Department, Peoples University of Medical Furthermore, Health Sciences for Women (PUMHSW), Nawabshah Shaheed Benazirabad, Nawabshah 67480, PakistanDepartment of Electronic Engineering, Quaid-e-Awam University of Engineering, Science and Technology, Larkana 77150, PakistanDepartment of Electrical Engineering, Sukkur IBA University, Sukkur 65200, PakistanDepartment of Software Engineering, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah 67450, PakistanElectrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 61441, Saudi ArabiaDepartment of Electrical Engineering, Quaid-e-Awam University of Engineering, Science and Technology, Nawabshah 67450, PakistanElectrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 61441, Saudi ArabiaNetworks and Communication Engineering Department, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi ArabiaDepartment of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi ArabiaRadiology Department, Armed Forces Hospital, Jazan 156-84224, Saudi ArabiaDepartment of Surgery, Faculty of Medicine, Najran University, Najran 61441, Saudi ArabiaDepartment of Ophthalmology, King Khalid University College of Medicine, P.O. Box 641, Abha 61421, Saudi ArabiaDepartment of Ophthalmology, King Khalid University College of Medicine, P.O. Box 641, Abha 61421, Saudi ArabiaAnalyzing medical images has always been a challenging task because these images are used to observe complex internal structures of the human body. This research work is based on the study of the retinal fundus and magnetic resonance images (MRI) for the analysis of ocular and cerebral abnormalities. Typically, clinical quality images of the eyes and brain have low-varying contrast, making it challenge to diagnose a specific disease. These issues can be overcome, and preprocessing or an image enhancement technique is required to properly enhance images to facilitate postprocessing. In this paper, we propose an iterative algorithm based on the McCann Retinex algorithm for retinal and brain MRI. The foveal avascular zone (FAZ) region of retinal images and the coronal, axial, and sagittal brain images are enhanced during the preprocessing step. The High-Resolution Fundus (HRF) and MR brain Oasis images databases are used, and image contrast and peak signal-to-noise ratio (PSNR) are used to assess the enhancement step parameters. The average PSNR enhancement on images from the Oasis brain MRI database was about 3 dB with an average contrast of 57.4. The average PSNR enhancement of the HRF database images was approximately 2.5 dB with a contrast average of 40 over the database. The proposed method was also validated in the postprocessing steps to observe its impact. A well-segmented image was obtained with an accuracy of 0.953 and 0.0949 on the DRIVE and STARE databases. Brain tumors were detected from the Oasis brain MRI database with an accuracy of 0.97. This method can play an important role in helping medical experts diagnose eye diseases and brain tumors from retinal images and Oasis brain images.https://www.mdpi.com/2076-3417/12/16/8243retinal imagesbrain MRIbrain tumor detectionretinal vessel segmentationMcCann Retinex algorithmimage enhancement |
| spellingShingle | Yassir Edrees Almalki Nisar Ahmed Jandan Toufique Ahmed Soomro Ahmed Ali Pardeep Kumar Muhammad Irfan Muhammad Usman Keerio Saifur Rahman Ali Alqahtani Samar M. Alqhtani Mohammed Awaji M. Hakami Alqahtani Saeed S Waleed A. Aldhabaan Abdulrahman Samir Khairallah Enhancement of Medical Images through an Iterative McCann Retinex Algorithm: A Case of Detecting Brain Tumor and Retinal Vessel Segmentation Applied Sciences retinal images brain MRI brain tumor detection retinal vessel segmentation McCann Retinex algorithm image enhancement |
| title | Enhancement of Medical Images through an Iterative McCann Retinex Algorithm: A Case of Detecting Brain Tumor and Retinal Vessel Segmentation |
| title_full | Enhancement of Medical Images through an Iterative McCann Retinex Algorithm: A Case of Detecting Brain Tumor and Retinal Vessel Segmentation |
| title_fullStr | Enhancement of Medical Images through an Iterative McCann Retinex Algorithm: A Case of Detecting Brain Tumor and Retinal Vessel Segmentation |
| title_full_unstemmed | Enhancement of Medical Images through an Iterative McCann Retinex Algorithm: A Case of Detecting Brain Tumor and Retinal Vessel Segmentation |
| title_short | Enhancement of Medical Images through an Iterative McCann Retinex Algorithm: A Case of Detecting Brain Tumor and Retinal Vessel Segmentation |
| title_sort | enhancement of medical images through an iterative mccann retinex algorithm a case of detecting brain tumor and retinal vessel segmentation |
| topic | retinal images brain MRI brain tumor detection retinal vessel segmentation McCann Retinex algorithm image enhancement |
| url | https://www.mdpi.com/2076-3417/12/16/8243 |
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