Medical image enhancement through Klein–Gordon model with advanced differential operators
Image enhancement in medicine is crucial for diagnosing anatomical structures like tissues, organs, bones, and tumors. Insufficient lighting, incorrect settings, sensor constraints, excessive exposure, noise, patient motion, technical artifacts, and poor post–processing lead to low contrast and dist...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-10-01
|
| Series: | Ain Shams Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447925003582 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849336161263157248 |
|---|---|
| author | A. Priya S. Kalaivani |
| author_facet | A. Priya S. Kalaivani |
| author_sort | A. Priya |
| collection | DOAJ |
| description | Image enhancement in medicine is crucial for diagnosing anatomical structures like tissues, organs, bones, and tumors. Insufficient lighting, incorrect settings, sensor constraints, excessive exposure, noise, patient motion, technical artifacts, and poor post–processing lead to low contrast and distorted noise interference. Consequently, the processes of diagnosis and decision-making is a challenges for medical professionals. The proposed work introduces a novel method using the Klein–Gordon equation to model image intensity as a scalar field, improving propagation, reducing noise, and maintaining edge sharpness. The Grunwald-Letnikov fractional derivative detects subtle edges while balancing smoothing and enhancement, preserving internal structure. Hausdorff fractal derivative significantly influence low-contrast images by improving local contrast and preserving details. The proposed method’s effectiveness is shown through assessments using no-reference image quality metrics BRISQUE and NIQE. The technique uses Chest X-ray, Dental X-ray, SARS-COV-CT Scan, and brain MRI results in BRISQUE and NIQE values, revealing significant visual enhancements over other methods. |
| format | Article |
| id | doaj-art-e619e40be85f436681c6e028725d1f55 |
| institution | Kabale University |
| issn | 2090-4479 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj-art-e619e40be85f436681c6e028725d1f552025-08-20T03:45:03ZengElsevierAin Shams Engineering Journal2090-44792025-10-01161010361710.1016/j.asej.2025.103617Medical image enhancement through Klein–Gordon model with advanced differential operatorsA. Priya0S. Kalaivani1School of Advanced Sciences, Vellore Institute of Technology, Vellore, IndiaSchool of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India; Corresponding author.Image enhancement in medicine is crucial for diagnosing anatomical structures like tissues, organs, bones, and tumors. Insufficient lighting, incorrect settings, sensor constraints, excessive exposure, noise, patient motion, technical artifacts, and poor post–processing lead to low contrast and distorted noise interference. Consequently, the processes of diagnosis and decision-making is a challenges for medical professionals. The proposed work introduces a novel method using the Klein–Gordon equation to model image intensity as a scalar field, improving propagation, reducing noise, and maintaining edge sharpness. The Grunwald-Letnikov fractional derivative detects subtle edges while balancing smoothing and enhancement, preserving internal structure. Hausdorff fractal derivative significantly influence low-contrast images by improving local contrast and preserving details. The proposed method’s effectiveness is shown through assessments using no-reference image quality metrics BRISQUE and NIQE. The technique uses Chest X-ray, Dental X-ray, SARS-COV-CT Scan, and brain MRI results in BRISQUE and NIQE values, revealing significant visual enhancements over other methods.http://www.sciencedirect.com/science/article/pii/S2090447925003582Grunwald – Letnikov fractional derivativeHausdorff fractal derivativeKelin-Gordon equationMedical imagesImage enhancement |
| spellingShingle | A. Priya S. Kalaivani Medical image enhancement through Klein–Gordon model with advanced differential operators Ain Shams Engineering Journal Grunwald – Letnikov fractional derivative Hausdorff fractal derivative Kelin-Gordon equation Medical images Image enhancement |
| title | Medical image enhancement through Klein–Gordon model with advanced differential operators |
| title_full | Medical image enhancement through Klein–Gordon model with advanced differential operators |
| title_fullStr | Medical image enhancement through Klein–Gordon model with advanced differential operators |
| title_full_unstemmed | Medical image enhancement through Klein–Gordon model with advanced differential operators |
| title_short | Medical image enhancement through Klein–Gordon model with advanced differential operators |
| title_sort | medical image enhancement through klein gordon model with advanced differential operators |
| topic | Grunwald – Letnikov fractional derivative Hausdorff fractal derivative Kelin-Gordon equation Medical images Image enhancement |
| url | http://www.sciencedirect.com/science/article/pii/S2090447925003582 |
| work_keys_str_mv | AT apriya medicalimageenhancementthroughkleingordonmodelwithadvanceddifferentialoperators AT skalaivani medicalimageenhancementthroughkleingordonmodelwithadvanceddifferentialoperators |