Bimodal Technique for Enhancement of Picture Quality of Medical Images

  • Akeem Abimbola Raji Department of Electrical and Electronics Engineering, Federal University of Agriculture, Abeokuta, Nigeria http://orcid.org/0000-0003-4303-4940
  • Oluwaseun Ibrahim Adebisi Department of Electrical and Electronics Engineering, Federal University of Agriculture, Abeokuta, Nigeria http://orcid.org/0000-0001-8958-4951
  • Adewale Olubunmi Akinola Department of Electrical and Electronics Engineering, Federal University of Agriculture, Abeokuta, Nigeria http://orcid.org/0000-0001-6532-1698
  • Benson Ayoade Ogundare Department of Electrical and Electronics Engineering, Lagos State University of Science and Technology, Ikorodu, Nigeria http://orcid.org/0009-0003-7876-6444

Abstract

Medical images are useful for diagnosis of medical disorder in human body. Image enhancement has received significant attention in the literature in a bid to help medical personnel in ascertaining the cause of ailment in human body. Conventional techniques for enhancing medical images suffer from over contrast enhancement, noise and poor picture quality. As a result, this work proposes a bimodal technique that combines Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) for improving the picture quality of medical images. The performance of the proposed model in enhancing the picture quality of gray scale X-ray, Computed Tomography (CT) and Magnetic Resonance Image (MRI) images is compared with HE and CLAHE. It is observed that the proposed bimodal technique performs better than HE and CLAHE in all images used as candidates of investigation. It produces better picture quality and better structural quality than HE and CLAHE. It is found that the proposed model exhibits 59% picture quality while HE and CLAHE, respectively, exhibit 11% and 30% picture qualities.

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Published
2025-06-26
How to Cite
Raji, A., Adebisi, O., Akinola, A., & Ogundare, B. (2025). Bimodal Technique for Enhancement of Picture Quality of Medical Images. ITEGAM-JETIA, 11(53), 177-185. https://doi.org/10.5935/jetia.v11i53.1670
Section
Articles