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All Journal International Journal of Advances in Applied Sciences
Olumayowa Ayodeji Idowu
Federal University of Agriculture
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Tri-modal technique for medical images enhancement Kamoli Akinwale Amusa; Olumayowa Ayodeji Idowu; Isaiah Adediji Adejumobi; Gboyega Augustine Adebayo
International Journal of Advances in Applied Sciences Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1075.135 KB) | DOI: 10.11591/ijaas.v11.i3.pp199-210

Abstract

Owing to methods of acquisition, medical images often require enhancement for them to serve the intended purpose of computer-aided diagnosis. Most medical image enhancement techniques are application specific, leading to the introduction of different enhancement methods for different medical images. In addition, the execution time of most of the previous enhancement methods is longer than necessary. Hence, there is a need for a method that produces fast and satisfactory results when deployed for the enhancement of several medical images. This paper proposes a tri-modal technique, involving a hybrid combination of unsharp masking, logarithmic transformation, and histogram equalization approaches, for medical image enhancement. Three classes of medical images: X-ray, magnetic resonance, and computer tomographic images are used for the evaluations of the proposed tri-modal method, where absolute mean brightness error, peak signal-to-noise ratio, and entropy are utilized as performance metrics. Both qualitative and quantitative evaluations reveal that the proposed tri-modal method performed better than the four previous methods in the literature for the three classes of medical images used in the evaluation. Also, the execution time of the tri-modal technique compares well with those of mono-mode methods. Thus, the tri-modal technique produces better enhanced medical images from different medical image inputs.
Co-Authors Gboyega Augustine Adebayo Isaiah Adediji Adejumobi Kamoli Akinwale Amusa