DOI : https://doi.org/10.18517/ijaseit.14.6.10115

A Review of Brain Early Infarct Image Contrast Enhancement Using Various Histogram Equalization Techniques

Yu Jie Ng (1), Kok Swee Sim (2)
(1) Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, Melaka, Malaysia
(2) Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, Melaka, Malaysia
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How to cite (IJASEIT) :
Ng , Yu Jie, and Kok Swee Sim. “A Review of Brain Early Infarct Image Contrast Enhancement Using Various Histogram Equalization Techniques”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 6, Dec. 2024, pp. 1849-60, doi:10.18517/ijaseit.14.6.10115.
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Stroke is one of the leading causes of death worldwide, accounting for five of all deaths in Malaysia. It happens when an infarct from a blocked blood artery results in brain necrosis. Diagnoses involving brain diseases and injuries can be made with the help of CT scans, which create axial images by using exact X-ray measurements. These scans offer vital information on the anatomy and physiology of the brain. For an appropriate diagnosis, early infarct brain CT scan contrast can be improved. The two main types of histogram equalization (HE) approaches used for this purpose are Global Histogram Equalization (GHE) and Local Histogram Equalization (LHE), which is also referred to as adaptive histogram equalization (AHE). Locally, LHE uses the block overlapped method to improve photos. Additional sophisticated methods include Dualistic Sub Image Histogram Equalization (DSIHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Recursive Sub Image Histogram Equalization (RSIHE), Gamma Correction Adaptive Extreme Level Eliminating With Weighting Distribution (GCAELEWD), and Brightness Preserving Bi Histogram Equalization (BBHE). The contrast of brain images is greatly improved by these techniques. Nevertheless, a number of these methods have issues with blur, noise, and preserving local image brightness. According to our research, CLAHE and DSIHE are especially good to improve image contrast and yield better outcomes than other techniques. These methods lessen frequent problems, which makes them better suited to create precise and comprehensive brain images—an essential component of successful stroke diagnosis and treatment.

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