Contrast Enhancement of CT Brain Images Using Gamma Correction Adaptive Extreme-Level Eliminating with Weighting Distribution

Stroke is one of the top leading causes of fatality globally among people whose ages are above 60 years old. Computed tomography (CT) scan is the primary medical diagnosis equipment operated by radiologist and doctor for inspection of stroke cases. Window setting is always used for presentation of CT and magnetic resonance imaging (MRI) brain image. Besides, contrast enhancement techniques are also implemented to improve the contrast of CT brain image. Nevertheless, it is very difficult for radiologist and doctor to diagnose the brain image especially on early stroke cases. The main reason is because the ordinary window parameters and some of the existing contrast enhancement techniques cannot provide a good contrast for highlighting the region of interest (ROI) or hypodense area during the stroke diagnosis. Therefore, by implementing the concept of local histogram equalization (LHE), a new histogram modification technique called gamma correction adaptive extreme-level eliminating with weighting distribution (GCAELEWD) is proposed. This new technique is used to increase the difference of intensities on CT brain images. Moreover, this new approach is competent to preserve the local change of brightness in input image while extending the dynamic range of an input image