Automatic Contrast Enhancement of Brain MR Images Using Hierarchical Correlation Histogram Analysis

Parkinson’s disease is a progressive neurodegenerative disorder that has a higher probability of occurrence in middle-aged and older adults than in the young. With the use of a computer-aided diagnosis (CAD) system, abnormal cell regions can be identified, and this identification can help medical personnel to evaluate the chance of disease. This study proposes a hierarchical correlation histogram analysis based on the grayscale distribution degree of pixel intensity by constructing a correlation histogram, that can improves the adaptive contrast enhancement for specific objects. The proposed method produces significant results during contrast enhancement preprocessing and facilitates subsequent CAD processes, thereby reducing recognition time and improving accuracy. The experimental results show that the proposed method is superior to existing methods by using two estimation image quantitative methods of PSNR and average gradient values. Furthermore, the edge information pertaining to specific cells can effectively increase the accuracy of the results.