Physics

Radiology

Pattern recognition (psychology)

Metric (unit)

Contrast enhancement

Algorithm

Image (mathematics)

Optics

Magnetic resonance imaging

Brightness

Peak signal-to-noise ratio

Color image

Artificial intelligence

Economics

Operations management

Contrast (vision)

Computer science

Histogram

Image histogram

Computer vision

Image quality

Medicine

Image processing

Procedia Computer Science

Medical images constitute important information that clinicians need to diagnose and make the suitable treatment decisions. The diagnostic process extremely involves the image visual perception. Unfortunately, the possibility of error existence in perception is not acceptable as it mainly affects the patients' lives. Image enhancement improves the visual quality of image, helps the clinician in his decision and thus saves the patients' lives. Histogram is a common tool for improving contrast in medical imaging. It recovers the lost contrast by redistributing the image brightness values that unfortunately may generate undesirable artifacts. Therefore, researchers developed the histogram-based algorithms to overcome this problem. This paper presents a comprehensive study of many histogram-based algorithms. We utilized the powerful MATLAB package to analyze the enhancement performance of these histogram-based algorithms. Moreover, this paper quantitatively compares the results and thus evaluates their performance by three metric parameters, which are the mean square error, standard deviation, and the peak signal to noise ratio.

Medical image enhancement based on histogram algorithms