MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Abstract

Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram

Automatic Histogram Specification for Glioma Grading Using Multicenter Data

“…Existing image enhancement techniques (empirical or heuristic) are remarkably related to a particular image and usually aimed at improving image contrast. However, no unified standard is available to measure the quality effect of image enhancement [21][22][23][24].…”

“…is method results in boundary noise and possibly uneven histogram brightness in two parts. In addition, MedGA [24] was an enhancement method that HE combined with genetic algorithm to directly improve the histogram frequency of images and has achieved obvious results. However, this method could only be limited to the presence of two grayscale regional tissues and not enhance complex brain tumors.…”

Bat Algorithm Based Non-linear Contrast Stretching for Satellite Image Enhancement

“…Contrast enhancement is another category of image enhancement which stretches the dynamic range of the image to improve the image visibility. It is one of the most critical problems faced in areas such as medical image processing [3,4], video processing and remote sensing [5,6]. An image contrast enhancement using sub-histogram equalization and fuzzy clustering is presented in [7].…”

2D electrophoresis image brightness correction based on gradient interval histogram

“…Researchers have proposed a variety of methods for 2DE image brightness correction [9][10][11][12][13][14][15]. In 2006, Kazhiyur-Mannar et al [10] used contour wavelet filtering to alleviate the influence of inconsistent background brightness.…”

“…In 2016, Wu et al [13] proposed a method of modeling brightness inhomogeneity by using polynomial fitting of slowly varying gradients and succeeded in removing some background intensities and weak protein spots simultaneously. Specific correction methods in other domains, such as those proposed in the references [14][15][16], are applicable to images with certain characteristics but are not ideal for 2DE images. In addition, as conventional enhancement methods, histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE) involve over-enhancement [17].…”