MRI brain image enhancement using brightness preserving adaptive fuzzy histogram equalization

Abstract

In this article, fuzzy logic based adaptive histogram equalization (AHE) is proposed to enhance the contrast of MRI brain image. Medical image plays an important role in monitoring patient's health condition and giving an effective diagnostic. Mostly, medical images suffer from different problems such as poor contrast and noise. So it is necessary to enhance the contrast and to remove the noise in order to improve the quality of a various medical images such as CT, X‐ray, MRI, and MAMOGRAM images. Fuzzy logic is a useful tool for handling the ambiguity or uncertainty. Brightness Preserving Adaptive Fuzzy Histogram Equalization technique is proposed to improve the contrast of MRI brain images by preserving brightness. Proposed method comprises of two stages. First, fuzzy logic is applied to an input image and then it's output is given to AHE technique. This process not only preserves the mean brightness and but also improves the contrast of an image. A huge number of highly MRI brain images are taken in the proposed method. Performance of the proposed method is compared with existing methods using the parameters namely entropy, feature similarity index, and contrast improvement index and the experimental results show that the proposed method overwhelms the previous existing methods.

An integrated feature frame work for automated segmentation of COVID‐19 infection from lung CT images

“…The infected pixels are brightened visually as shown in Figure 2. Also, the Contrast Improvement Index (CII) 46 metrics are analysed to highlight the importance of pre‐processing on the COVID dataset. The image quality (pixel intensity) is improved by 30% to 40% when compared to the original CT image as shown in Table 2.…”

Bidirectional ConvLSTMXNet for Brain Tumor Segmentation of MR Images

“…To enhance the contrast of MRI brain images, deferent spatial domain techniques were proposed like Histogram Equalization (HE) [4,5,7,9,10], Adaptive Histogram Equalization (AHE) [4,5], Contrast Limited Adaptive Histogram Equalization (CLAHE) [4,7], LHE [4], BBHE [5,10], MMBEBHE [5,6], BPDHE [5,6,8], RMSHE [6], BPDHE [6], DSIHE [6], BPDFHE [7], Deferent Techniques like GHE [8], Modified BHE, Brightness preserving BHE (BBHE) [10], Fuzzy logic based Adaptive Histogram Equalization (AHE) [5], Multi Scale Retinex (MSR) [9] and Non-sub sampled Contour-let Transform (NSCT)-FU [9].Different frequency based domain methods were proposed to enhance MRI brain images. Methods are Gabor Filter [13], Gaussian Filter [13,23,30,29], salt and peppernoise [13,23], Median Filter [16,17,18,20,22,25,26,30], An-isotropic Diffusion Filter [15,17], Linear Filter [29], Wiener Filter…”

Brightness preserving optimized weighted bi‐histogram equalization algorithm and its application to MR brain image segmentation

“…Then, every sub‐image is equalized separately and these sub‐images are combined together to get an enhanced image. Various enhancement algorithms have been proposed in order to improve the contrast and preserve the fine details in an image.…”

“…Then, every sub-image is equalized separately and these sub-images are combined together to get an enhanced image. Various enhancement algorithms [9][10][11][12] have been proposed in order to improve the contrast and preserve the fine details in an image. Differential evolution (DE) and genetic algorithm (GA) are stochastic and robust metaheuristics in the field of evolutionary computation and also used in image processing field to solve optimization problems.…”