A New Approach based on Fuzzy Clustering and Enhancement Operator for Medical Image Contrast Enhancement

Background: Image enhancement is a very significant topic in image processing that improves the quality of the images. The methods of image enhancement are classified into 3 categories. They include histogram method, fuzzy logic method and optimal method. Studies on image enhancement are often based on rules: if it's bright then it's brighter, if it's dark then it's darker and using the global approach. So, it's hard to enhance objects in all dark and light areas, as in the medical images.

Objective: Input data is downloaded from the link: http://www.med.harvard.edu/AANLIB.

Method: This paper introduces a new algorithm for enhancing medical images that is called the medical image enhancement based on cluster enhancement (MIECE). Firstly, the input image is clustered by the algorithm of fuzzy clustering. Then, the upper bound, and lower bound are calculated according to cluster. Next, the sub-algorithm is implemented for clustering enhancement using an enhancement operator. For each pixel, the gray levels for each channel (R, G, B) are transformed with this sub-algorithm to generate new corresponding gray levels. Because after clustering, each pixel belongs to each cluster with the corresponding membership value. Therefore, the output gray level value will be aggregated from the enhanced gray levels by the sub-algorithm with the weight of the corresponding cluster membership value.

Results: This paper experiences the method MIECE with input data downloaded from the link: http://www.med.harvard.edu/AANLIB. The experimental results are compared with some recent methods that include: SGHIE (2017), Ying (2017) and KinD++ (2021).

Conclusion: This paper introduces the new algorithm which is based on cluster enhancement (MIECE) to enhance the medical image contrast. The experimental results show that the output images of the proposed algorithm are better than some other recent methods for enhancing dark objects.