Modified Histogram Equalization for Improved CNN Medical Image Segmentation

Abstract

This research aims to improve the performance of convolutional neural network (CNN) in medical image segmentation that will detect specific parts of the body's anatomical structures. Medical images have drawbacks, such as the image's variability, quality, and complexity. We developed image preprocessing scenarios using Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and the hybrid approaches (HE-CLAHE and CLAHE-HE). We propose CNN with image enhancement for image segmentation and evaluate its performance on Lung CT-Scan and Chest X-ray datasets, which totaled 267 and 3616 images, respectively, and had ground truth. The experimental results indicate that the optimal cumulative distribution function (CDF) value of HE is 0 to 39, and the clip limit of CLAHE is 0.01. CNN produces the best segmentation with the addition of the CLAHE-HE approach. This method can increase the accuracy by 1.23 percentage points (training) and 3.22 percentage points (testing) for Lung CT-Scan images. Meanwhile, for Chest X-ray images, the training and testing accuracy increased by 1.58 and 0.96 percentage points. In addition, the proposed medical image segmentation approach using the CNN method with CLAHE-HE obtained the values of comparative coefficients DSC (dice similarity coefficient), and SSIM (structural similarity index measurement) of only about 0.92 and 0.97, respectively.