Comparative analysis of image enhancement techniques for braintumor segmentation: contrast, histogram, and hybrid approaches

¹ Faculty of Computer Science, AGH University of Krakow, Krakow 30-059, Poland
² Department of Informatics, Universitas Pembangunan Nasional Veteran Yogyakarta, Yogyakarta 55281, Indonesia
³ College of Computer and Information, Hohai University, Nanjing 211100 China
⁴ Department of Informatics, Faculty of Industrial Technology, Universitas Ahmad Dahlan, Yogyakarta 55166 Indonesia
⁵ Artificial Intelligence Research Group (AIRG), Informatics Department, Faculty of Industrial Technology, Universitas Ahmad Dahlan, Yogyakarta 55166 Indonesia

^* Corresponding author: shoffans@upnyk.ac.id
^† Corresponding author: andri.pranolo@tif.uad.ac.id

Abstract

This study systematically investigates the impact of image enhancement techniques on Convolutional Neural Network (CNN)-based Brain Tumor Segmentation, focusing on Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and their hybrid variations. Employing the U-Net architecture on a dataset of 3064 Brain MRI images, the research delves into preprocessing steps, including resizing and enhancement, to optimize segmentation accuracy. A detailed analysis of the CNN-based U-Net architecture, training, and validation processes is provided. The comparative analysis, utilizing metrics such as Accuracy, Loss, MSE, IoU, and DSC, reveals that the hybrid approach CLAHE-HE consistently outperforms others. Results highlight its superior accuracy (0.9982, 0.9939, 0.9936 for training, testing, and validation, respectively) and robust segmentation overlap, with Jaccard values of 0.9862, 0.9847, and 0.9864, and Dice values of 0.993, 0.9923, and 0.9932 for the same phases, emphasizing its potential in neuro-oncological applications. The study concludes with a call for refinement in segmentation methodologies to further enhance diagnostic precision and treatment planning in neuro-oncology.