West Asia Organization for Cancer Prevention (WAOCP), APOCP's West Asia Chapter. Asian Pacific Journal of Cancer Prevention 1513-7368 25 9 2024 09 01 Image Enhancement Using Bidimensional Empirical Mode Decomposition and Morphological Operations for Brain Tumor Detection and Classification 3327 3336 91339 10.31557/APJCP.2024.25.9.3327 EN Giang Hong Nguyen Department of Physics and Computer Science, Faculty of Physics & Engineering Physics, University of Science, Ho Chi Minh City, Vietnam. Department of General Education, Cao Thang Technical College, Ho Chi Minh City, Vietnam. 0009-0009-2043-7828 Yen Thi Hoang Hua Department of Physics and Computer Science, Faculty of Physics & Engineering Physics, University of Science, Ho Chi Minh City, Vietnam. Viet Nam National University, Ho Chi Minh City, Vietnam. 0009-0007-8824-5633 Linh Chi Nguyen Department of Physics and Computer Science, Faculty of Physics & Engineering Physics, University of Science, Ho Chi Minh City, Vietnam. Viet Nam National University, Ho Chi Minh City, Vietnam. Liet Van Dang Department of Physics and Computer Science, Faculty of Physics & Engineering Physics, University of Science, Ho Chi Minh City, Vietnam. Viet Nam National University, Ho Chi Minh City, Vietnam. Journal Article 2024 05 08 Objective: The three steps of brain image processing – preprocessing, segmentation, and classification are becoming increasingly important in patient care. The aim of this article is to present a proposed method in the mentioned three-steps, with emphasis on the preprocessing step, which includes noise removal and contrast enhancement. Methods: The fast and adaptive bidimensional empirical mode decomposition and the anisotropic diffusion equation as well as the modified combination of top-hat and bottom-hat transforms are used for noise reduction and contrast enhancement. Fast C-means clustering with enhanced image is used to detect tumors and the tumor cluster corresponds to the maximum centroid. Finally, Ensemble learning is used for classification. Result: The Figshare brain tumor dataset contains magnetic resonance images used for data selection. The optimal parameters for both noise reduction and contrast enhancement are investigated using a tumor contaminated with Gaussian noise. The results are evaluated against state-of-the-art results and qualitative performance metrics to demonstrate the dominance of the proposed approach. The fast C-means algorithm is applied to detect tumors using twelve enhanced images. The detected tumors were compared to the ground truth and showed an accuracy and specificity of 99% each, and a sensitivity and precision of 90% each. Six statistical features are retrieved from 150 enhanced images using wavelet packet coefficients at level 4 of the Daubechies 4 wavelet function. These features are used to develop the classifier model using ensemble learning to create a model with training and testing accuracy of 96.7% and 76.7%, respectively. When this model is applied to classify twelve detected tumor images, the accuracy is 75%; there are three misclassified images, all of which belong to the pituitary disease group. Conclusion: Based on the research, it appears that the proposed approach could lead to the development of computer-aided diagnosis (CADx) software that physicians can use as a reference for the treatment of rain tumor. https://journal.waocp.org/article_91339_f7c6ab2911e6314052132f93d7c5b48c.pdf