Brain tumor detection and accurate size estimation are crucial for early diagnosis, treatment planning, and monitoring disease progression. Manual analysis of Magnetic Resonance Imaging (MRI) scans is time-consuming and prone to human error, underscoring the need for automated systems. This paper presents an automated brain tumor detection and size estimation system using MRI images, implemented with image processing techniques and a graphical user interface (GUI). The proposed method begins with image preprocessing, including resizing, grayscale conversion, contrast enhancement, and Gaussian filtering for noise reduction. Segmentation is performed using Otsu’s thresholding to convert the image to binary, followed by morphological operations to remove noise and refine tumor regions. Connected component analysis is then applied to identify potential tumor regions, and validation criteria based on area, solidity, and intensity are used to eliminate false detections. The most prominent valid region is selected as the tumor. Experimental results demonstrate that the proposed system effectively detects tumors in MRI images and accurately distinguishes non-tumor cases, producing appropriate results. The method is computationally efficient, easy to implement, and suitable for preliminary diagnostic support. This work highlights the potential of image processing techniques for developing low-cost, accessible medical image analysis tools and lays a foundation for future improvements through advanced machine learning.
Brain Tumor Detection, MRI Imaging, Image Segmentation.
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