Brain Tumour Detection Using Segmentation Using MATLAB


  • Manoj Goswami M Tech. Scholar in RIET, Jaipur
  • Shalini Sharma Assistant Professor in RIET, Jaipur


Tumour segmentation; k-means clustering; magnetic resonance imaging


Tumors in the brain develop when aberrant cells multiply too quickly. It invades the skull and stops the brain from functioning normally. It is critical to detect the tumor while it is still relatively small using MRI or CT scanned images since it may progress to malignancy if left untreated. In this research, we propose an approach for utilizing MRI scans to detect and pinpoint the specific site of preexisting brain tumors in individuals. The proposed method consists of the three stages of pre-processing, edge detection, and segmentation. During this stage, the original image is converted to grayscale and any detectable or undetectable noise is removed. The outcomes of our edge detection using the Sobel, Prewitt, and Canny algorithms are then subjected to image enhancement techniques. Next, the MRI scans of the tumor are divided to highlight the affected region. Kmeans is then used to cluster like colored pixels into larger regions. For this application, we choose to use MATLAB version 2021a for development. Low sensitivity border pixels in glioma brain imaging provide a significant difficulty for cancer area detection. In this study, we use Non-Sub sampled Contourlet Transform (NSCT) to enhance a previously acquired brain scan and then use that enhanced scan to extract texture features. To assess whether or not a particular brain image is a Glioma, these criteria are employed in an ANFIS-based training and classification procedure. The Glioma brain image is then segmented using morphological techniques to isolate the tumor areas.

Keywords: Tumour segmentation; k-means clustering; magnetic resonance imaging




How to Cite

Goswami, M. ., & Sharma, S. . (2023). Brain Tumour Detection Using Segmentation Using MATLAB. International Journal of Engineering Technology and Computer Research, 11(5). Retrieved from