Abstract: Image segmentation has historically been thought of as the first step in image processing. A good segmentation
result will make further image processing analysis much simpler. However, there are numerous image segmentation
algorithms and approaches available. One of the most widely used image segmentation techniques is clustering. Medical
experts perform tumour segmentation from magnetic resonance imaging (MRI) data, which is an essential yet timeconsuming
manual task. Because of the wide variation in the appearance of tumour tissues among patients and their
sometimes close resemblance to normal tissues, automating this process is a difficult task. MRI is a type of advanced
medical imaging that provides detailed details about the anatomy of the human soft tissues. To identify and segment a
brain tumour from MRI images, various brain tumour detection and segmentation methods are used. The advantages
and disadvantages of these methods for brain tumour detection and segmentation are discussed, with an emphasis on
enlightening the advantages and disadvantages of these methods for brain tumour detection and segmentation. The
application of MRI image detection and segmentation to various procedures is also discussed. A brief overview of various
segmentation methods for detecting brain tumours from MRI scans of the brain is presented here & also use different
Clustering Techniques.
Keywords: Tumor Detection; Magnetic resonance imaging (MRI); Tumor Segmentation; Automated System; Pre-processing,
Filtering; computed tomography (CT).