Brain Abnormality Detection and Analysis through Tissues Detection and Segmentation in Magnetic Resonance Imaging of the Brain

Given the constraints of human labour, an automatic CAD framework is essential for the investigation of medical phenomena, especially when a high number of images is involved. It is preferable to utilise an automatic segmentation method because it eliminates the need for human specialists and produces 100% reproducible results. As MR pictures often contain a great deal of information, a computer programme has the advantage of being able to process all of this data more reliably and consistently than human raters. Automatic brain abnormality detection in medical pictures requires great precision because it involves human lives. Also, medical facilities are actively seeking out computer assistance since it has the potential to improve human results in a field where the percentage of false negative and positive cases must be extremely low. Double-reading medical photos have been shown to improve the detection of aberrant regions. This study proposes an iterative application of level set methodology for accurate brain MRI tissue segmentation and abnormality identification. Segmentation of normal tissues including white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF), as well as other components of the human head such the skull, marrow, muscles, skin, and tumour lesions, is possible. The proposed approach is superior to both the current gold standard and more contemporary methods in terms of segmentation accuracy, as it eliminates the problems of over- and under- segmentation.