부산대학교 도서관

Segmenting a brain tumour entail dividing the various tumour tissues from healthy brain tissues. Image segmentation is incredibly important for the efficient extraction of properties as well as details from many kinds of multimodal images. Extraction of patient-specific critical clinical data and related diagnostic features is the principal objective of brain tumour imaging evaluation. Once an infection has been identified and localised, the data included in the multidimensional images can direct and track therapies, eventually resulting in expertise for the clinical assessment, illness stage, and intervention. One of the important concerns in the medical field is to secure the patient's personal and health information. In order to overcome this, a blockchain (BC) based healthcare system is adopted that enables safe, unintrusive, and rapid data transfer. Numerous experts in the fields of medical imaging and soft computing have previously conducted extensive research on the segmentation of brain tumours using both semi and fully automated techniques. In-depth literature reviews of current techniques for tumour segmentation and blockchain technology used for encrypted medical information transfer are addressed in this article. Modern techniques are used, and their performance and quantitative analysis are included. With the most current contributions from numerous researchers, various image segmentation techniques are briefly discussed. In this instance, an attempt is made to provide readers with fresh perspectives on the subject of the research.