Magnetic Resonance Imaging Image Segmentation and Brain Tumour Detection Using Pulse-Coupled Neural Networks

Image processing can be defined as a functional structure to correct and change the images viewed and their interpretation. One of the applications of digital image processing is using image processing techniques in the component and image segmentation. One of these techniques is magnetic resonance imaging (MRI) in the medical world. In this article, a brain tumour detection system and various anomalies and abnormalities are presented where image pre-processing and preparation include image enhancement, filtering and noise reduction. Then image segmentation is done by a pulse neural network. Next, the image features are extracted and finally, the tumour and abnormal area are separated from the normal area by the algorithms. In this research, the feature selection and integration method are used and the most important statistical features of brain MRI images are used to improve brain tumour detection. Along with the studies done and the implementation of tumour detection systems, the following suggestions can be provided for future researches and the tumour detection system will work more efficiently. The pulse-coupled neural network (PCNN) can be used for image segmentation in the pre-processing stage, especially in the image filtering.