Abstract
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.
Highlights
With the dramatic increase in the volume of information and advances in medicine, there is a growing need for methods and techniques that can provide data efficiently and extract information
Segmentation of brain tumours and surrounding abnormal tissues based on magnetic resonance imaging (MRI) images can provide doctors with a direct understanding of tumours and assistance for analysis and treatment
The proposed technique in El-Dahshan et al.,[26] based on pulsecoupled neural network (PCNN) for segmentation, discrete wavelet transform (DWT) for feature extraction phase, has used principal component analysis (PCA) algorithm to reduce the dimension and the classification is done on the data
Summary
With the dramatic increase in the volume of information and advances in medicine, there is a growing need for methods and techniques that can provide data efficiently and extract information. MRI is widely used for the diagnosis of brain tumours.[2] Segmentation of brain tumours and surrounding abnormal tissues based on MRI images can provide doctors with a direct understanding of tumours and assistance for analysis and treatment. PCNN is a biological model based on the mammalian visual nucleus proposed by Eckhorn et al.[11] The proposed model can be used as an analysis and evaluation software for brain tumour detection. The purpose of this project is to process MRI images to diagnose brain tumours.
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