Abstract
Image processing is a crucial effort to discover suspicious regions and robust features from magnetic resonance imaging (MRI). Many segmentation algorithms have been proposed to increase the accuracy of brain tumor detection. However, brain image segmentation is considered a complex and challenging step in medical image processing. K-means is an unsupervised learning method for MRI image clustering and other tasks such as image segmentation and pattern identification of interest. However, the K-means algorithm still has many shortcomings, including a low convergence rate, a tendency to get stuck in local minima, and sensitivity to initialization. An improved version of Aquila Optimizer (AO) boosted with an alternative selection scheme and Cauchy mutation, called Mutated Aquila Optimizer (MAO), and the K-Means algorithm were combined to create a new dynamic and intelligent clustering method for brain tumor segmentation. The proposed MAO-Kmeans approach aims to increase the ability of K-Means to automatically extract the correct number and location of cluster centers and the number of pixels in each cluster in abnormal MRI images (multiple sclerosis lesions). The experimental results on the Brain Tumor Segmentation dataset (BraTS 2020) demonstrated the effectiveness of the MAO-Kmeans method in improving the performance of conventional K-means clustering. Furthermore, qualitative and quantitative comparisons between MAO-Kmeans and the other 12 state-of-the-art segmentation techniques (which include machine learning, artificial neural network, and deep learning-based methods) demonstrate the superiority of our proposed algorithm.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.