Abstract
Early detection of a brain tumour is a challenging task and allows the doctors to reduce the death rate. A brain tumour is first diagnosed by doctors; this process takes more time and needs a lot of experience. The Magnetic resonance imaging (MRI) is primary diagnostic tool to detect the tumour from abnormal brain image at an early stage. An automated segmentation method is introduced in this research article for efficiently segmenting and extracting tumour from MRI images. Pre-processing, clustering, fusion, and segmentation are the four key phase's uses in the proposed method. After pre-processing, the images are clustered by using K-mans and fuzzy c-means (FCM) algorithms. The clustered images are embedded by using the convolution neural network (CNN) data fusion method to obtain high level tumor information of the brain tumor. The fused images are segmented by active contour methods such as chan-vese (C-V) and level set method (LSM) to detect the exact tumor region. All experiments are performed on two brain tumour datasets: BRATS 2017 and Brain Web. The performance of the proposed flow work is measured by various similarity metrics. The Segmentation after fusion gives better results than segmentation alone. Overall, the CNN fusion-based C-V segmentation outperforms than LSM in order to detect the tumor from T1w brain MRI images with minimal loss of information.
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.