Brain Tumor Segmentation by Level-Set and Chan-Vese Methods using different Fusion Approaches

Early diagnosis of a brain tumor may increase life expectancy. Magnetic resonance imaging (MRI) accompanied by several segmentation algorithms is preferred as a reliable method for assessment. This manuscript presented the performance analysis of clustered based and fusion-based segmentation techniques intended to detect the tumor from human brain MRI images in an efficient manner. Four primary steps are involved in this work such as pre-processing, clustering, segmentation, and fusion techniques. The main clustering methods such as K-means and fuzzy c-means (FCM) were first applied to the pre-processed MRI images, and then, the clustered images were segmented directly using the active contour segmentation techniques such as chan-vese (C-V) and level set method (LSM). Then in the next step, the clustered images were fused by using the non-sub sampled contour transform (NSCT) and convolution neural network (CNN) fusion methods, and then, the fused images were segmented by using the C-V and LSM segmentation methods again. The results of both clustered based and fusion-based segmentation in terms of structural similarity index measure (SSIM), dice coefficient (DC), computational time, sensitivity, precision, and segmentation accuracy revealed that CNN fusion-based C-V segmentation performs better than without fusion (clustered based or direct segmentation) to detect the tumors from the MRI images. The results indicate that C-V performs better with CNN as compared with the LSM. Finally, the fusion-based segmentation is an efficient approach to detect the tumor from the MRI images with minimal information loss and high segmentation accuracy over the clustered based segmentation.

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Brain Tumor Segmentation by Level-Set and Chan-Vese Methods using different Fusion Approaches

Cited by 4 publications

References 10 publications

An automated brain tumor detection and classification from MRI images using machine learning techniques with IoT

An automated brain tumor detection and classification from MRI images using machine learning techniques with IoT

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