ENHANCING BRAIN TUMOR SEGMENTATION AND AREA ESTIMATION USING HYBRID SHAFT CLUSTERING TECHNIQUES
DOI:
https://doi.org/10.48047/Keywords:
Brain Tumor Segmentation, Magnetic Resonance Images, Hybrid Shaft Clustering, Area Estimation, K-means, Fuzzy C-Means, Spatial Information, Quantitative Analysis.Abstract
A key component of quantitative brain image analysis is the accurate segmentation of brain tumors in magnetic resonance (MR) images, which has attracted a lot of scientific interest. Conventional techniques for segmenting MR brain images, such K-means and fuzzy C-Means (FCM) clustering algorithms, handle each pixel independently and do not integrate spatial information between nearby pixels. Because of the noise and intensity inhomogeneity in brain magnetic resonance imaging, these segmentation methods are
therefore subject to accuracy limits. We present a novel way to segmentation that tackles this problem by utilizing the hybrid shaft clustering method, which blends Fuzzy Kernel C-Means (FKCM) and adaptive K-means clustering. Furthermore, we estimate the area by figuring out how many cells the tumor occupies and how segmented its area is. The outcomes of our simulations show that, in comparison to traditional methods, our suggested strategy provides better segmentation and area estimation accuracy.
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References
Saba, Tanzila, et al. "Brain tumor detection using fusion of hand crafted and deep learning features." Cognitive Systems Research 59 (2020): 221-230.
Sharif, Muhammad, et al. "A unified patch based method for brain tumor detection using features fusion." Cognitive Systems Research 59 (2020): 273-286.
Toğaçar, Mesut, Burhan Ergen, and Zafer Cömert. "BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model." Medical Hypotheses 134 (2020): 109531.
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