Publicado en 3C Tecnología. Edición Especial/Special Issue – Noviembre/November 2021
To segment a tumefaction of brain images obtained from any of the imaging modalities is a lofty goal owing to the varied shape, locality and measure of tumor. This segmentation process can be done manually by a Doctor or otherwise can be done automatically using computer aided diagnosis. Self – Activated segmentation of brain tumor is nothing but the separation of the tissues that are not related with the tissues within the brain case akin to the regions with myelinated nerve fibers without dendrites, portions of nerve fibers with dendrites and the cerebrum area along with the cerebrospinal fluid (CSF). The various imaging modalities are the scans from the radioscopy with emitting positrons (PET), multiple X – rays (CT) and through Magnetic Resonance (MRI). In this paper, an overview of recent automatic brain tumor segmentation techniques of MRI and the advantages of multimodal imaging techniques has been explained. The segmentation techniques such as thresholding, edge based, morphology based, watershed, k means and markov random method are the conventional tactics of segmentation that are addressed. Also, the advanced segmentation methods such as region growing, genetic method, fuzzy clustering, deformation, atlas method and artificial neural network are also discussed. Moreover, the hybrid methods that have different combination of genetic algorithm, artificial neural networks and SVM are also considered. Among all the methods, the hybrid methods are found to be better as they provide the beneficiary factors of every method involved. But one should be aware about the algorithm’s robustness and accuracy.
Palabras claveTumor, Segmentation, Automatic, Multimodal Imaging, MRI Images.
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