Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality
| Autor: | K. Arvind Karthik, Venkatesan Rajinikanth, C. Emmanuel, Hong Lin, Luminita Moraru, Fuqian Shi, K. Kamalanand, Nilanjan Dey, João Manuel R. S. Tavares |
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| Přispěvatelé: | Faculdade de Engenharia |
| Rok vydání: | 2019 |
| Předmět: |
Active contour model
Adaptive neuro fuzzy inference system Computer science business.industry 0206 medical engineering Ciências médicas e da saúde Biomedical Engineering Brain tumor Pattern recognition 02 engineering and technology Fluid-attenuated inversion recovery medicine.disease 020601 biomedical engineering Ciências Tecnológicas Ciências médicas e da saúde Technological sciences Medical and Health sciences Medical and Health sciences 0202 electrical engineering electronic engineering information engineering Medical imaging Brain mri medicine 020201 artificial intelligence & image processing Segmentation Artificial intelligence business Classifier (UML) |
| Zdroj: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP |
| ISSN: | 0208-5216 |
| DOI: | 10.1016/j.bbe.2019.07.005 |
| Popis: | Brain tumor is one of the harsh diseases among human community and is usually diagnosed with medical imaging procedures. Computed-Tomography (CT) and Magnetic-Resonance-Image (MRI) are the regularly used non-invasive methods to acquire brain abnormalities for medical study. Due to its importance, a significant quantity of image assessment and decision-making procedures exist in literature. This article proposes a two-stage image assessment tool to examine brain MR images acquired using the Flair and DW modalities. The combination of the Social-Group-Optimization (SGO) and Shannon’s-Entropy (SE) supported multi-thresholding is implemented to pre-processing the input images. The image post-processing includes several procedures, such as Active Contour (AC), Watershed and region-growing segmentation, to extract the tumor section. Finally, a classifier system is implemented using ANFIS to categorize the tumor under analysis into benign and malignant. Experimental investigation was executed using benchmark datasets, like ISLES and BRATS, and also clinical MR images obtained with Flair/DW modality. The outcome of this study confirms that AC offers enhanced results compared with other segmentation procedures considered in this article. The ANFIS classifier obtained an accuracy of 94.51% on the used ISLES and real clinical images. |
| Databáze: | OpenAIRE |
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