TY - JOUR ID - 161754 TI - Improving the Accuracy of Brain Tumor Identification in Magnetic Resonanceaging using Super-pixel and Fast Primal Dual Algorithm JO - International Journal of Engineering JA - IJE LA - en SN - 1025-2495 AU - Emadi, M. AU - Jafarian Dehkordi, Z. AU - Iranpour Mobarakeh, M. AD - Faculty of Electrical Engineering, Mobarakeh Branch, Islamic Azad University, Mobarakeh, Isfahan, Iran AD - Faculty of Software Engineering, Najaf Abad Branch, Islamic Azad University, Najaf Abad, Isfahan, Iran AD - Department of Computer Engineering and IT, Payam Noor University, Tehran, Iran Y1 - 2023 PY - 2023 VL - 36 IS - 3 SP - 505 EP - 512 KW - Brain tumor identification KW - Fast Primal Dual Algorithm KW - Markov random fields KW - MRI imaging KW - Segmentation DO - 10.5829/ije.2023.36.03c.10 N2 - Brain tumors are one of the most common causes of death that have been widely investigated by scholars in research areas, including care and prevention. Despite various empirical studies on the brain tumor segmentatin, there is still a need for further investigation. This fact is more needed in the automatic methods of brain tumors detection. In the present study, a new method for improving brain tumor segmentation accuracy based on super-pixel and fast primal dual (PD) algorithms has been proposed. The proposed method detects brain tumor tissue in Flair-MRI imaging in BRATS2012 dataset. This method detects the primary borders of tumors using a super-pixel algorithm, and improves brain tumor borders using fast PD in Markov random field optimization. Then, post-processing processes are used to delete white brain areas. Finally, an active contour algorithm was employed to display tumor area. Different experiments were carried on the proposed method and qualitative and quantitative criteria such as dice similarity measure, accuracy and F-measure were used for evaluation. The obtained results showed the efficiency of the proposed method, such that in the accuracy and sensitivity of 86.59 and 88.57%  and F1-Measure 86.37 were obtained, respectively. UR - https://www.ije.ir/article_161754.html L1 - https://www.ije.ir/article_161754_ed4c1af855306abc44c3744ed8c53359.pdf ER -