“…It is highly commendable that the proposed MPSO‐based FCM with Region Growing algorithm outperforms the other competitive algorithms in terms of sensitivity. The average sensitivity value proposed by Chinnadurai and Chandrashekhar, (), ranges between 92.47 and 94.37. The proposed algorithm offers 96% as the average sensitivity value.…”
Tumor and Edema region present in Magnetic Resonance (MR) brain image can be segmented using Optimization and Clustering merged with seed-based region growing algorithm. The proposed algorithm shows effectiveness in tumor detection in T1 -w, T2 -w, Fluid Attenuated Inversion Recovery and Multiplanar Reconstruction type MR brain images. After an initial level segmentation exhibited by Modified Particle Swarm Optimization (MPSO) and Fuzzy C -Means (FCM) algorithm, the seed points are initialized using the region growing algorithm and based on these seed points; tumor detection in MR brain images is done. The parameters taken for comparison with the conventional techniques are Mean Square Error, Peak Signal to Noise Ratio, Jaccard (Tanimoto) index, Dice Overlap indices and Computational Time. These parameters prove the efficacy of the proposed algorithm. Heterogeneous type tumor regions present in the input MR brain images are segmented using the proposed algorithm. Furthermore, the algorithm shows augmentation in the process of brain tumor identification. Availability of gold standard images has led to the comparison of the suggested algorithm with MPSO-based FCM and conventional Region Growing algorithm. Also, the algorithm recommended through this research is capable of producing Similarity Index value of 0.96, Overlap Fraction value of 0.97 and Extra Fraction value of 0.05, which are far better than the values articulated by MPSO-based FCM and Region Growing algorithm. The proposed algorithm favors the segmentation of contrast enhanced images.
“…It is highly commendable that the proposed MPSO‐based FCM with Region Growing algorithm outperforms the other competitive algorithms in terms of sensitivity. The average sensitivity value proposed by Chinnadurai and Chandrashekhar, (), ranges between 92.47 and 94.37. The proposed algorithm offers 96% as the average sensitivity value.…”
Tumor and Edema region present in Magnetic Resonance (MR) brain image can be segmented using Optimization and Clustering merged with seed-based region growing algorithm. The proposed algorithm shows effectiveness in tumor detection in T1 -w, T2 -w, Fluid Attenuated Inversion Recovery and Multiplanar Reconstruction type MR brain images. After an initial level segmentation exhibited by Modified Particle Swarm Optimization (MPSO) and Fuzzy C -Means (FCM) algorithm, the seed points are initialized using the region growing algorithm and based on these seed points; tumor detection in MR brain images is done. The parameters taken for comparison with the conventional techniques are Mean Square Error, Peak Signal to Noise Ratio, Jaccard (Tanimoto) index, Dice Overlap indices and Computational Time. These parameters prove the efficacy of the proposed algorithm. Heterogeneous type tumor regions present in the input MR brain images are segmented using the proposed algorithm. Furthermore, the algorithm shows augmentation in the process of brain tumor identification. Availability of gold standard images has led to the comparison of the suggested algorithm with MPSO-based FCM and conventional Region Growing algorithm. Also, the algorithm recommended through this research is capable of producing Similarity Index value of 0.96, Overlap Fraction value of 0.97 and Extra Fraction value of 0.05, which are far better than the values articulated by MPSO-based FCM and Region Growing algorithm. The proposed algorithm favors the segmentation of contrast enhanced images.
“…The integration of multimodal imaging techniques has been investigated with increasing interest in recent studies and has shown promise in tumor differentiation, grading, accurate tumor extent definition and therapeutic response determination. For example, the integration of parameter maps such as ADC, FA and rCBV derived from diffusion and perfusion MRI data have been used to differentiate among GBMs, solitary brain metastases, and primary cerebral lymphomas [ 71 ] to serve as indicators of glioma proliferation [ 72 ], to predict tumor infiltration in patients with gliomas [ 59 ], to evaluate glioma grading and to segment tumor, edema, necrosis, CSF and normal tissues [ 73 ]. Fig.…”
Accurate target volume delineation is crucial for the radiotherapy of tumors. Diffusion and perfusion magnetic resonance imaging (MRI) can provide functional information about brain tumors, and they are able to detect tumor volume and physiological changes beyond the lesions shown on conventional MRI. This review examines recent studies that utilized diffusion and perfusion MRI for tumor volume definition in radiotherapy of brain tumors, and it presents the opportunities and challenges in the integration of multimodal functional MRI into clinical practice. The results indicate that specialized and robust post-processing algorithms and tools are needed for the precise alignment of targets on the images, and comprehensive validations with more clinical data are important for the improvement of the correlation between histopathologic results and MRI parameter images.
“…The apparent diffusion coefficient (ADC) is the characteristic that reflects the diffusion of water quantitatively. Numerous studies have indicated that functional MRI based on ADC value contrast can provide accurate and quantitative measures of neuronal activities, and has already been used in several functional MRI studies (7)(8)(9)(10)(11)(12)(13)(14)(15).…”
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