07-05-2017, 01:03 AM
I am badly in need of this code for thesis.plz send me in refatjany2k12[at]gmail.com.
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brain image segmentaion using neural network matlab code using mri
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I am badly in need of this code for thesis.plz send me in refatjany2k12[at]gmail.com.
08-05-2017, 11:09 AM
Image segmentation plays a vital role in medical imaging. K-means modified Clustering is used for image segmentation. Clustering is the unsupervised popular technique. The modified K-means is to improve the effectiveness and efficiency for the segmentation of images. Medical imaging technique is most commonly used to visualize the internal structure and function of the body. Magnetic resonance imaging (MRI) provides a much greater contrast between different soft tissues of the body. The brain tumor is one of the serious diseases that causes death among people. The tumor is an uncontrolled growth of tissue in any part of the body. In this clustering method, magnetic resonance imaging is used to identify the brain tumor. The magnetic resonance image of the brain is given as input. The system must process the input image and detect the tumor. This method may even detect the slightest anomaly in the previous step. The different anomalies of magnetic resonance imaging of patients' brains are taken for processing.
Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy at its highest level. Therefore, treatment planning is a key stage in improving the quality of life of cancer patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to evaluate these tumors, but the large amount of MRI data prevents manual segmentation within a reasonable time, limiting the use of accurate quantitative measurements in clinical practice. Therefore, automatic and reliable segmentation methods are required; However, the large spatial and structural variability among brain tumors makes automatic segmentation a challenging problem. In this work we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 × 3 nuclei.
The use of small cores allows designing a deeper architecture, as well as having a positive effect against over execution, given the lower number of weights in the network. The use of normalization of intensity as a pre-processing step, which although not common in CNN-based segmentation methods, was demonstrated along with increased data to be highly efficient for segmentation of brain tumor imaging magnetic resonance. Our proposal was validated in the database of the Brain Tumour Segmentation Challenge 2013 (BRATS 2013), simultaneously obtaining the first position for complete regions, core and improvement in metric coefficient of similarity of data (0.88, 0.83 , 0.77) for the Challenge data set. In addition, it obtained the first global position by the platform of evaluation in line. We also participated in the BRATS 2015 Challenge on the site using the same model, obtaining the second place, with Data Likelihood Coefficient metric of 0.78, 0.65 and 0.75 for the complete, core and improvement regions , Respectively.
Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy at its highest level. Therefore, treatment planning is a key stage in improving the quality of life of cancer patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to evaluate these tumors, but the large amount of MRI data prevents manual segmentation within a reasonable time, limiting the use of accurate quantitative measurements in clinical practice. Therefore, automatic and reliable segmentation methods are required; However, the large spatial and structural variability among brain tumors makes automatic segmentation a challenging problem. In this work we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 × 3 nuclei.
The use of small cores allows designing a deeper architecture, as well as having a positive effect against over execution, given the lower number of weights in the network. The use of normalization of intensity as a pre-processing step, which although not common in CNN-based segmentation methods, was demonstrated along with increased data to be highly efficient for segmentation of brain tumor imaging magnetic resonance. Our proposal was validated in the database of the Brain Tumour Segmentation Challenge 2013 (BRATS 2013), simultaneously obtaining the first position for complete regions, core and improvement in metric coefficient of similarity of data (0.88, 0.83 , 0.77) for the Challenge data set. In addition, it obtained the first global position by the platform of evaluation in line. We also participated in the BRATS 2015 Challenge on the site using the same model, obtaining the second place, with Data Likelihood Coefficient metric of 0.78, 0.65 and 0.75 for the complete, core and improvement regions , Respectively.
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