mri image segmentation using neural networks matlab code free download
#1

pls send me the code.i am badly in need of this code for my study.
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#2
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. This work proposes an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 × 3 nuclei. The use of small nuclei allows to design a deeper architecture, besides having a positive effect against the overexecution, given the Lower number of weights in the network. We also investigated 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 Tumor 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.


Segmentation of the brain tumor is an important task in medical imaging. Early diagnosis of brain tumors plays an important role in improving the chances of treatment and increases the survival rate of patients. The manual segmentation of brain tumors for the diagnosis of cancer, from a large number of MRI images generated in the clinical routine, is a difficult and time-consuming task. There is a need for automatic segmentation of the brain tumor image. The purpose of this paper is to provide a review of the methods of segmentation of brain tumors based on MRI. Recently, automated segmentation using deep learning methods proved to be popular as these methods achieve the most advanced results and can address this problem better than other methods. Deep learning methods can also allow for efficient processing and objective evaluation of large amounts of MRI-based image data. There are a number of existing review documents, focusing on traditional methods for segmentation of MRI-based brain tumors. Different from others, in this article, we focus on the recent trend of deep learning methods in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Next, we discuss the latest generation algorithms with a focus on the recent trend of deep learning methods. Finally, an evaluation of the current state is presented and future developments are addressed to standardize the methods of segmentation of brain tumors based on MRI in the daily clinical routine.
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