Brain Tumor Segmentation by Texture Feature Extraction with the Parallel Implementation of Fuzzy C-Means using CUDA on GPU

Exact segmentation of the brain tumor is one of the imperative tasks in medical image processing and its analysis as it deals with extracting the information of the tumorous region from the brain MRI sequences. Automated segmentation and detection of brain tumors from the brain MRI is an exigent issue caused by the texture, size, shape, and location. In this paper, a significant method of brain tumor segmentation from the FLAIR MRI sequences is by classifying local window followed by parallel fuzzy c means clustering. Fuzzy c-means methods have shown their efficiency in extracting a variety of objects in several medical image processing applications. However, one of the major issues of these algorithms is high computational requirements at the time of dealing with large data set. Nowadays, NVIDIA's GPU plays an extremely essential role in implementing such time-consuming algorithms to reduce the time complexity. Our experiments based on NCI-MICCAI BRATS 2017 FLAIR MRI of HGG (High-Grade Glioma) demonstrate the efficiency of the implemented parallel algorithm. For the segmentation of tumorous region, a mechanism of sliding window is implemented on CPU (host) in which a 45 × 45 sized window is taken to classify whether that particular window is having tumor region or not. For perfect segmentation at the GPU (device) side, fuzzy c means technique is used to get the exact location of the tumor. Approx 17.6 speed up obtained, for the BRATS data sets over the implementation of the algorithm on CPU. Apart from speed up significant dice similarity coefficients are obtained which shows the efficient segmentation in the reasonable time.