Syed M. S. Reza

TL;DR: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) as mentioned in this paper was organized in conjunction with the MICCAI 2012 and 2013 conferences, and twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low and high grade glioma patients.

TL;DR: This paper proposes a common evaluation framework for automatic stroke lesion segmentation from MRIP, describes the publicly available datasets, and presents the results of the two sub‐challenges: Sub‐Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES).

TL;DR: Comparison with other state-of-the art brain tumor segmentation works with publicly available low-grade glioma BRATS2012 dataset show that the segmentation results are more consistent and on the average outperforms these methods for the patients where ground truth is made available.

TL;DR: In this article, a novel non-invasive brain tumor type classification using Multi-fractal Detrended Fluctuation Analysis (MFDFA) in structural magnetic resonance (MR) images is proposed.

TL;DR: The novelty of this work is to exploit tumor cell density as a feature to predict brain tumor segmentation, using a stochastic multi-resolution RF-based method, and improve the performance of another state-of-the-art tumor growth and segmentation method, GB, by fusing with theRF-based segmentation labels.