B. Uma Shankar

TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.

TL;DR: 3D-Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors and can be employed for quantitative image feature extraction and image data mining research in large patient cohorts.

TL;DR: The problem of scarcity of labeled pixels, required for segmentation of remotely sensed satellite images in supervised pixel classification framework, is addressed in this article and a support vector machine (SVM) is considered for classifying the pixels into different landcover types.

TL;DR: Methods of selecting the appropriate granule size and efficient computation of rough entropy are described, which results in minimization of roughness in both object and background regions; thereby determining the threshold of partitioning.

TL;DR: Among all the techniques, fuzzy correlation, followed by fuzzy entropy, performed better for extracting the structures, and fuzzy geometry based thresholding algorithms produced a single stable threshold for a wide range of membership variation.