Fully automatic 3D segmentation of the thoracolumbar spinal cord and the vertebral canal from T2-weighted MRI using K-means clustering algorithm

Abstract

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Effect of Minimally Invasive Puncture Drainage and Conservative Treatment on Prognosis of Patients with Cerebral Hemorrhage

“…The basic principle is to use Euclidean distance to cluster the samples in the space as the cluster center and update the values of each center step by step through continuous iterative method, so as to output the best results. It has been found that the fuzzy K -means clustering algorithm can effectively divide the gray matter, cerebrospinal fluid, and white matter in the magnetic resonance brain image [ 21 ]. It is found that, compared with the two-dimensional maximum entropy algorithm, the K -means clustering algorithm has high quality and short average segmentation time for CT images of patients with cerebral hemorrhage.…”

A hybrid Hausdorff distance track correlation algorithm based on time sliding window

“…The random error of radar A obeys the Gaussian distribution with mean value of 0 and variance of 2500m 2 . The random error of radar B follows a Gaussian distribution with a mean of 0 and a variance of 3600m 2 . Radar A and B detected data once every 1s, Radar A is started 0.2 seconds later than radar B, randomly generated track data and conducted 500 Monte Carlo simulation experiments.…”

“…Hausdorff distance can be used to measure the similarity of two sets, which is a measurement method of set distance. Hausdorff distance has been applied to power fault troubleshooting, point cloud data, medical measurement, image segmentation, vehicle trajectory recognition and other directions [1][2] , and rich results have been achieved. In this paper, Hausdorff distance is applied to the multi-sensor track association problem, to compare the similarity degree between different track sets and determine whether the track is related or not [3] .…”

Evaluation and Optimization of Motion Correction in Spinal Cord fMRI Preprocessing

“…In the fMRI studies, physiological noise is described as cardiac and respiratory-related changes in the signal which can influence the analysis of fMRI data. This includes the motion of the spinal cord due to cerebrospinal fluid (CSF) pulsation and magnetic field fluctuations due to movement of lungs and changes in the air volume in the thorax and lungs (Dehghani et al, 2020). In order to minimize the effect of physiological noise, we followed a two-step correction protocol:…”

“…Our results in this study depend critically on the quality of MR images. Quality of MR images relies on some parameters such as image resolution (matrix, field of view, slice thickness), region of interest in imaging procedure, can affect the spinal cord fMRI preprocessing steps like co-registration and segmentation (Sabaghian, Dehghani, Batouli, Khatibi, & Oghabian, 2020). The field of view and orientation of raw-data was axial and all of preprocessing steps are dedicated to axially oriented raw-data.…”