Name
Title | ||
|---|---|---|
Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm. |
Abstract | ||
|---|---|---|
High-resolution three-dimensional magnetic resonance imaging (3D-MRI) is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM) algorithm in the quality assessment of structural brain images, using global and region of interest (ROI) automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy) of the automated SVM approach was assessed, by comparing the SVM predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3389/fninf.2016.00052 | FRONTIERS IN NEUROINFORMATICS |
Keywords | Field | DocType |
structural magnetic resonance imaging,database management,automated quality assessment,machine learning,support vector machine,artifact detection,region of interest | Data mining,Visual inspection,Structural magnetic resonance imaging,Computer science,Support vector machine,Image quality,Algorithm,Artificial intelligence,Type I and type II errors,Region of interest,Machine learning,Magnetic resonance imaging | Journal |
Volume | ISSN | Citations |
10 | 1662-5196 | 2 |
PageRank | References | Authors |
0.43 | 5 | 12 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ricardo Pizarro | 1 | 5 | 1.28 |
| Xi Cheng | 2 | 2 | 0.43 |
| Alan S. Barnett | 3 | 54 | 3.79 |
| H Lemaitre | 4 | 32 | 2.47 |
| Beth A Verchinski | 5 | 9 | 1.45 |
| Aaron L. Goldman | 6 | 2 | 0.43 |
| Ena Xiao | 7 | 2 | 0.43 |
| Qian Luo | 8 | 2 | 0.43 |
| Karen Faith Berman | 9 | 11 | 3.26 |
| Joseph H Callicott | 10 | 27 | 3.98 |
| Daniel R. Weinberger | 11 | 419 | 48.41 |
| Venkata S. Mattay | 12 | 87 | 19.18 |