Literature DB >> 28716679

Modified-BRISQUE as no reference image quality assessment for structural MR images.

Li Sze Chow1, Heshalini Rajagopal2.   

Abstract

An effective and practical Image Quality Assessment (IQA) model is needed to assess the image quality produced from any new hardware or software in MRI. A highly competitive No Reference - IQA (NR - IQA) model called Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) initially designed for natural images were modified to evaluate structural MR images. The BRISQUE model measures the image quality by using the locally normalized luminance coefficients, which were used to calculate the image features. The modified-BRISQUE model trained a new regression model using MR image features and Difference Mean Opinion Score (DMOS) from 775 MR images. Two types of benchmarks: objective and subjective assessments were used as performance evaluators for both original and modified-BRISQUE models. There was a high correlation between the modified-BRISQUE with both benchmarks, and they were higher than those for the original BRISQUE. There was a significant percentage improvement in their correlation values. The modified-BRISQUE was statistically better than the original BRISQUE. The modified-BRISQUE model can accurately measure the image quality of MR images. It is a practical NR-IQA model for MR images without using reference images.
Copyright © 2017 Elsevier Inc. All rights reserved.

Keywords:  BRISQUE; Mean subtracted contrast normalized (MSCN); Modified-BRISQUE

Mesh:

Year:  2017        PMID: 28716679     DOI: 10.1016/j.mri.2017.07.016

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  9 in total

1.  MRI super-resolution via realistic downsampling with adversarial learning.

Authors:  Bangyan Huang; Haonan Xiao; Weiwei Liu; Yibao Zhang; Hao Wu; Weihu Wang; Yunhuan Yang; Yidong Yang; G Wilson Miller; Tian Li; Jing Cai
Journal:  Phys Med Biol       Date:  2021-10-05       Impact factor: 4.174

2.  A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images.

Authors:  Igor Stępień; Mariusz Oszust
Journal:  J Imaging       Date:  2022-06-04

3.  Knee Implant Identification by Fine-Tuning Deep Learning Models.

Authors:  Sukkrit Sharma; Vineet Batta; Malathy Chidambaranathan; Prabhakaran Mathialagan; Gayathri Mani; M Kiruthika; Barun Datta; Srinath Kamineni; Guruva Reddy; Suhas Masilamani; Sandeep Vijayan; Derek F Amanatullah
Journal:  Indian J Orthop       Date:  2021-09-28       Impact factor: 1.033

4.  Explainable artificial intelligence-based edge fuzzy images for COVID-19 detection and identification.

Authors:  Qinhua Hu; Francisco Nauber B Gois; Rafael Costa; Lijuan Zhang; Ling Yin; Naercio Magaia; Victor Hugo C de Albuquerque
Journal:  Appl Soft Comput       Date:  2022-05-13       Impact factor: 8.263

5.  Fully automated segmentation of the left atrium, pulmonary veins, and left atrial appendage from magnetic resonance angiography by joint-atlas-optimization.

Authors:  Menyun Qiao; Yuanyuan Wang; Floris F Berendsen; Rob J van der Geest; Qian Tao
Journal:  Med Phys       Date:  2019-03-22       Impact factor: 4.071

6.  Magnetic Resonance Image Quality Assessment by Using Non-Maximum Suppression and Entropy Analysis.

Authors:  Rafał Obuchowicz; Mariusz Oszust; Marzena Bielecka; Andrzej Bielecki; Adam Piórkowski
Journal:  Entropy (Basel)       Date:  2020-02-16       Impact factor: 2.524

7.  Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment.

Authors:  Igor Stępień; Rafał Obuchowicz; Adam Piórkowski; Mariusz Oszust
Journal:  Sensors (Basel)       Date:  2021-02-03       Impact factor: 3.576

8.  Comparison of color accuracy and picture quality of digital SLR, point and shoot and mobile cameras used for dental intraoral photography - A pilot study.

Authors:  Rishi Saincher; Santhosh Kumar; Pratibha Gopalkrishna; M Maithri; Pradeep Sherigar
Journal:  Heliyon       Date:  2022-04-07

9.  A consistency evaluation of signal-to-noise ratio in the quality assessment of human brain magnetic resonance images.

Authors:  Shaode Yu; Guangzhe Dai; Zhaoyang Wang; Leida Li; Xinhua Wei; Yaoqin Xie
Journal:  BMC Med Imaging       Date:  2018-05-16       Impact factor: 1.930

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.