An effective no-reference image quality index prediction with a hybrid Artificial Intelligence approach for denoised MRI images

• Published in: BMC medical imaging Vol. 24; no. 1; pp. 208 - 19
• Main Authors: Radhabai, Prianka Ramachandran, KVN, Kavitha, Shanmugam, Ashok, Imoize, Agbotiname Lucky
• Format: Journal Article
• Language: English
• Published: London BioMed Central 12.08.2024; BioMed Central Ltd; BMC
• Subjects: Algorithms; Artificial Intelligence; Classification; Correlation coefficient; Correlation coefficients; Datasets; Deep learning; Design; Digital imaging; Drug resistance; Error analysis; Feature selection; Fuzzy Logic; Graphical representations; Humans; Hybridization; Image Processing, Computer-Assisted - methods; Image quality; Image Quality Assessment; Imaging; Magnetic Resonance Images; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Measurement techniques; Medical imaging; Medical imaging equipment; Medicine; Medicine & Public Health; Methods; Multi-objective optimization; Neural networks; Neural Networks, Computer; Noise reduction; Performance metrics; Quality assessment; Quality control; Quality management; Radiology; Reptiles; Root-mean-square errors; Search algorithms; Semantics; Signal-To-Noise Ratio; Performance metrics; Multi-objective optimization; Hybridization; Magnetic Resonance Images; Artificial Intelligence; Image Quality Assessment
• ISSN: 1471-2342; 1471-2342
• DOI: 10.1186/s12880-024-01387-1

As the quantity and significance of digital pictures in the medical industry continue to increase, Image Quality Assessment (IQA) has recently become a prevalent subject in the research community. Due to the wide range of distortions that Magnetic Resonance Images (MRI) can experience and the wide variety of information they contain, No-Reference Image Quality Assessment (NR-IQA) has always been a challenging study issue. In an attempt to address this issue, a novel hybrid Artificial Intelligence (AI) is proposed to analyze NR-IQ in massive MRI data. First, the features from the denoised MRI images are extracted using the gray level run length matrix (GLRLM) and EfficientNet B7 algorithm. Next, the Multi-Objective Reptile Search Algorithm (MRSA) was proposed for optimal feature vector selection. Then, the Self-evolving Deep Belief Fuzzy Neural network (SDBFN) algorithm was proposed for the effective NR-IQ analysis. The implementation of this research is executed using MATLAB software. The simulation results are compared with the various conventional methods in terms of correlation coefficient (PLCC), Root Mean Square Error (RMSE), Spearman Rank Order Correlation Coefficient (SROCC) and Kendall Rank Order Correlation Coefficient (KROCC), and Mean Absolute Error (MAE). In addition, our proposed approach yielded a quality number approximately we achieved significant 20% improvement than existing methods, with the PLCC parameter showing a notable increase compared to current techniques. Moreover, the RMSE number decreased by 12% when compared to existing methods. Graphical representations indicated mean MAE values of 0.02 for MRI knee dataset, 0.09 for MRI brain dataset, and 0.098 for MRI breast dataset, showcasing significantly lower MAE values compared to the baseline models.