This study analyzes and compares the performance of five denoising techniques (Lee filter, gamma filter, principal component analysis, maximum noise fraction, and wavelet transform) in order to identify the most appropriate one that lead to the most accurate classification of burned tissue in hyperspectral images. Fifteen hyperspectral images of burned patients were acquired and denoising techniques were applied to each image. Spectral angle mapper classifier was used for data classification and the confusion matrix was used for quantitative evaluation of the performances of the denoising methods. The results revealed that gamma filter performed better than other denoising techniques with values of overall accuracy and kappa coefficient of 91.18% and 0.8958 respectively. The lowest performance was detected for principal component analysis. In conclusion, the gamma filter could be considered an optimal choice for noise reduction in burn hyperspectral images and could be used for a more accurate diagnosis of burn depth.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1002/jbio.202200374 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Evaluating the effect of noise reduction strategies in CT perfusion imaging for predicting infarct core with deep learning.
Neuroradiol J
January 2025
Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, NY, USA.
This study evaluates the efficacy of deep learning models in identifying infarct tissue on computed tomography perfusion (CTP) scans from patients with acute ischemic stroke due to large vessel occlusion, specifically addressing the potential influence of varying noise reduction techniques implemented by different vendors. We analyzed CTP scans from 60 patients who underwent mechanical thrombectomy achieving a modified thrombolysis in cerebral infarction (mTICI) score of 2c or 3, ensuring minimal changes in the infarct core between the initial CTP and follow-up MR imaging. Noise reduction techniques, including principal component analysis (PCA), wavelet, non-local means (NLM), and a no denoising approach, were employed to create hemodynamic parameter maps.
View Article and Find Full Text PDFJoint k-b space diffusion-weighted image reconstruction and apparent diffusion coefficient fitting for diffusion-weighted turbo-spin-echo imaging.
Med Phys
January 2025
Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Background: Diffusion-weighted (DW) turbo-spin-echo (TSE) imaging offers improved geometric fidelity compared to single-shot echo-planar-imaging (EPI). However, it suffers from low signal-to-noise ratio (SNR) and prolonged acquisition times, thereby restricting its applications in diagnosis and MRI-guided radiotherapy (MRgRT).
Purpose: To develop a joint k-b space reconstruction algorithm for concurrent reconstruction of DW-TSE images and the apparent diffusion coefficient (ADC) map with enhanced image quality and more accurate quantitative measurements.
Convergent-Diffusion Denoising Model for multi-scenario CT Image Reconstruction.
Comput Med Imaging Graph
January 2025
The Department of Computer and Data Science, Case Western Reserve University, Cleveland, OH, USA; The Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
A generic and versatile CT Image Reconstruction (CTIR) scheme can efficiently mitigate imaging noise resulting from inherent physical limitations, substantially bolstering the dependability of CT imaging diagnostics across a wider spectrum of patient cases. Current CTIR techniques often concentrate on distinct areas such as Low-Dose CT denoising (LDCTD), Sparse-View CT reconstruction (SVCTR), and Metal Artifact Reduction (MAR). Nevertheless, due to the intricate nature of multi-scenario CTIR, these techniques frequently narrow their focus to specific tasks, resulting in limited generalization capabilities for diverse scenarios.
View Article and Find Full Text PDFBackground: Mild Cognitive Impairment (MCI) represents an intermediate stage between normal age‐related cognitive decline and more severe degenerative conditions such as Alzheimer's disease. Understanding the differences between Early‐MCI (EMCI) and Late‐MCI (LMCI) is crucial to facilitate early diagnosis and future clinical interventions. This study employed free‐water diffusion tensor imaging (FW‐DTI) to explore the differences in white matter alterations between EMCI and LMCI.
View Article and Find Full Text PDFInvestigating the role of gut microbiome in alcohol consumption‐associated alterations to cognitive function and biomarkers of Alzheimer’s disease and related dementias.
Alzheimers Dement
December 2024
Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
Background: Heavy alcohol consumption is associated with increased risk for Alzheimer’s disease and related dementias (ADRD), with mixed evidence suggesting a dose‐dependent nonlinear effect of alcohol on ADRD. Potential mechanisms by which alcohol may promote or attenuate brain pathology need further exploration. Although chronic alcohol consumption associates with gut microbiome alterations, it remains unclear whether microbial alterations mediate alcohol‐associated neurodegeneration and cognitive decline.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!