Blind/Referenceless Image Spatial Quality Evaluator Research Articles

Deep learning with adaptive convolutions for classification of retinal diseases via optical coherence tomography

Optical coherence tomography (OCT) uses interferometry to capture high-resolution cross-sectional images of the retina to diagnose retinal diseases. Convolutional neural networks (CNNs) have become essential for developing efficient computer-aided diagnostic algorithms, but noisy images can hinder their performance. This study introduces an innovative image preprocessing strategy that involves a new method of representing images to reduce image noise and a new adaptive convolution layer. The adaptive convolution layer aims to replace traditional convolution layers for OCT image classification by relying on local’Feature Content’. The proposed image representation is based on Zeckendorf's theorem, which states that every positive integer may be split into a unique sum of distinct, non-adjacent Fibonacci numbers. The proposed approach enables the generation of two separate images, known as the ‘base’ and ‘fine,’ where the ‘base’ image is the denoised image. We assessed our methodology by evaluating against ten filters, comprising a Low-pass filter, Gaussian filter, Wiener filter, Wavelet filter, Guided filter, Lee filter, Frost filter, Kuan filter, Detail Preserving Anisotropic Diffusion (DPAD) filter, and Non-Local Means (NLM) filter. In our study, we found that the proposed filter produced the most favorable results in five of the six no-reference parameters (Blur Percent (BP), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), Naturalness Image Quality Evaluator (NIQE), Wavelet Variance (WAVV), Wavelet Variance Radial (WAVR)) used to assess the effectiveness of the proposed image enhancement technique. The OCT dataset utilized in the study was compiled by the University of California San Diego. The proposed adaptive convolution layer and its accompanying activation function were tested using seven OCT image classification CNN architectures. The test architectures comprise OctNET, NT-CNN, AOCT-NET, M-CNN, LightOCT, RetiNet, and DeepOCT. Experiments were conducted to assess the impact of the new preprocessing algorithm and the placement of the adaptive convolution layer as a substitute for the conventional convolution layer. Implementing the proposed approaches resulted in accuracy improvements ranging from 0.44% to 2.44% across architectures. Our findings highlight the efficacy of the proposed indirect noise reduction technique and a texture-sensitive adaptive convolution layer.

DL-EDOF: Novel Multi-Focus Image Data Set and Deep Learning-Based Approach for More Accurate and Specimen-Free Extended Depth of Focus.

Depth of focus (DOF) is defined as the axial range in which the specimen stage moves without losing focus while the imaging apparatus remains stable. It may not be possible to capture an image that includes the entire specimen in focus due to the narrow DOF in microscopic systems. Extended depth of focus (EDOF) is used to overcome this limitation in microscopic systems. Although the researchers have developed so many EDOF microscope approaches, this research field still has some crucial shortcomings such as high computational costs, complexity and execution time, requiring additional equipment, low precise characterization of curves, and edges in images, varying performance depending on the specimen and microscope, using only gray levels of input images to acquire the pixel's focus values. In order to minimize these shortcomings and comprehensively analyze the performance of EDOF approaches, a novel multi-focus image data set is generated, and a deep learning-based EDOF microscope approach is proposed in this study. When compared with the state-of-art EDOF approaches, our study provides various crucial contributions such as the first EDOF approach based on unsupervised deep learning, providing more accurate and specimen-free EDOF, generating a novel multi-focus image data, not requiring any pre- or post-processing technique and acquiring the pixel's focus degrees using deep features. In order to evaluate the effectiveness of the suggested approach, 20 different EDOF approaches are applied to a multi-focus image data set containing 9 image collections (4 synthetic and 5 microscope image collections) in total. Performance analysis metrics with and without requiring a reference image are preferred to identify which EDOF microscope approach can extract more essential details from the multi-focus images for the synthetic and microscope image collections, which are Root Mean Square Error (RMSE), Peak Signal Noise Ratio (PSNR), Universal Quality Index (UQI), Correlation Coefficient (CC), Perception-based Image Quality Evaluator (PIQE), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), Extension of Universal Quality Index for N Images (UQIN), and Naturalness Image Quality Evaluator (NIQE). Objective and subjective analysis of this study demonstrates that unsupervised deep learning model is more efficient to transmit crucial details from multi-focus images. Moreover, the suggested EDOF microscope approach with highest PSNR, UQI, CC, UQIN and lowest RMSE, PIQE, BRISQUE, NIQE produces higher performance than the state-of-art approaches.

Parametric image-based concrete defect assessment method

Structural health monitoring aims to ensure the integrity of infrastructure. Assessing structural integrity through image classification techniques based on human perception is often challenging. Incorporating a morphological image-based crack detection algorithm can mitigate these limitations. This study proposes a parametric approach for concrete crack analysis using digital image processing techniques. The objective is to detect damage to the concrete surface of infrastructures, potentially exacerbating structural deterioration. The research justifies the utilization of digital parametric image processing techniques, including a weighted median filter, grayscale, Otsu filter, and other methods. These techniques were applied to evaluate different concrete images for crack detection. The findings were validated through Image Quality Assessment (IQA), determining the condition of the studied images and statistical properties using metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), Natural Image Quality Evaluator (NIQE), and Perceptual Image Quality Evaluator (PIQE). Additionally, uncontrollable factors such as variations in lighting effects under different conditions, testing setups, and different textures of the concrete were considered in this study. The comprehensive experimental results demonstrated that the Otsu filter outperforms other filtering techniques. Overall, the study achieved remarkable accuracy, approximately 95%, in detecting cracks on building infrastructure. The proposed method holds potential for integration with advanced techniques in practical applications for the maintenance and safety of infrastructure. This research not only contributes to technological advancements in concrete defect assessment but also carries significant implications for the future of automated and reliable structural health monitoring.

Hierarchical deconvolution dehazing method based on transmission map segmentation.

Images captured in fog are often affected by scattering. Due to the absorption and scattering of light by aerosols and water droplets, the image quality will be seriously degraded. The specific manifests are brightness decrease, contrast decrease, image blur, and noise increase. In the single-image dehazing method, the image degradation model is essential. In this paper, an effective image degradation model is proposed, in which the hierarchical deconvolution strategy based on transmission map segmentation can effectively improve the accuracy of image restoration. Specifically, the transmission map is obtained by using the dark channel prior (DCP) method, then the transmission histogram is fitted. The next step is to divide the image region according to the fitting results. Furthermore, to more accurately recover images of complex objects with a large depth of field, different levels of inverse convolution are adopted for different regions. Finally, the sub-images of different regions are fused to get the dehazing image. We tested the proposed method using synthetic fog images and natural fog images respectively. The proposed method is compared with eight advanced image dehazing methods on quantitative rating indexes such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM), image entropy, natural image quality evaluator (NIQE), and blind/referenceless image spatial quality evaluator (BRISQUE). Both subjective and objective evaluations show that the proposed method achieves competitive results.

Improvement of 2D cine image quality using 3D priors and cycle generative adversarial network for low field MRI-guided radiation therapy.

Cine magnetic resonance (MR) images have been used for real-time MR guided radiation therapy (MRgRT). However, the onboard MR systems with low-field strength face the problem of limited image quality. To improve the quality of cine MR images in MRgRT using prior image information provided by the patient planning and positioning MR images. This study employed MR images from 18 pancreatic cancer patients who received MR-guided stereotactic body radiation therapy. Planning 3D MR images were acquired during the patient simulation, and positioning 3D MR images and 2D sagittal cine MR images were acquired before and during the beam delivery, respectively. A deep learning-based framework consisting of two cycle generative adversarial networks (CycleGAN), Denoising CycleGAN and Enhancement CycleGAN, was developed to establish the mapping between the 3D and 2D MR images. The Denoising CycleGAN was trained to first denoise the cine images using the time domain cine image series, and the Enhancement CycleGAN was trained to enhance the spatial resolution and contrast by taking advantage of the prior image information from the planning and positioning images. The denoising performance was assessed by signal-to-noise ratio (SNR), structural similarity index measure, peak SNR, blind/reference-less image spatial quality evaluator (BRISQUE), natural image quality evaluator, and perception-based image quality evaluator scores. The quality enhancement performance was assessed by the BRISQUE and physician visual scores. In addition, the target contouring was evaluated on the original and processed images. Significant differences were found for all evaluation metrics after Denoising CycleGAN processing. The BRISQUE and visual scores were also significantly improved after sequential Denoising and Enhancement CycleGAN processing. In target contouring evaluation, Dice similarity coefficient, centroid distance, Hausdorff distance, and average surface distance values were significantly improved on the enhanced images. The whole processing time was within 20ms for a typical input image size of 512 × 512. Taking advantage of the prior high-quality positioning and planning MR images, the deep learning-based framework enhanced the cine MR image quality significantly, leading to improved accuracy in automatic target contouring. With the merits of both high computational efficiency and considerable image quality enhancement, the proposed method may hold important clinical implication for real-time MRgRT.

Practical study for comparing edge detection filters in digital image processing

Edge detection is a fundamental and important tool in image processing and computer vision. The research topic discusses edge extraction of a digital image using various digital image processing techniques. Digital image edge identification using variety of digital image processing methods. The most popular technique for identifying discontinuities in intensity levels is edge detection. The actual image contains noise that could affect the digital image's quality. Some edge detection filters were used such as Canny, Sobel, and Prewitt. Laplace of Gaussian (LOG) edge recognition, Robert boundary detection, zero edge recognition and analysis. In light of the results of the comparison of edge filters the best edge filter had been obtained. Also, a hybrid method that merges between Canny filter and morphology operations were produced for edge detection. The research aims to reach an appropriate image purification by using different types of filters and to reduce distortions at the edges of the image and to determine which type of filter is the best. As well as, a comparison operation is applied between the traditional method and the hybrid method. Following the results explained by the filters used and displaying the resulting images. The results evaluated from hybrid filter show that this method is optimal than the traditional filters based on the Blind/Reference less Image Spatial Quality Evaluator (BRISQUE) with a value equals 43.43. A smaller score indicates better perceptual quality. Also, the edged images quality consequences from the traditional filters were measured by this metric. The results produced from the traditional filters prove that the best filter for edge image detection is Canny filter based on Blind/Reference less Image Spatial Quality Evaluator (BRISQUE) that reaches 46.01. In addition, the correlation coefficient was estimated to find which of the resulting images are better and closer to the original image. While for correlation coefficient the better filter was Prewitt filter with 0.3133 value. This study program was applied under the MATLAB R2020b system.

The impact of deep learning reconstruction in low dose computed tomography on the evaluation of interstitial lung disease.

To evaluate the effect of the deep learning model reconstruction (DLM) method in terms of image quality and diagnostic agreement in low-dose computed tomography (LDCT) for interstitial lung disease (ILD), 193 patients who underwent LDCT for suspected ILD were retrospectively reviewed. Datasets were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction Veo (ASiR-V), and DLM. For image quality analysis, the signal, noise, signal-to-noise ratio (SNR), blind/referenceless image spatial quality evaluator (BRISQUE), and visual scoring were evaluated. Also, CT patterns of usual interstitial pneumonia (UIP) were classified according to the 2022 idiopathic pulmonary fibrosis (IPF) diagnostic criteria. The differences between CT images subjected to FBP, ASiR-V 30%, and DLM were evaluated. The image noise and BRISQUE scores of DLM images was lower and SNR was higher than that of the ASiR-V and FBP images (ASiR-V vs. DLM, p < 0.001 and FBP vs. DLR-M, p < 0.001, respectively). The agreement of the diagnostic categorization of IPF between the three reconstruction methods was almost perfect (κ = 0.992, CI 0.990-0.994). Image quality was improved with DLM compared to ASiR-V and FBP.

An efficient multi-level pre-processing algorithm for the enhancement of dermoscopy images in melanoma detection.

In this paper, a multi-level algorithm for pre-processing of dermoscopy images is proposed, which helps in improving the quality of the raw images, making it suitable for skin lesion detection. This multi-level pre-processing method has a positive impact on automated skin lesion segmentation using Regularized Extreme Learning Machine. Raw images are subjected to de-noising, illumination correction, contrast enhancement, sharpening, reflection removal, and virtual shaving before the skin lesion segmentation. The Non-Local Means (NLM) filter with lowest Blind Reference less Image Spatial Quality Evaluator (BRISQUE) score exhibits better de-noising of dermoscopy images. To suppress uneven illumination, gamma correction is subjected to the denoised image. The Robust Image Contrast Enhancement (RICE) algorithm is used for contrast enhancement, and produces enhanced images with better structural preservation and negligible loss of information. Unsharp masking for sharpening exhibits low BRISQUE scores for better sharpening of fine details in an image. Output images produced by the phase congruency-based method in virtual shaving show high similarity with ground truth images as the hair is removed completely from the input images. Obtained scores at each stage of pre-processing framework show that the performance is superior compared to all the existing methods, both qualitatively and quantitatively, in terms of uniform contrast, preservation of information content, removal of undesired information, and elimination of artifacts in melanoma images. The output of the proposed system is assessed qualitatively and quantitatively with and without pre-processing of dermoscopy images. From the overall evaluation results, it is found that the segmentation of skin lesion is more efficient using Regularized Extreme Learning Machine if the multi-level pre-processing steps are used in proper sequence.

De‐noising method of aquatic product image with foreign matters based on improved wavelet transform and bilateral filter

AbstractAiming at the strong spot noise caused by the reflection of meat slices in fish image with foreign matters that collected by ordinary camera or Single Lens Reflex (SLR) camera, we proposed an improved wavelet transform and bilateral filtering algorithm to reduce the noise of image. In this paper, our algorithm absorbed the advantages of wavelet threshold de‐noising algorithm and bilateral filtering algorithm, the wavelet threshold de‐noising algorithm being used for high frequency components and bilateral filtering being used for low frequency components, which not only achieved the effect of eliminating noise and weakening light spot, but also protected the details of the image. In the experiment, in order to verify the effectiveness of the proposed algorithm, we used the traditional bilateral filtering, Gaussian filtering and median filtering algorithms to process 500 salmon fish images which had residual fishbone, respectively. Since the original image itself was a noisy image, it was impossible to evaluate the noise reduction effect by using an algorithm that requires a noiseless image as a reference image, so we used five methods to evaluate the quality of the image processed by each algorithm, which were Blind/Referenceless Image Spatial QUality Evaluator (BRISQE), Cumulative Probability of Blur Detection (CPBD), variance, image entropy, and operation speed. Among them, BRISQE and CPBD could quantitatively reflect the overall quality of the image, the lower the BRISQE score or the higher the CPBD score, the better the quality of the image. Compared with other algorithms, the BRISQE score and CPBD score of the image processed by our algorithm were the best, with the average scores reaching 20.65 and .8421, respectively. Experimental results showed that the improved algorithm in this paper can effectively eliminate this kind of strong spot noise. The processed image has achieved better visual effect in both subjective and objective level.

SynthEye: Investigating the Impact of Synthetic Data on Artificial Intelligence-assisted Gene Diagnosis of Inherited Retinal Disease.

Rare disease diagnosis is challenging in medical image-based artificial intelligence due to a natural class imbalance in datasets, leading to biased prediction models. Inherited retinal diseases (IRDs) are a research domain that particularly faces this issue. This study investigates the applicability of synthetic data in improving artificial intelligence-enabled diagnosis of IRDs using generative adversarial networks (GANs). Diagnostic study of gene-labeled fundus autofluorescence (FAF) IRD images using deep learning. Moorfields Eye Hospital (MEH) dataset of 15692 FAF images obtained from 1800 patients with confirmed genetic diagnosis of 1 of 36 IRD genes. A StyleGAN2 model is trained on the IRD dataset to generate 512× 512 resolution images. Convolutional neural networks are trained for classification using different synthetically augmented datasets, including real IRD images plus 1800 and 3600 synthetic images, and a fully rebalanced dataset. We also perform an experiment with only synthetic data. All models are compared against a baseline convolutional neural network trained only on real data. We evaluated synthetic data quality using a Visual Turing Test conducted with 4 ophthalmologists from MEH. Synthetic and real images were compared using feature space visualization, similarity analysis to detect memorized images, and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score for no-reference-based quality evaluation. Convolutional neural network diagnostic performance was determined on a held-out test set using the area under the receiver operating characteristic curve (AUROC) and Cohen's Kappa (κ). An average true recognition rate of 63% and fake recognition rate of 47% was obtained from the Visual Turing Test. Thus, a considerable proportion of the synthetic images were classified as real by clinical experts. Similarity analysis showed that the synthetic images were not copies of the real images, indicating thatcopied real images, meaning the GAN was able to generalize. However, BRISQUE score analysis indicated that synthetic images were of significantly lower quality overall than real images (P < 0.05). Comparing the rebalanced model (RB) with the baseline (R), no significant change in the average AUROC and κ was found (R-AUROC=0.86[0.85-88], RB-AUROC=0.88[0.86-0.89], R-k=0.51[0.49-0.53], and RB-k=0.52[0.50-0.54]). The synthetic data trained model (S) achieved similar performance as the baseline (S-AUROC=0.86[0.85-87], S-k=0.48[0.46-0.50]). Synthetic generation of realistic IRD FAF images is feasible. Synthetic data augmentation does not deliver improvements in classification performance. However, synthetic data alone deliver a similar performance as real data, and hence may be useful as a proxy to real data. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references.

Interval type-2 fuzzy set based block-SBU for image fusion technique

Image fusion is the process of examining one or more obscure images, combining only the critical information in those images to make them a good quality image. Due to the large gap between the high dynamic range of landscape scenes and the low quality of consumer quality cameras, a single-shot image cannot record the information of a single set. A part of image processing called image fusion is used to overcome the above problem. In this method, authors propose a new technology for image fusion with intuitionistic fuzzy sets. During the processing, the given images are converted into fuzzy images and then intuitionistic fuzzy images (IFIs). Thus, a significant change occurs between the given image and IFIs. The resulting IFIs are converted into interval type-2 fuzzy images (IT2FIs) to overcome this problem. The proposed technique is compared to other procedures such as discrete cosine harmonic wavelet transform (DCHWT), multi-resolution single value decay (MSVD), primary component analysis (PCA), standard wavelet transform (SWT), two scale image fusion (TSIF), and wavelet transform (WT). The proposed method gives the best results based on performance analysis such as Entropy, standard deviation (STD), average gradient (AG), mutual information (MIF), and blind referenceless image spatial quality evaluator (BRISQUE). Using the proposed technique, one can obtain the best results for entropy, STD, AG, MIF, and BRISQUE respectively as 7.6241, 49.6745, 69.2100, 5.7276, and 3.3091. Hence it reveals that the proposed method performs better than other methods regarding overall visual quality and performance measurements.

Low-Quality Face Recognition Using an Index-Based Super Resolution Technique

Low-quality face recognition (LQFR), unlike high-quality face recognition is very challenging. This is due to the standoff between the subject and the camera and small face size. The most common way to overcome the mentioned problems is using super-resolution (SR) techniques. In this paper, we show that the efficiency of a FR system degrades significantly in low-resolution images. We propose a novel FR approach using an index-based super-resolution method to improve performance and computational load of a FR system. To achieve this goal, first we apply face quality assessment (FQA) to select appropriate face images with high quality from sequence of images. Then, we introduce Blind/Reference-less Image Spatial Quality Evaluator (BRISQUE) as an index based on which we decided whether or not to use super-resolution in a LQFR system. We also conduct experiments to explore factors which affect FR performance including super-resolution methods, face image size (pixels), face quality and change of the sequence of the conventional LQFR stages. To demonstrate the efficiency of the proposed approach, we compare face identification rate on various deep CNN face recognition. Experimental results show that the proposed method increases the average identification rate to 71.20% on SCFace dataset.

Research on the denoising method of infrared thermogram during rock fracture

In order to overcome the defects of low contrast and low signal-to-noise ratio of the infrared thermal image of rock (ITIR), a joint denoising method of additive and multiplicative noise is proposed for infrared radiation thermal image of rock bearing fracture process. ITIR is de-noised using background phase subtraction, adaptive median filtering, and wavelet multi-scale decomposition threshold denoising Firstly, the noise types in ITIR are analyzed, the basic principles and implementation steps of the denoising method are described, and the denoising effects of different combinations of wavelet basis functions with different decomposition scales in the wavelet threshold denoising process are simulated. Finally, the denoising effect was evaluated in terms of peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and blind/referenceless image spatial quality evaluator (BRISQE). The results show that the PSNR of ITIR is increased by 15.01 % based on removing additive noise. The image quality scores increased by 49.54 % before and after overall denoising. The studies have significance for IR signal extraction and denoising in rocks and solid materials.

Image deblurring to improve the grain monitoring in a rice combine harvester

One of the most important sources of grain losses happens during harvesting. A machine vision system is capable to send online information of the grain condition to the adjustment units in order to reduce the grain losses. However, the continuous vibration of combine during harvesting prevents to acquire desirable images for grain monitoring. To solve this issue, several image deblurring algorithms were investigated and the best one was introduced. The x-y-z accelerometer sensors were installed on the combine tank cap and then the acceleration data was extracted. These data were then used for fabrication of a small-scale combine tank simulator for preparation of the image dataset. Five algorithms were used for deblurring, including Fast Image Deconvolution Hyper-Laplacian Priors, Wiener, Lucy-Richardson, Maximum likelihood deconvolution, and regularized iterative image restoration algorithm. Also, three filters of Motion, Average, and Disk were used. Blind/Reference-less Image Spatial QUality Evaluator (BRISQUE) and Natural Image Quality Evaluator (NIQE) were used as the image quality indices. The results showed that Lucy-Richardson had the best performance in image deblurring (NIQE=6.0539 and BRISQUE=43.0489). Maximum Likelihood showed similar performance (NIQE=6.1974 and BRISQUE=43.4288). Analyzing the execution time of the algorithms showed that Lucy-Richardson was the fastest one (0.8538 s) and therefore, this is preferred to Maximum Likelihood in online applications. The results of the applied filters based on the subjective criterion showed that motion filter has produced sharper images while, disk filter performs better in de-noising. Comparing two objective indices showed that both indices performed well in evaluating the quality of deblurred images. However, the results of original images (blurred images) revealed that NIQE showed more sensitivity to image contrast and noise, compared to BRISQUE.

The Image Definition Assessment of Optoelectronic Tracking Equipment Based on the BRISQUE Algorithm with Gaussian Weights.

Defocus is an important factor that causes image quality degradation of optoelectronic tracking equipment in the shooting range. In this paper, an improved blind/referenceless image spatial quality evaluator (BRISQUE) algorithm is formulated by using the image characteristic extraction technology to obtain a characteristic vector (CV). The CV consists of 36 characteristic values that can effectively reflect the defocusing condition of the corresponding image. The image is evaluated and scored subjectively by the human eyes. The subjective evaluation scores and CVs constitute a set of training data samples for the defocusing evaluation model. An image database that contains sufficiently many training samples is constructed. The training model is trained to obtain the support vector machine (SVM) model by using the regression function of the SVM. In the experiments, the BRISQUE algorithm is used to obtain the image feature vector. The method of establishing the image definition evaluation model via SVM is feasible and yields higher subjective and objective consistency.

A dehazing method for flight view images based on transformer and physical priori.

Aiming at the problems of local dehazing distortion and incomplete global dehazing of existing algorithms in real airborne cockpit environments, a two-stage dehazing method PhysiFormer combining physical a priori with a Transformer oriented flight perspective was proposed. The first stage used synthetic pairwise data to pre-train the dehazing model. First, a pyramid pooling module (PPM) was introduced in the Transformer for multiscale feature extraction to solve the problem of poor recovery of local details, then a global context fusion mechanism was used to enable the model to better perceive global information. Finally, considering that combining the physical a priori needs to rely on the estimation of the atmosphere light, an encoding-decoding structure based on the residual blocks was used to estimate the atmosphere light, which was then used for dehazing through the atmospheric scattering model for dehazing. The second stage used real images combined with physical priori to optimize the model to better fit the real airborne environment. The experimental results show that the proposed method has better naturalness image quality evaluator (NIQE) and blind/referenceless image spatial quality evaluator (BRISQUE) indexes and exhibits the best dehazing visual effect in the tests of dense haze, non-uniform haze and real haze images, which effectively improves the problems of color distortion and haze residue.

An automated method for BRISQUE quantification of image quality in echocardiography

Abstract Background Echocardiography (echo) remains the most widely used imaging modality for the assessment, monitoring, and prognostication of the heart. Despite its prevalence, standardisation efforts for echo chamber quantification are ongoing, with challenges owing to subjectivity during acquisition and analysis. Furthermore, the confidence in derived functional indices is often dependent on the quality of the acquired images. However, few studies have investigated the accuracy of echo measurements compared to a reference modality such as cardiac magnetic resonance (CMR) imaging, when stratified by image quality. Purpose To develop an objective and automated method to quantify echo image quality, and subsequently to investigate the relationship between image quality and patient demographics, as well as the magnitude of bias in left ventricular (LV) functional indices compared with CMR. Methods Transthoracic apical 2D echo (2DE) and 3D echo (3DE) data from 128 participants (72 healthy controls and 56 patients with acquired heart disease) were used to train a BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator) algorithm [1]. Briefly, feature extraction was performed by fitting pixel luminances to a generalised Gaussian distribution (GGD), followed by support vector regression to correlate features (i.e., shape, variance, and mean parameters of the GGD) to quality scores (Fig. 1). Independent BRISQUE models were trained on 580 2DE images (consisting of 2-, 3-, and 4-chamber views) and 128 targeted LV 3DE acquisitions at end-diastole, each assigned a subjective perceived quality score between 1 (poor) and 9 (excellent) by a single observer. LV indices including end-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), and global longitudinal strain (GLS), were assessed according to standard guidelines. Resultant BRISQUE scores were plotted against patient demographics (age, height, weight) and the measurement bias by comparison to CMR (acquired within 1 hour of echo). Results Several linear relationships (where P-value of slope &amp;lt;0.05) were observed between demographics, cardiac indices, and BRISQUE scores. Increasing patient weight (and height in 3DE) were found to be associated with poorer image quality. There was no apparent relationship between image quality and age. Of interest, EF exhibited a relationship with image quality in both 2DE and 3DE (Fig. 2), whereby higher quality images tended to overestimate EF, while lower quality images underestimated EF. For 3DE, image quality dependency was also observed for ESV and GLS biases. Conclusions BRISQUE can objectively quantify image quality to produce scores which correlate to those of an expert observer, with potential utility for the standardised quantification of echo image quality. Using this method, it may be possible to predict patient characteristics which adversely impact echo quality, as well as the magnitude of measurement biases for certain functional indices. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Health Research Council (HRC) of New Zealand; National Heart Foundation (NHF) of New Zealand

An automated method for BRISQUE quantification of image quality in echocardiography

Abstract Background Echocardiography (echo) remains the most widely used imaging modality for the assessment, monitoring, and prognostication of the heart. Despite its prevalence, standardisation efforts for echo chamber quantification are ongoing, with challenges owing to subjectivity during acquisition and analysis. Furthermore, the confidence in derived functional indices is often dependent on the quality of the acquired images. However, few studies have investigated the accuracy of echo measurements compared to a reference modality such as cardiac magnetic resonance (CMR) imaging, when stratified by image quality. Purpose To develop an objective and automated method to quantify echo image quality, and subsequently to investigate the relationship between image quality and patient demographics, as well as the magnitude of bias in left ventricular (LV) functional indices compared with CMR. Methods Transthoracic apical 2D echo (2DE) and 3D echo (3DE) data from 128 participants (72 healthy controls and 56 patients with acquired heart disease) were used to train a BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator) algorithm [1]. Briefly, feature extraction was performed by fitting pixel luminances to a generalised Gaussian distribution (GGD), followed by support vector regression to correlate features (i.e., shape, variance, and mean parameters of the GGD) to quality scores (Fig. 1). Independent BRISQUE models were trained on 580 2DE images (consisting of 2-, 3-, and 4-chamber views) and 128 targeted LV 3DE acquisitions at end-diastole, each assigned a subjective perceived quality score between 1 (poor) and 9 (excellent) by a single observer. LV indices including end-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), and global longitudinal strain (GLS), were assessed according to standard guidelines. Resultant BRISQUE scores were plotted against patient demographics (age, height, weight) and the measurement bias by comparison to CMR (acquired within 1 hour of echo). Results Several linear relationships (where P-value of slope &amp;lt;0.05) were observed between demographics, cardiac indices, and BRISQUE scores. Increasing patient weight (and height in 3DE) were found to be associated with poorer image quality. There was no apparent relationship between image quality and age. Of interest, EF exhibited a relationship with image quality in both 2DE and 3DE (Fig. 2), whereby higher quality images tended to overestimate EF, while lower quality images underestimated EF. For 3DE, image quality dependency was also observed for ESV and GLS biases. Conclusions BRISQUE can objectively quantify image quality to produce scores which correlate to those of an expert observer, with potential utility for the standardised quantification of echo image quality. Using this method, it may be possible to predict patient characteristics which adversely impact echo quality, as well as the magnitude of measurement biases for certain functional indices. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Health Research Council (HRC) of New Zealand; National Heart Foundation (NHF) of New Zealand

Laser Cleaning Surface Roughness Estimation Using Enhanced GLCM Feature and IPSO-SVR

In order to evaluate the effect of laser cleaning, a new method of workpiece surface roughness estimation is proposed. First, a Cartesian robot and visible-light camera are used to collect a large number of surface images of a workpiece after laser cleaning. Second, various features including the Tamura coarseness, Alexnet abstract depth, single blind/referenceless image spatial quality evaluator (BRISQUE), and enhanced gray level co-occurrence matrix (EGLCM) are computed from the images above. Third, the improved particle swarm optimization (IPSO) is used to improve the training parameters of support vector regression (SVR). The learning factor of SVR adopts the strategy of dynamic nonlinear asynchronous adaptive adjustment to improve its optimization-processing ability. Finally, both the image features and the IPSO-SVR are considered for the surface roughness estimation. Extensive experiment results show that the accuracy of the IPSO-SVR surface roughness estimation model can reach 92.0%.

Performance analysis of reshaped Gabor filter for removing the Rician distributed noise in brain MR images.

Magnetic Resonance Imaging (MRI) is an essential clinical tool for detecting the abnormalities such as tumors and clots in the human brain. The brain MR images are contaminated by artifacts and noise that follow Rician distribution during the acquisition process. It causes the loss of fine details information, distortion, and a blurred vision of the image. A reshaped Gabor filter-based denoising technique is proposed to overcome these issues. To develop the reshaped Gabor filter, the range of reshaping parameters of the filter is initially obtained by a random search method. Further, to evaluate the better performance of the proposed filter, a manual search is used to find the optimal parametric values and tested on T1, T2, and PD weighted MR data sets one by one. Also, the proposed technique is compared with the existing state of the art filtering methods such as Wiener, Median, Partial differential equation (PDE), Anisotropic diffusion filter (ADF), Non-local means filter (NLM), Modified complex diffusion filter (MCD), Multichannel residual learning of CNN (MRL), Maximum a posteriori (MAP), Adaptive non-local means algorithm (ADNLM), and Advance NLM filtering with non-sub sampled (AVNLMNS) on the basic reference and no reference parameter. The parameters such as mean square error (MSE), peak signal to noise ratio (PSNR), structural similarity index metric (SSIM), perception-based image quality evaluator (PIQE), and blind/referenceless image spatial quality evaluator (BRISQE) are evaluated on T1, T2, and PD weighted MR images with different noise variances such as 1%, 3%, 5%, 7%, and 9%. The proposed method may be used as a better denoising scheme for Rician distributed noise, edge preservation, fine details restoration, and enhancement of abnormalities.