Edge Preservation Index Research Articles

Region-Growing Algorithm on CT Angiography Images for Detection of Gynecological Malignant Tumor

This paper aimed to explore pelvic lymphadenectomy for gynecological malignant tumors guided by computed tomography angiography (CTA) images under region-growing algorithm (RGA). 100 cases of malignant tumor patients who received pelvic lymphadenectomy in hospital from January 2018 to January 2020 were analyzed. Patients were classified into control group (CTA image) and experimental group (RGA-based CTA image), each with 50 cases. The overall accuracy (OA) of the pelvic CT image segmentation parameters under RGA, the watershed segmentation algorithm (WA), and the swarm intelligence optimization algorithm (SIOA) was compared. Comparisons of segmentation parameters, denoising performance, and CT imaging of patients as well as diagnosis rate and total efficiency rate were carried out. The results showed that overall accuracy (OA) of RGA was considerably higher versus watershed segmentation algorithm (WA) and swarm intelligence optimization algorithm (SIOA). However, false positive rate (FPR) and false negative rate (FNR) of RGA were greatly lower than those of other algorithms. RGA greatly improved the accuracy of pelvic tumor detection. The peak signal-to-noise ratio (PSNR) of RGA was superior to that of WA and SIOA, but differences in edge preservation index (EPI) value were not significant. The diagnosis rate of the experimental group was 48/50 (96%), while the diagnosis rate by manual means was 38/50 (76%). For the diagnosis rate and total efficiency, results of the experimental group were evidently higher in contrast to the control group ( P < 0.05 ). In conclusion, under RGA, CTA image-guided pelvic lymphadenectomy had good segmentation accuracy and denoising performance, and it was superior in total efficiency and diagnosis rate, which was worthy of clinical promotion.

Non-Local Patch Regression Algorithm-Enhanced Differential Photoacoustic Methodology for Highly Sensitive Trace Gas Detection

A non-local patch regression (NLPR) denoising-enhanced differential broadband photoacoustic (PA) sensor was developed for the high-sensitive detection of multiple trace gases. Using the edge preservation index (EPI) and signal-to-noise ratio (SNR) as a dual-criterion, the fluctuation was dramatically suppressed while the spectral absorption peaks were maintained by the introduction of a NLPR algorithm. The feasibility of the broadband framework was verified by measuring the C2H2 in the background of ambient air. A normalized noise equivalent absorption (NNEA) coefficient of 6.13 × 10−11 cm−1·W·Hz−1/2 was obtained with a 30-mW globar source and a SNR improvement factor of 23. Furthermore, the simultaneous multiple-trace-gas detection capability was determined by measuring C2H2, H2O, and CO2. Following the guidance of single-component processing, the NLPR processed results showed higher EPI and SNR compared to the spectra denoised by the wavelet method and the non-local means algorithm. The experimentally determined SNRs of the C2H2, H2O, and CO2 spectra were improved by a factor of 20. The NNEA coefficient reached a value of 7.02 × 10−11 cm−1·W·Hz−1/2 for C2H2. The NLPR algorithm presented good performance in noise suppression and absorption peak fidelity, which offered a higher dynamic range and was demonstrated to be an effective approach for trace gas analysis.

Restoration and enhancement of breast ultrasound images using extended complex diffusion based unsharp masking.

Ultrasound is a well-known imaging modality for the interpretation of breast cancer. It is playing very important role for breast cancer detection that are missed by mammograms. The image acquisition is usually affected by the presence of noise, artifacts, and distortion. To overcome such type of issues, there is a need of image restoration and enhancement to improve the quality of image. This paper proposes a single framework for denoising and enhancement of ultrasound images, where a smoothing filter is replaced with an extended complex diffusion-based filter in an unsharp masking technique. The performance evaluation of the proposed method is tested on real ultrasound breast cancer images database and synthetic ultrasound image. The performance evaluation comprises qualitative and quantitative evaluation along with comparative analysis of pre-existing and proposed method. The quantitative evaluation metrics are mean squared error, peak-signal-to-noise ratio, correlation parameter, normalized absolute error, universal quality index, similarity structure index, edge preservation index, a measure of enhancement, a measure of enhancement by entropy, and second derivative like measurement. The result specifies that the proposed method is better suited approach for the removal of speckle noise which follows Rayleigh distribution, restoration of information, enhancement of abnormalities, and proper edge preservation.

Evaluation of the change in synthetic aperture radar imaging using transfer learning and residual network

Change detection from synthetic aperture radar images becomes a key technique to detect change area related to some phenomenon as flood and deformation of the earth surface. This paper proposes a transfer learning and Residual Network with 18 layers (ResNet-18) architecture-based method for change detection from two synthetic aperture radar images. Before the application of the proposed technique, batch denoising using convolutional neural network is applied to the two input synthetic aperture radar image for speckle noise reduction. To validate the performance of the proposed method, three known synthetic aperture radar datasets (Ottawa; Mexican and for Taiwan Shimen datasets) are exploited in this paper. The use of these datasets is important because the ground truth is known, and this can be considered as the use of numerical simulation. The detected change image obtained by the proposed method is compared using two image metrics. The first metric is image quality index that measures the similarity ratio between the obtained image and the image of the ground truth, the second metrics is edge preservation index, it measures the performance of the method to preserve edges. Finally, the method is applied to determine the changed area using two Sentinel 1 B synthetic aperture radar images of Eddahbi dam situated in Morocco.

Deep feature loss to denoise OCT images using deep neural networks.

.Significance: Speckle noise is an inherent limitation of optical coherence tomography (OCT) images that makes clinical interpretation challenging. The recent emergence of deep learning could offer a reliable method to reduce noise in OCT images.Aim: We sought to investigate the use of deep features (VGG) to limit the effect of blurriness and increase perceptual sharpness and to evaluate its impact on the performance of OCT image denoising (DnCNN).Approach: Fifty-one macula-centered OCT pairs were used in training of the network. Another set of 20 OCT pair was used for testing. The DnCNN model was cascaded with a VGG network that acted as a perceptual loss function instead of the traditional losses of and . The VGG network remains fixed during the training process. We focused on the individual layers of the VGG-16 network to decipher the contribution of each distinctive layer as a loss function to produce denoised OCT images that were perceptually sharp and that preserved the faint features (retinal layer boundaries) essential for interpretation. The peak signal-to-noise ratio (PSNR), edge-preserving index, and no-reference image sharpness/blurriness [perceptual sharpness index (PSI), just noticeable blur (JNB), and spectral and spatial sharpness measure (S3)] metrics were used to compare deep feature losses with the traditional losses.Results: The deep feature loss produced images with high perceptual sharpness measures at the cost of less smoothness (PSNR) in OCT images. The deep feature loss outperformed the traditional losses ( and ) for all of the evaluation metrics except for PSNR. The PSI, S3, and JNB estimates of deep feature loss performance were 0.31, 0.30, and 16.53, respectively. For L1 and L2 losses performance, the PSI, , and JNB were 0.21 and 0.21, 0.17 and 0.16, and 14.46 and 14.34, respectively.Conclusions: We demonstrate the potential of deep feature loss in denoising OCT images. Our preliminary findings suggest research directions for further investigation.

Real-time noise reduction based on ground truth free deep learning for optical coherence tomography.

Optical coherence tomography (OCT) is a high-resolution non-invasive 3D imaging modality, which has been widely used for biomedical research and clinical studies. The presence of noise on OCT images is inevitable which will cause problems for post-image processing and diagnosis. The frame-averaging technique that acquires multiple OCT images at the same or adjacent locations can enhance the image quality significantly. Both conventional frame averaging methods and deep learning-based methods using averaged frames as ground truth have been reported. However, conventional averaging methods suffer from the limitation of long image acquisition time, while deep learning-based methods require complicated and tedious ground truth label preparation. In this work, we report a deep learning-based noise reduction method that does not require clean images as ground truth for model training. Three network structures, including Unet, super-resolution residual network (SRResNet), and our modified asymmetric convolution-SRResNet (AC-SRResNet), were trained and evaluated using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge preservation index (EPI) and computation time (CT). The effectiveness of these three trained models on OCT images of different samples and different systems was also investigated and confirmed. The SNR improvement for different sample images for L2-loss-trained Unet, SRResNet, and AC-SRResNet are 20.83 dB, 24.88 dB, and 22.19 dB, respectively. The SNR improvement for public images from different system for L1-loss-trained Unet, SRResNet, and AC-SRResNet are 19.36 dB, 20.11 dB, and 22.15 dB, respectively. AC-SRResNet and SRResNet demonstrate better denoising effect than Unet with longer computation time. AC-SRResNet demonstrates better edge preservation capability than SRResNet while Unet is close to AC-SRResNet. Eventually, we incorporated Unet, SRResNet, and AC-SRResNet into our graphic processing unit accelerated OCT imaging system for online noise reduction evaluation. Real-time noise reduction for OCT images with size of 512×512 pixels for Unet, SRResNet, and AC-SRResNet at 64 fps, 19 fps, and 17 fps were achieved respectively.

Blind Deconvolution Based on Compressed Sensing with bi-l0-l2-norm Regularization in Light Microscopy Image.

Blind deconvolution of light microscopy images could improve the ability of distinguishing cell-level substances. In this study, we investigated the blind deconvolution framework for a light microscope image, which combines the benefits of bi-l0-l2-norm regularization with compressed sensing and conjugated gradient algorithms. Several existing regularization approaches were limited by staircase artifacts (or cartooned artifacts) and noise amplification. Thus, we implemented our strategy to overcome these problems using the bi-l0-l2-norm regularization proposed. It was investigated through simulations and experiments using optical microscopy images including the background noise. The sharpness was improved through the successful image restoration while minimizing the noise amplification. In addition, quantitative factors of the restored images, including the intensity profile, root-mean-square error (RMSE), edge preservation index (EPI), structural similarity index measure (SSIM), and normalized noise power spectrum, were improved compared to those of existing or comparative images. In particular, the results of using the proposed method showed RMSE, EPI, and SSIM values of approximately 0.12, 0.81, and 0.88 when compared with the reference. In addition, RMSE, EPI, and SSIM values in the restored image were proven to be improved by about 5.97, 1.26, and 1.61 times compared with the degraded image. Consequently, the proposed method is expected to be effective for image restoration and to reduce the cost of a high-performance light microscope.

Performance Analysis of Kalman Filter in Computed Tomography Thorax for Image Denoising

: Medical image processing is a very important field of study due to its large number of applications in human life. For diagnosis of any disease, several methods of medical image acquisition are possible such as Ultrasound (US), Magnetic Resonance Imaging (MRI) or Computed Tomography (CT). Depending upon the type of image acquisition, different types of noise can occur. Background: The most common types of noises in medical images are Gaussian noise, Speckle noise, Poisson noise, Rician noise and Salt & Pepper noise. The related noise models and distributions are described in this paper. We compare several filtering methods for denoising the mentioned types of noise. Objective: The main purpose of this paper is to compare well-known filtering methods such as arithmetic mean, median and enhanced lee filter with only rarely used filtering methods like Kalman filter as well as with relative new methods like Non-Local Means (NLM) filter. Methods: To compare these different filtering methods, we use comparative parameters like Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Mean Structural Similarity (MSSIM), Edge Preservation Index (EPI) and the Universal Image Quality Index (UIQI). Results: The processed images are shown for a specific noise density and noise variance. We show that the Kalman filter performs better than Mean, Median and Enhanced Lee filter for removing Gaussian, Speckle, Poisson and Rician noise. Conclusion: Experimental results show that the Kalman filter provides better results as compared to other methods. It could be also a good alternative to NLM filter due to almost equal results and lower computation time.

Differential synthetic aperture radar interferometric phase map despeckling in discrete Riesz wavelets domain

Phase extraction in differential synthetic aperture radar (SAR) interferometry (DInSAR) is an important tool used for detecting subcentimeter-level change in ground deformation. The evaluated phase map processing is conducted via two important and successive steps: phase denoising and phase unwrapping. We attack the first step and propose the performance of discrete Riesz wavelets transform to reduce the residual speckle noise from the generated DinSAR phase map. The performance of the proposed method is appraised using three important criteria such as peak-to-signal-noise ratio (PSNR), the quality index Q, and the edge preservation index. The obtained metric values reveal that this technique can improve the PSNR and Q in comparison with other famous techniques. Finally, we apply the algorithm to denoise a real DinSAR phase map generated with sentinel application platform software using three SAR data of the Napa city in California.

A Tutorial and Performance Analysis on ENVI Tools for SAR Image Despeckling

Background: The presence of speckle noise in synthetic aperture radar (SAR) images makes the images of low quality in terms of textural features and spatial resolution which are required for processing issues such as image classification and clustering. Already, there are many adaptive filters to remove noise in SAR images. ENVI software is a fully applicable tool for this purpose which has a good library including several filters in the classes of adaptive, orderstatistics and non-linear filters. Materials and Methods: In this study, the toolbox of ENVI is reviewed, analyzed and then numerically evaluated based on several single-band images along with multi-band polarimetric SAR (Pol-SAR) images achieved from SAR sensors such as TerraSAR-X. For evaluation, two metrics including Equivalent Number of Looks (ENL) and Edge Preservation Index (EPI) are used which show the ability of the filters in preserving jointly spatial/textural features based on general information and edges quality, respectively. Results: It is notable that both metrics illustrate that some classic filters are better in comparison to newer filters. Conclusion: The experiments can help us in selecting a better filter towards our aims. In this respect, attention to the results of commercial filters of ENVI software and their analysis can guide us to find the best case in order to process commercial data of SAR sensors in the applications of environmental monitoring, geo-science studies, industrial usages and so on.

Fractional Fourier Transform based Riesz fractional derivative approach for edge detection and its application in image enhancement

Edge detection plays a key role in detecting the boundaries of an object in the image to improve the quality of image edges. The edge detection techniques based on integer order derivatives are more prevalent in literature. But these techniques have the limitation of providing thicker edges and being sensitive to noise. Therefore, in order to deal with this issue, an edge detection technique based on Riesz fractional derivative (RFD) in Fractional Fourier Transform (FrFT) domain is proposed in this paper. The RFD mask used for edge detection is obtained by using various interpolation methods. The selection of mask size is done on the basis of Figure of Merit (FOM) and Edge Preservation Index (EPI). The edges obtained with proposed approach in FrFT domain are further used for image enhancement. The effectiveness of proposed approach is confirmed using test images from different standard datasets by considering performance metrics such as FOM, EPI, Information Entropy, Average Gradient, Edge Intensity, etc. Further validation of the proposed approach is done by considering various uncontrolled features such as Poisson noise, different illumination conditions, JPEG compression artifacts, etc. Experimental results show that proposed technique outperforms the existing techniques by providing improvement in various performance parameters.

Gradient Information-Orientated Colour-Line Priori Knowledge for Remote Sensing Images Dehazing

The frequent occurrence of haze weather seriously affects the object detection and other remote sensing applications, so it is a great challenge to recover clear objects in haze image. Image dehazing can make the raw image have higher contrast, sharpness and more detail information. Therefore, it is of great significance to study image dehazing. In this paper, we propose a gradient information-orientated colour-line priori knowledge method for remote sensing images dehazing. In the case that the color-line prior is not applicable to some scenes, in this paper, a transmittance optimization method of edge-preserving is proposed. The weight distribution is obtained by calculating the prior confidence of the color-line. The similarity between the transmittance of different pixels and the prior image is controlled by the weight. Meanwhile, the gradient information of the original image is used to control the transmittance edge to avoid halo effect. This method can estimate the haze distribution more accurately, thus it can recover a clear image without haze. The experimental results show that our method obtains the better effect in terms of Structural Similarity Index (SSIM), Edge Preservation Index (EPI), mean squared error (MSE), Information Entropy (IE), Gray Mean Grads (GMG) than other state-of-the-arts image dehazing methods.

Region of Interest Prediction using Segmentation

Segmentation separates an image into different sections badsed on the desire of the user. Segmentation will be carried out in an image, until the region of interest (ROI) of an object is extracted. Segmentation reliability predicts the progress of the various segmentation techniques. In this paper, various segmentation methods are proposed and quality of segmentation is verified by using quality metrics like Mean Squared Error (MSE),Signal to Noise Ratio (SNR), Peak- Signal to Noise Ratio (PSNR), Edge Preservation Index (EPI) and Structural Similarity Index Metric (SSIM).

High-quality photoacoustic image reconstruction based on deep convolutional neural network: towards intra-operative photoacoustic imaging

The use of intra-operative imaging system as an intervention solution to provide more accurate localization of complicated structures has become a necessity during the neurosurgery. However, due to the limitations of conventional imaging systems, high-quality real-time intra-operative imaging remains as a challenging problem. Meanwhile, photoacoustic imaging has appeared so promising to provide images of crucial structures such as blood vessels and microvasculature of tumors. To achieve high-quality photoacoustic images of vessels regarding the artifacts caused by the incomplete data, we proposed an approach based on the combination of time-reversal (TR) and deep learning methods. The proposed method applies a TR method in the first layer of the network which is followed by the convolutional neural network with weights adjusted to a set of simulated training data for the other layers to estimate artifact-free photoacoustic images. It was evaluated using a generated synthetic database of vessels. The mean of signal to noise ratio (SNR), peak SNR, structural similarity index, and edge preservation index for the test data were reached 14.6 dB, 35.3 dB, 0.97 and 0.90, respectively. As our results proved, by using the lower number of detectors and consequently the lower data acquisition time, our approach outperforms the TR algorithm in all criteria in a computational time compatible with clinical use.

An Image Denoising Method Based on BM4D and GAN in 3D Shearlet Domain

To overcome the disadvantages of the traditional block-matching-based image denoising method, an image denoising method based on block matching with 4D filtering (BM4D) in the 3D shearlet transform domain and a generative adversarial network is proposed. Firstly, the contaminated images are decomposed to get the shearlet coefficients; then, an improved 3D block-matching algorithm is proposed in the hard threshold and wiener filtering stage to get the latent clean images; the final clean images can be obtained by training the latent clean images via a generative adversarial network (GAN).Taking the peak signal-to-noise ratio (PSNR), structural similarity (SSIM for short) of image, and edge-preserving index (EPI for short) as the evaluation criteria, experimental results demonstrate that the proposed method can not only effectively remove image noise in high noisy environment, but also effectively improve the visual effect of the images.

A quality improvement method for lung LDCT images.

Low dose computed tomography (LDCT) reduces radiation damage to patients. However, with the decrease of radiation dose, LDCT images of the lung often appear some serious problems such as poor contrast, noise and streak artifacts. To improve the quality of lung LDCT images, this study proposed and investigated a new denoising method based on classification training structure combined dictionary for lung LDCT images. First, top-hat transform and anisotropic diffusion with a shock filter (ADSF) algorithm are used to enhance the image contrast and image details. Second, an adaptive dictionary is trained and used for noise reduction. Third, more image details are extracted from the residual image by using the atom activity measurement. The final result is obtained by combining the dictionary denoising result with the extracted detail information. The proposed method is then validated by both simulated and clinical lung LDCT images. Four metrics including Contrast-to-Noise Ratio (CNR), Noise Suppression Index (NSI), Edge Preserving Index (EPI), and Blurring Index (BI) are computed to quantitatively evaluate image quality. The results showed that the CNR, NSI, EPI, and BI of our method reached 8.953, 0.9500, 0.7230 and 0.0170, respectively. Noise and streak artifacts can be removed from lung LDCT images while keeping and retaining more details. Comparing with the results of other methods tested using the same dataset, this study demonstrated that our new method significantly improved quality of the lung LDCT images.

A quantitative assessment of speckle noise reduction in SAR images using TLFFBP neural network

Synthetic aperture radar (SAR) images are difficult to analyze due to the presence of speckle noise. Speckle noise must be filtered out before applying to other image processing applications. Three-layered feed forward back propagation neural network (TLFFBPNN) has been proposed to suppress the speckle noise. Gray-level co-occurrence matrix properties have been extracted, and back propagation training algorithm is used to train the neural network. The performance metrics such as peak signal to noise (PSNR), structural similarity index matrix (SSIM), edge preservation index (EPI), equivalent number of looks (ENL), and speckle suppression index (SSI) have been evaluated to find the efficiency of TLFFBPNN and compared with four recently developed de-speckling techniques. The exploratory outcomes show that the TLFFBPNN method has better de-speckling execution with great edge preservation. The comparative outcome reveals that the proposed TLFFBPNN de-speckled method outperformed in terms of PSNR of 0.98%, SSIM of 1.0%, SSI of 2.0%, EPI of 0.84%, and ENL of 0.5% when compared with the Wiener Filter Sparse Optimization in Contourlet transform domain de-speckling method.

Texture Smoothing Quality Assessment via Information Entropy

Image texture smoothing (ITS) aims at completely removing textures while preserving as much as possible different-scale structures of an image. However, few quality assessment metrics have been formulated to objectively evaluate the ITS results, due to the lack of ground-truth texture-free images. Considering both the smoothness of textures and the preservation of structures, we make the debut of objective evaluation of ITS results, and design an intuitive Texture Smoothness and Structural Similarity Index (T3SI) based on human perception. Specifically, we first intuitively select some patches which contain relatively more texture/structure information. We then employ the Edge Preservation Index and the Structural Similarity Index Measure to evaluate the texture smoothness and the structure similarity on the selected patches, respectively. We finally formulate the novel exponential entropy function to balance the texture smoothness and the structure similarity for the quality assessment of ITS. T3SI is objective, easy to use, simple to implement, and stable. An independent User Study is performed to verify our proposed T3SI, and large experiments show that T3SI competes successfully to the state-of-the-art metrics. Our code is publicly available.

Selection of suitable segmentation technique based on image quality metrics

ABSTRACTImage segmentation [Gonzalez RG, Woods RE. Third edition digital image processing. South Asia: Pearson; 2014, Sridhar S. Digital image processing. India: Oxford; 2011] is a very important step in applications of the image processing field. Segmentation will divide the image into different parts and extract a required area known as Region of Interest (ROI). ROI varies with applications. For the effective implementation of image processing applications segmentation of the required area is the first major problem. There is no single universal segmentation algorithm to extract ROI from all types of images. So the best solution is to try all algorithms and pick a suitable one for a particular image. In this paper, we compare different image segmentation algorithms by applying them to different images and by calculating various image quality parameters such as MSE (Mean Squared Error), SNR (Signal to Noise Ratio), PSNR (Peak-Signal to Noise Ratio), EPI (Edge Preservation Index) and SSIM (Structural Similarity Index Metric).

Non-blind deconvolution with an alternating direction method of multipliers (ADMM) after noise reduction in nondestructive testing

To ensure the quality of material, nondestructive testing is necessary, and radiography testing is the nondestructive technique most commonly used today. For inspection, the quality of a radiographic image is critical, and there are many image artifacts that can reduce inspection accuracies such as noise or blurring. The deterioration in spatial resolution caused by blur in both the X-ray imaging itself and the noise reduction process are particular problems. To tackle them, we implemented a non-blind deconvolution method that employs the alternating direction method of multipliers (ADMM) after noise reduction. Experimental results confirm that the proposed algorithm effectively restores edge sharpness. The 50% modulation transfer function of the restored image of a slit-camera was about 3.54 line-pairs per mm, which is about 2.5 times higher than that of the denoised image. Moreover, the edge preservation index values are about 0.82, 0.54, and 0.75 for the restored, denoised, and acquired images, respectively. Consequentially, the proposed method has the potential to increase inspection efficiency in industrial applications.