Ultrasonography is widely used to screen thyroid tumors because it is safe, easy to use, and low-cost. However, it is simultaneously affected by speckle noise and other artifacts, so early detection of thyroid abnormalities becomes difficult for the radiologist. Therefore, various researchers continuously address the limitations of sonography and improve the diagnosis potential of US images for thyroid tissue from the last three decays. Accordingly, the present study extensively reviewed various CAD systems used to classify thyroid tumor US (TTUS) images related to datasets, despeckling algorithms, segmentation algorithms, feature extraction and selection, assessment parameters, and classification algorithms. After the exhaustive review, the achievements and challenges have been reported, and build a road map for the new researchers.

Encoder-decoder-based semantic segmentation models classify image pixels into the corresponding class, such as the ROI (region of interest) or background. In the present study, simple / dilated convolution / series / directed acyclic graph (DAG)-based encoder-decoder semantic segmentation models have been implemented, i.e., SegNet (VGG16), SegNet (VGG19), U-Net, mobileNetv2, ResNet18, ResNet50, Xception and Inception networks for the segment TTUS(Thyroid Tumor Ultrasound) images. Transfer learning has been used to train these segmentation networks using original and despeckled TTUS images. The performance of the networks has been calculated using mIoU and mDC metrics. Based on the exhaustive experiments, it has been observed that ResNet50-based segmentation model obtained the best results objectively with values 0.87 for mIoU, 0.94 for mDC, and also according to radiologist opinion on shape, margin, and echogenicity characteristics of segmented lesions. It is noted that the segmentation model, namely ResNet50, provides better segmentation based on objective and subjective assessment. It may be used in the healthcare system to identify thyroid nodules accurately in real time.

OBJECTIVE: The primary causes of kidney failure are chronic and polycystic kidney diseases. Cyst, stone, and tumor development lead to chronic kidney diseases that commonly impair kidney functions. The kidney diseases are asymptomatic and do not show any significant symptoms at its initial stage. Therefore, diagnosing the kidney diseases at their earlier stage is required to prevent the loss of kidney function and kidney failure.
METHODS: This paper proposes a computer-aided diagnosis (CAD) system for detecting multi-class kidney abnormalities from ultrasound images. The presented CAD system uses a pre-trained ResNet-101 model for extracting the features and support vector machine (SVM) classifier for the classification purpose. Ultrasound images usually gets affected by speckle noise that degrades the image quality and performance of the CAD system. Hence, it is necessary to remove speckle noise from the ultrasound images. Therefore, a CAD based system is proposed with the despeckling module using a deep residual learning network (RLN) to reduce speckle noise. Pre-processing of ultrasound images using deep RLN helps to drastically improve the classification performance of the CAD system. The proposed CAD system achieved better prediction results when compared to the existing state-of-the-art methods.
RESULTS: To validate the proposed CAD system performance, the experiments have been carried out in the noisy kidney ultrasound images. The designed system framework achieved the maximum classification accuracy when compared to the existing approaches. The SVM classifier is selected for the CAD system based on performance comparison with various classifiers like K-nearest neighbour, tree, discriminant, Naive Bayes, and linear.
CONCLUSIONS: The proposed CAD system outperforms in classifying the noisy kidney ultrasound images precisely as compared to the existing state-of-the-art methods. Further, the CAD system is evaluated in terms of selectivity and sensitivity scores. The presented CAD system with the pre-processing module would serve as a real-time supporting tool for diagnosing multi-class kidney abnormalities from the ultrasound images.

Speckles are introduced in the ultrasound data due to constructive and destructive interference of the probing signals that are used for capturing the characteristics of the tissue being imaged. There are a plethora of models discussed in the literature to improve the contrast and resolution of the ultrasound images by despeckling them. There is a class of models that assumes that the noise is multiplicative in its original form, and transforming the model to a log domain makes it an additive one. Nevertheless, such a transformation duly oversimplifies the scenario and does not capture the inherent properties of the data-correlated nature of speckles. Therefore, it results in poor reconstruction. This problem is addressed to a considerable extent in the subsequent works by adopting various models to address the data-correlated nature of the noise and its distributions. This work introduces a weberized non-local total bounded variational model based on the noise distribution built on the Retinex theory. This perceptually inspired model apparently restores and improves the contrast of the images without compromising much on the details inherently present in the data. The numerical implementation of the model is carried out using the Bregman formulation to improve the convergence rate and reduce the parameter sensitivity. The experimental results are highlighted and compared to demonstrate the efficiency of the model.

Speckle noise contaminates medical ultrasound images, and the suppression of speckle noise is helpful for image interpretation. Traditional ultrasound denoising (i.e., despeckling) methods are developed on two-dimensional static images. However, one of the advantages of ultrasonography is its nature of dynamic imaging. A method for dynamic ultrasound despeckling is expected to incorporate both the spatial and temporal information in successive images of dynamic ultrasound and thus yield better denoising performance. Here we regard a dynamic ultrasound video as three-dimensional (3-D) images with two dimensions in the spatial domain and one in the temporal domain, and we propose a despeckling algorithm for dynamic ultrasound named the 3-D Gabor-based anisotropic diffusion (GAD-3D). The GAD-3D expands the classic two-dimensional Gabor-based anisotropic diffusion (GAD) into 3-D domain. First, we proposed a robust 3-D Gabor-based edge detector by capturing the edge with 3-D Gabor transformation. Then we embed this novel detector into the partial differential equation of GAD to guide the 3-D diffusion process. In the simulation experiment, when the noise variance is as high as 0.14, the GAD-3D improves the Pratt\'s figure of merit, mean structural similarity index and peak signal-to-noise ratio by 24.32%, 10.98%, and 6.51%, respectively, compared with the best values of seven other methods. Experimental results on clinical dynamic ultrasonography suggest that the GAD-3D outperforms the other seven methods in noise reduction and detail preservation. The GAD-3D is effective for dynamic ultrasound despeckling and may be potentially valuable for disease assessment in dynamic medical ultrasonography.

Background and objective Ultrasound is the non-radioactive imaging modality used in the diagnosis of various diseases related to the internal organs of the body. The presence of speckle noise in ultrasound image (UI) is inevitable and may affect resolution and contrast of the image. Existence of the speckle noise degrades the visual evaluation of the image. The despeckling of UI is a desirable pre-processing step in computer-aided UI based diagnosis systems. Methods This paper proposes a novel method for despeckling UIs using pre-trained residual learning network (RLN). Initially, RLN is trained with pristine and its corresponding noisy images in order to achieve a better performance. The developed method chooses a pre-trained RLN for despeckling UIs with less computational resources. But the training procedure of RLN from scratch is computationally demanding. The pre-trained RLN is a blind despeckling approach and does not require any fine tuning and noise level estimation. The presented approach shows superiority in the removal of speckle noise as compared to the existing state-of-art methods. Results To highlight the effectiveness of the proposed method the pristine images from the Waterloo dataset has been considered. The proposed pre-trained RLN based UI despeckling method resulted in a better peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) at different speckle noise levels. The no-reference image quality approach is adopted to ensure robustness of the established method for real time UI. From results it is obvious that, the performance of the proposed method is superior than the existing methods in terms of naturalness image quality evaluator (NIQE). Conclusions From the experimental results, it is clear that the proposed method outperforms the existing despeckling methods in terms of both artificially added and naturally occurring speckle images.

This paper presents novel pre-processing image enhancement algorithms for retinal optical coherence tomography (OCT). These images contain a large amount of speckle causing them to be grainy and of very low contrast. To make these images valuable for clinical interpretation, we propose a novel method to remove speckle, while preserving useful information contained in each retinal layer. The process starts with multi-scale despeckling based on a dual-tree complex wavelet transform (DT-CWT). We further enhance the OCT image through a smoothing process that uses a novel adaptive-weighted bilateral filter (AWBF). This offers the desirable property of preserving texture within the OCT image layers. The enhanced OCT image is then segmented to extract inner retinal layers that contain useful information for eye research. Our layer segmentation technique is also performed in the DT-CWT domain. Finally we describe an OCT/fundus image registration algorithm which is helpful when two modalities are used together for diagnosis and for information fusion.

Ultrasound imaging of the common carotid artery (CCA) is a non-invasive tool used in medicine to assess the severity of atherosclerosis and monitor its progression through time. It is also used in border detection and texture characterization of the atherosclerotic carotid plaque in the CCA, the identification and measurement of the intima-media thickness (IMT) and the lumen diameter that all are very important in the assessment of cardiovascular disease (CVD). Visual perception, however, is hindered by speckle, a multiplicative noise, that degrades the quality of ultrasound B-mode imaging. Noise reduction is therefore essential for improving the visual observation quality or as a pre-processing step for further automated analysis, such as image segmentation of the IMT and the atherosclerotic carotid plaque in ultrasound images. In order to facilitate this preprocessing step, we have developed in MATLAB(®) a unified toolbox that integrates image despeckle filtering (IDF), texture analysis and image quality evaluation techniques to automate the pre-processing and complement the disease evaluation in ultrasound CCA images. The proposed software, is based on a graphical user interface (GUI) and incorporates image normalization, 10 different despeckle filtering techniques (DsFlsmv, DsFwiener, DsFlsminsc, DsFkuwahara, DsFgf, DsFmedian, DsFhmedian, DsFad, DsFnldif, DsFsrad), image intensity normalization, 65 texture features, 15 quantitative image quality metrics and objective image quality evaluation. The software is publicly available in an executable form, which can be downloaded from http://www.cs.ucy.ac.cy/medinfo/. It was validated on 100 ultrasound images of the CCA, by comparing its results with quantitative visual analysis performed by a medical expert. It was observed that the despeckle filters DsFlsmv, and DsFhmedian improved image quality perception (based on the expert\'s assessment and the image texture and quality metrics). It is anticipated that the system could help the physician in the assessment of cardiovascular image analysis.