Ultrasound Kidney Images Research Articles

Abnormality Detection in Kidney Ultrasound Images by Various Classifiers with FPGA

Ultrasound scanning has been used as the preliminary diagnosis tool all over the world. The ultrasound data are being analyzed by tele-radiologists. It lacks online availability. The drawbacks of tele-radiology have been overcome by using computer-aided diagnosis. As an aid to this, the Random Forest Classifier has been used here for detecting kidney abnormalities. The initial pre-processing stage filters the speckle noise existing in the input kidney image. Then feature extraction has been performed. Image categorization as normal, cyst and stone has been done with Random Forest Classifier. Then the performance is evaluated by comparing it with K-nearest neighbor classifier and support vector machine. From experimentation, it is observed that the accuracy and F-measure values of Random Forest Classifier range high when compared with other classifiers due to the continuous split of the trees until accurate categorization is done. Simulation and Implementation have been done using Modelsim 6.4a and Xilinx Spartan-6 FPGA board.

AN IMPROVED BUTTERFLY-NET BASED DETECTION OF STONES IN KIDNEY IMAGES

Kidney stone disease is one of the major life threatening problem existing worldwide. It is important to recognize the true and exact location of kidney stone. The ultrasound images of kidney comprise speckle noise and are of low contrast which makes the identification of kidney abnormalities a challenging task. Thus, the doctors may find recognition of small stones difficult. Butterfly-net model is a low-complexity convolutional neural network with sparse cross channel connections and with pre-processing techniques for kidney stone detection. IntegratingSerious problems are well approximated with network complexity based on frequency bandwidth rather than input dimension using a butterfly-net with a fully connected neural network. The proposed Butterfly-net obviously embraces the arranged construction in the Butterfly plot as the stacked convolutional layers. In the event that the boundaries are hard-coded as that in the Butterfly plot, Butterfly-net on the whole registers the Fourier coefficients of the info signal with ensured mathematical exactness. Keywords:Butterfly-Net , Kidney Stone, Deep Learning.

A Hybrid Random Forest Classifier for Chronic Kidney Disease Prediction from 2D Ultrasound Kidney Images

Chronic kidney disease (CKD) is one of the causes of mortality in almost all countries across the globe and the notable thing is its asymptomatic nature in the early stages. This disease is characterized by the gradual loss of kidney function in an individual. Frequently chronic kidney disease is diagnosed based on the Estimated Glomerular Filtration Rate (eGFR) determined from blood and urine tests. In order to reduce the risk factors arising due to chronic kidney disease, it is essential to be diagnosed in the earlier stages itself. This work proposes an automated chronic kidney disease detection based on the textural features of the kidney using a hybrid random forest classifier from 2D ultrasound kidney images. The proposed classifier is compared with the other competing machine learning classifiers through experimenting on a dataset of 150 images and gives a better accuracy of [Formula: see text] with [Formula: see text] of recall and precision, thus proving it to be promising in detecting CKD noninvasively in the early stages.

Machine Learning-Aided Chronic Kidney Disease Diagnosis Based on Ultrasound Imaging Integrated with Computer-Extracted Measurable Features.

Although ultrasound plays an important role in the diagnosis of chronic kidney disease (CKD), image interpretation requires extensive training. High operator variability and limited image quality control of ultrasound images have made the application of computer-aided diagnosis (CAD) challenging. This study assessed the effect of integrating computer-extracted measurable features with the convolutional neural network (CNN) on the ultrasound image CAD accuracy of CKD. Ultrasound images from patients who visited Severance Hospital and Gangnam Severance Hospital in South Korea between 2011 and 2018 were used. A Mask regional CNN model was used for organ segmentation and measurable feature extraction. Data on kidney length and kidney-to-liver echogenicity ratio were extracted. The ResNet18 model classified kidney ultrasound images into CKD and non-CKD. Experiments were conducted with and without the input of the measurable feature data. The performance of each model was evaluated using the area under the receiver operating characteristic curve (AUROC). A total of 909 patients (mean age, 51.4 ± 19.3years; 414 [49.5%] men and 495 [54.5%] women) were included in the study. The average AUROC from the model trained using ultrasound images achieved a level of 0.81. Image training with the integration of automatically extracted kidney length and echogenicity features revealed an improved average AUROC of 0.88. This value further increased to 0.91 when the clinical information of underlying diabetes was also included in the model trained with CNN and measurable features. The automated step-wise machine learning-aided model segmented, measured, and classified the kidney ultrasound images with high performance. The integration of computer-extracted measurable features into the machine learning model may improve CKD classification.

A novel convolutional neural network for kidney ultrasound images segmentation

Background and Objective: Ultrasound imaging has been widely used in the screening of kidney diseases. The localization and segmentation of the kidneys in ultrasound images are helpful for the clinical diagnosis of diseases. However, it is a challenging task to segment the kidney accurately from ultrasound images due to the interference of various factors.Methods: In this paper, a novel multi-scale and deep-supervised CNN architecture is proposed to segment the kidney. The architecture consists of an encoder, a pyramid pooling module and a decoder. In the encoder, we design a multi-scale input pyramid with parallel branches to capture features at different scales. In the decoder, a multi-output supervision module is developed. The introduction of the multi-output supervision module enables the network to learn to predict more precise segmentation results scale-by-scale. In addition, we construct a kidney ultrasound dataset, which contains of 400 images and 400 labels.Results: To highlight effectiveness of the proposed approach, we use six quantitative indicators to compare with several state-of-the-art methods on the same kidney ultrasound dataset. The results of our method on the six indicators of accuracy, dice, jaccard, precision, recall and ASSD are 98.86%, 95.86%, 92.18%, 96.38%, 95.47% and 0.3510, respectively.Conclusions: The analysis of evaluation indicators and segmentation results shows that our method achieves the best performance in kidney ultrasound image segmentation.

Kidney image classification using transfer learning with convolutional neural network

For abdominal studies, one of the most widely used diagnostic methods is ultrasound imaging. Several chronic kidney diseases (CKDs) such as kidney stone, cystic kidney, and hydronephrosis are present in the human kidney. These CKDs, later on, lead to the development of a number of severe diseases particularly heart diseases, pulmonary attacks, cardiomyopathy, etc. Therefore, early detection of CKDs is highly desirable in clinical practices as it can save hundreds of lives. Nowadays, the main focus of researchers is to develop automatic disease detection methods, avoiding the need for human interaction. The study of deep learning models is playing a critical role in various applications of healthcare not only due to their fast and accurate results, but also minimal manual interference is required in these methods. In this paper, two approaches are proposed for the detection of CKDs in ultrasound kidney images. The first one is a conventional approach that uses GA optimised neural network (GAONN) as classifier, whereas in other approach, convolution neural network model such as AlexNet is used for automatic detection of diseases. AlexNet is trained using the transfer learning process. Experimental results show that CNN performs better than GA optimised neural network in classifying kidney images.

MBANet: Multi-branch aware network for kidney ultrasound images segmentation

Due to the influence of kidney morphology, heterogeneous structure and image quality, segmenting kidney in ultrasound images is challenging. To alleviate this challenge, we proposed a novel deep neural network architecture, namely Multi-branch Aware Network (MBANet), to segment kidney accurately and robustly. MBANet mainly consists of multi-scale feature pyramid (MSFP), multi-branch encoders (MBE) and master decoder. The design of MSFP can make the network more accessible to different kinds of class details at different scales. The information exchange between MBE can reduce the loss of feature information and improve the segmentation accuracy of the network. In addition, we designed a multi-scale fusion block (MFBlock) in the MBE to further extract and fuse more refined multi-scale image information. In order to further improve the robustness of MBANet, this paper also designed a step-by-step training mechanism. We validated the proposed approach and compared to several state-of-the-art approaches on the same kidney ultrasound datasets using six quantitative metrics. The results of our method on the six indicators of pixel accuracy (PA), intersection over union (IoU), precision, recall, specificity and F1-score (F1) are 98.83%, 92.38%, 97.10%, 95.03%, 99.46% and 0.9601, respectively. Compared with the comparison method, the average values on the six indicators are improved by about 2%. The evaluation results and segmentation results demonstrate that the proposed approach achieves the best overall performance on kidney ultrasound images segmentation.

A novel hybrid deep learning architecture for predicting acute kidney injury using patient record data and ultrasound kidney images

ABSTRACT Acute kidney injury (AKI) is a sudden onset of kidney damage. Currently, there is no hybrid model predicting AKI that takes advantage of two types of data. In this research, a novel hybrid deep learning architecture for AKI prediction was created using de-identified numeric patient data and ultrasound kidney images. Using data features including serum creatinine among others, two numeric models using MIMIC-III and paired hospital data were developed, and with the hospital ultrasounds, an image-only model was developed. Convolutional neural networks (CNN) were used, including VGG and Resnet, and they were made into a hybrid model by concatenating feature maps of both types of models to create a new input. The hybrid model successfully predicted AKI and the highest AUROC of the model was 0.953, the first time an AKI machine learning model surpassed an AUROC of 0.9. The model also achieved an accuracy of 90% and F1-score of 0.91. This model can be implemented into urgent clinical settings such as the ICU and aid doctors by assessing the risk of AKI shortly after the patient’s admission to the ICU. The implementation of this research has great potential to be applied to different medical predictive applications.

Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

To explore the utilization of the convolutional neural network (CNN) and wavelet transform in ultrasonic image denoising and the influence of the optimized wavelet threshold function (WTF) algorithm on image denoising, in this exploration, first, the imaging principle of ultrasound images is studied. Due to the limitation of the principle of ultrasound imaging, the inherent speckle noise will seriously affect the quality of ultrasound images. The denoising principle of the WTF based on the wavelet transform is analyzed. Based on the traditional threshold function algorithm, the optimized WTF algorithm is proposed and applied to the simulation experiment of ultrasound images. By comparing quantitatively and qualitatively with the traditional threshold function algorithm, the advantages of the optimized WTF algorithm are analyzed. The results suggest that the image is denoised by the optimized WTF. The mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM) of the images are 20.796 dB, 34.294 dB, and 0.672 dB, respectively. The denoising effect is better than the traditional threshold function. It can denoise the image to the maximum extent without losing the image information. In addition, in this exploration, the optimized function is applied to the actual medical image processing, and the ultrasound images of arteries and kidneys are denoised separately. It is found that the quality of the denoised image is better than that of the original image, and the extraction of effective information is more accurate. In summary, the optimized WTF algorithm can not only remove a lot of noise but also obtain better visual effect. It has important value in assisting doctors in disease diagnosis, so it can be widely applied in clinics.

SDFNet: Automatic segmentation of kidney ultrasound images using multi-scale low-level structural feature

Due to speckle noise, changes in kidney shape and size between patients, and similar regions, segmenting kidneys in ultrasound images is challenging. To alleviate this challenge, we proposed a novel CNN model, namely multi-scale fusion network of structural features and detailed features (SDFNet), to segment kidneys accurately and robustly. Specifically, the architecture includes structure feature extraction network (S-Net), detail information extraction network (D-Net) and multi-scale fusion block (MCBlock), which are in charge of extracting structural features, capturing texture details and merging features, respectively. In S-Net, we designed a boundary detection (BD) module to obtain more complete kidney structural features. In addition, this paper also designed a step-by-step training mechanism to improve the generalization ability of the SDFNet. We validated the proposed method and compared the same kidney ultrasound dataset with several state-of-the-art methods using six quantitative indicators. The results demonstrate that the proposed approach achieves the best overall performance and outperforms all other approaches on kidney ultrasound image segmentation. It is worth noting that this paper quantitatively analyzes the loss function of segment kidney ultrasound images. This work is a good reference for future research.

Chronic kidney disease stage identification using texture analysis of ultrasound images

Early detection of chronic kidney disease (CKD) stages is an important aspect of human healthcare. Clinical approach for identification of CKD stage is expensive, time consuming and mostly not accessible to common people living in remote locations. An automatic method based on ultrasound (US) imaging is more appropriate to receive the first-hand information of kidney health in quick time. Extraction of local features from US image and selection of classifier plays a major role to improve the CKD stage detection accuracy. In this paper, gray level co-occurrence matrix (GLCM) used to extract local features of US kidney images in terms of a fourteen-point feature-vectors. The classifier processes the extracted feature-vectors to identify CKD stage. Extensive simulation performed in MATLAB using different multiclass classifier on 300 sample US images collected by the radiologist to select the multiclass classifier suitable to identify CKD stages with higher accuracy. Simulation result shows that linear discriminant analysis (LDA) classifier performs better than other available multiclass classifiers to classify CKD stages with higher accuracy. Proposed scheme identifies, CKD stage-1, stage-2 and stage-3 with accuracies nearly 96.82%, 100% and 98.38%, respectively. The proposed scheme using the fourteen-point feature vector identify CKD stages with 16% higher accuracy over the exisitng schemes. However, the proposed scheme able to identify CKD stages with 24% higher accuracy than the existing scheme when both the fourteen-point feature-vector and the LDA classifier are used. Therefore, the proposed scheme could be an alternative to the existing scheme for automatic CKD stage identification.

Computer-aided diagnosis system for the classification of multi-class kidney abnormalities in the noisy ultrasound images

Background and 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.

An automatic feature selection and classification framework for analyzing ultrasound kidney images using dragonfly algorithm and random forest classifier

In medical imaging, the automatic diagnosis of kidney carcinoma has become more difficult because it is not easy to detect by physicians. Pre-processing is the first identification method to enhance image quality, remove noise and unwanted components from the backdrop of the kidneys image. The pre-processing method is essential and significant for the proposed algorithm. The objective of this analysis is to recognize and classify kidney disturbances with an ultrasound scan by providing a number of substantial content description parameters. The ultrasound pictures are prepared to protect the interest pixels before extracting the feature. A series of quantitative features were synthesized of each images, the principal component analysis was conducted for minimizing the number of features to produce set of wavelet-based multi-scale features. Dragonfly algorithm (DFA) was executed in this method. In the proposed work, the design and training of a random decision forest classifier and selected features are implemented. The classification of e-health information using ideal characteristics is used by the RF classifier. The proposed technique is activated in MATLAB/simulink work site and the experimental results show that the peak accuracy of the proposed technique is 95.6% using GWO-FFBN techniques compared to other existing techniques.

Ultrasound Based Radiomics Features of Chronic Kidney Disease

Interstitial fibrosis, common to most chronic kidney diseases, can potentially affect the speckle patterns of kidney ultrasound (US). Here we use Radiomics features derived from US images to identify kidneys with chronic kidney disease. B-mode US without speckle reduction was performed on a cohort of CKD patients (n = 75) and healthy subjects (n = 27). Images of the patients with renal cysts, agenesis and calculi were excluded. After background subtraction, regions of interest were selected from each kidney. Four hundred and sixty-five Radiomics features including first and second-order gray level statistics were calculated on the selected regions. Second-order features were also calculated on wavelet transformed images. A random forest model was used to identify the most important features that can differentiate healthy and diseased kidneys. The ten most important features, based on the Gini index, were used to train a support vector machine. Synthetic minority oversampling technique was used to remove over fitting. Wavelet transformed, Gray Level Run Length Matrix based Normalized Run Length Non-uniformity, WT (LH) (GRLN) was identified as the most significant feature in differentiating CKD and healthy kidneys (accuracy - 0.85, sensitivity - 1.0). The mean WT (LH) GRLN of healthy kidneys (0.40 ± 0.01) was significantly higher (p < 0.01) than that of the CKD kidneys (0.24 ± 0.01). According to the Gini Index, the differentiability of WT (LH) GRLN was highest when the long axis of the kidney was oriented perpendicular to the columns of the image matrix. Radiomics features based on wavelet transformation are sensitive to directionality of US speckle patters and can be successfully used to differentiate CKD and healthy US kidney images.

Analysis and classification of kidney ultrasound images using SIFT features with neural network classifier

A unique method to analyse ultrasound scan images of kidney to classify renal abnormalities using SIFT features and artificial neural network is presented in this paper. The ultrasound kidney images are classified into four classes normal, cyst, calculi, and tumour. Preprocessing and denoising techniques are applied for the removal of speckle noise by applying median and Wiener filter. Segmentation to obtain region of interest (ROI) is carried out by applying fuzzy C-means clustering technique. Region of interest specifies to the objects such as kidney, calculi/stones, cysts and tumour inside the kidney present in the ultrasound scan images. First order statistical features and GLCM features are computed. To overcome the operator dependency of ultrasound scanning procedure rotational variance SIFT algorithm is applied. SIFT descriptors as features are obtained. Abnormalities are classified using supervised learning neural network algorithm (ANN). Classifier performance metrics are higher when hidden neurons are fixed as 101.

Analysis and classification of kidney ultrasound images using SIFT features with neural network classifier

Analysis and classification of kidney ultrasound images using SIFT features with neural network classifier

Performance Assessment of Ultrasound Kidney Images using De-speckling algorithms

Performance Assessment of Ultrasound Kidney Images using De-speckling algorithms

Assessment of hepatic fat content in using quantitative ultrasound measurement of hepatic/renal ratio and hepatic echo-intensity attenuation rate.

This study aims to evaluate and validate a simple quantitative ultrasound (US) method for determining the hepatic fat content (HFC) based on the combination of quantitative US hepatic/renal ratio (US-HRR) and quantitative US hepatic echo-intensity attenuation rate (US-HAR) as compared with [1H]-magnetic resonance spectroscopy (1H-MRS). There were a total of 242 subjects recruited in the present study. All subjects were examined for HFC by quantitative US and 1H-MRS methods. The QUS-HRR and QUS-HAR were calculated from ordinary ultrasound images of liver and kidney with a triple modality 3D abdominal phantom using the Image J software. The results found that US-HRR and US-HAR correlated with 1H-MRS HFC (US-HRR: r=0.946, p<0.001; US-HAR: r=0.936, p<0.001). The equation for HFC prediction by using quantitative US was: HFC (%) = 28.965 × US-HRR + 218.045 × US-HAR - 8.892. Subgroup analysis in study subjects with body mass index (BMI) ≥28 showed that quantitative US HFC was associated with 1H-MRS HFC (R2=0.953, p<0.001). Receiver operating characteristic (ROC) analysis observed that the cut-off value of fatty liver diagnosis was 6.71% in using the quantitative US model; the sensitivity and specificity for fatty liver diagnosis were 94.15% and 96.30%, respectively. Variability analysis indicated that there was a relative high degree of consistency in the measurement of HFC with different operators or ultrasonic apparatus. Quantitative US measurement could be regarded as a simple, sensitive tool to accurately assess HFC. It provides a valid alternative to 1H-MRS as an easy, non-invasive option for the precise estimation of HFC in clinical practice.

Automatic Segmentation and Analysis of Renal Calculi in Medical Ultrasound Images

Ultrasonography images have a high impact in the medical field for faster and accurate diagnosis of the diseases. The analysis and processing of ultrasound images is a tedious task. The proposed work focuses on automatic segmentation and analysis of renal calculi in digital ultrasound kidney images. The developed methodology includes steps such as preprocessing, segmentation and analysis. Preprocessing includes despeckling of input ultrasound images and is performed by using contourlet transform. Preprocessed images undergo automatic segmentation using the level set method. Analysis of the segmented stones is also carried out to obtain metrics such as the number of stones and their sizes. These metrics are essential to decide about the further plan of treatment by urologists and nephrologists. Performance of the developed algorithm is evaluated by the medical experts and also by using the various parameters such as dice similarity coefficient, Jaccard index, specificity, sensitivity, and accuracy.

An ensemble of deep neural networks for kidney ultrasound image classification

Background and objective: Chronic kidney disease is a worldwide health issue which includes not only kidney failure but also complications of reduced kidney functionality. Cyst formation, nephrolithiasis or kidney stone, and renal cell carcinoma or kidney tumor are the common kidney disorders which affects the functionality of kidneys. These disorders are typically asymptomatic, therefore early and automatic diagnosis of kidney disorders are required to avoid serious complications.Methods: This paper proposes an automatic classification of B-mode kidney ultrasound images based on the ensemble of deep neural networks (DNNs) using transfer learning. The ultrasound images are usually affected by speckle noise and quality selection in the ultrasound image is based on perception-based image quality evaluator score. Three variant datasets are given to the pre-trained DNN models for feature extraction followed by support vector machine for classification. The ensembling of different pre-trained DNNs like ResNet-101, ShuffleNet, and MobileNet-v2 are combined and final predictions are done by using the majority voting technique. By combining the predictions from multiple DNNs the ensemble model shows better classification performance than the individual models. The presented method proved its superiority when compared to the conventional and DNN based classification methods. The developed ensemble model classifies the kidney ultrasound images into four classes, namely, normal, cyst, stone, and tumor.Results: To highlight effectiveness of the proposed approach, the ensemble based approach is compared with the existing state-of-the-art methods and tested in the variants of ultrasound images like in quality and noisy conditions. The presented method resulted in maximum classification accuracy of 96.54% in testing with quality images and 95.58% in testing with noisy images. The performance of the presented approach is evaluated based on accuracy, sensitivity, and selectivity.Conclusions: From the experimental analysis, it is clear that the ensemble of DNNs classifies the majority of images correctly and results in maximum classification accuracy as compared to the existing methods. This automatic classification approach is a supporting tool for the radiologists and nephrologists for precise diagnosis of kidney diseases.