Adaptive attenuation correction during H-scan ultrasound imaging using K-means clustering

Tai, Haowei; Khairalseed, Mawia; Hoyt, Kenneth

doi:10.1016/j.ultras.2019.105987

Cited by 32 publications

(11 citation statements)

References 21 publications

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“…WNN, compared with traditional NN, has higher prediction accuracy, fault-tolerant ability and faster convergence speed. It has been proved that WNN has exceptional performance in terms of data modeling and forecasting [28][29][30][31], widely used in the field of prediction.…”

Section: B Proposed Rfr-wnn Methodsmentioning

confidence: 99%

A Mixed Optimization Method Based on Adaptive Kalman Filter and Wavelet Neural Network for INS/GPS During GPS Outages

Wei

Feng

et al. 2021

IEEE Access

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To improve the navigation performance of the navigation system combining inertial navigation system (INS) and global positioning system (GPS) under complicated environments, especially GPS outages, a navigation method -wavelet neural network based on random forest regression (RFR-WNN) to assist adaptive Kalman filter (AKF) -is proposed. AKF is employed to correct INS errors, the Kalman filter is improved by introducing adaptive factor, to suppress the influence of the complex environment and random errors on the filtering accuracy; RFR-WNN is used to construct a high-precision prediction model when GPS works well, and to provide the required observations for AKF update when GPS outages. To solve the problem that the single neural network structure is easy to cause the overfitting, unstable and low prediction accuracy due to the lack of comprehensive training samples, RFR is introduced to optimize the single WNN, which can improve the generalization ability and prediction accuracy. In order to verify the effectiveness and advancement of the proposed method, vehicle navigation experiments were carried out, the results indicate that the proposed method has better navigation accuracy and performance than compared methods during GPS outages, and this advantage is more obvious in the case that fewer samples are collected.INDEX TERMS Navigation technology, integrated navigation, wavelet neural network, adaptive Kalman filter, GPS outages, random forest regression.

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Section: B Proposed Rfr-wnn Methodsmentioning

confidence: 99%

A Mixed Optimization Method Based on Adaptive Kalman Filter and Wavelet Neural Network for INS/GPS During GPS Outages

Wei

Feng

et al. 2021

IEEE Access

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“…H. Tai et al [68] purposed to establish a novel method of attenuation correction based on adaptive clustering of Kmeans. In order to enhance the discernment of these signals, it was possible to adjust GH filters by using a side-moving window technique.…”

Section: )mentioning

confidence: 99%

Medical Images Segmentation Based on Unsupervised Algorithms: A Review

Armya

Abdulazeez

2021

QAJ

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Medical image segmentation plays an essential role in computer-aided diagnostic systems in various applications. Therefore, researchers are attracted to apply new algorithms for medical image processing because it is a massive investment in developing medical imaging methods such as dermatoscopy, X-rays, microscopy, ultrasound, computed tomography (CT), positron emission tomography, and magnetic resonance imaging. (Magnetic Resonance Imaging), So segmentation of medical images is considered one of the most important medical imaging processes because it extracts the field of interest from the Return on investment (ROI) through an automatic or semi-automatic process. The medical image is divided into regions based on the specific descriptions, such as tissue/organ division in medical applications for border detection, tumor detection/segmentation, and comprehensive and accurate detection. Several methods of segmentation have been proposed in the literature, but their efficacy is difficult to compare. To better address, this issue, a variety of measurement standards have been suggested to decide the consistency of the segmentation outcome. Unsupervised ranking criteria use some of the statistics in the hash score based on the original picture. The key aim of this paper is to study some literature on unsupervised algorithms (K-mean, K-medoids) and to compare the working efficiency of unsupervised algorithms with different types of medical images.

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“…ω ω − ω ρ dω, which can be calculated by a depth-adaptive K-means clustering algorithm (Huang 1998;Juang and Rabiner 1990;Krishna and Murty 1999;Tai et al 2019b). The center frequencies of the GH n kernels were independently and continuously adjusted at all depths to maximize spectral coverage (see Figure 1), and the filtered signals were combined via an overlap-add method (Crochiere 1980;Narasimha 2006).…”

Section: Attenuation Correctionmentioning

confidence: 99%

3-D H-Scan Ultrasound Imaging and Use of a Convolutional Neural Network for Scatterer Size Estimation

Tai

Khairalseed

Hoyt

2020

Ultrasound in Medicine & Biology

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H-scan ultrasound (US) is a new imaging technology that estimates the relative size of acoustic scattering objects and structures. The purpose of this study was to introduce a three-dimensional (3D) H-scan US imaging approach for scatterer size estimation in volume space. Using a programmable research scanner (Vantage 256, Verasonics Inc, Kirkland, WA, USA) equipped with a custom volumetric imaging transducer (4DL7, Vermon, Tours, France), raw radiofrequency (RF) data was collected for offline processing to generate H-scan US volumes. A deep convolutional neural network (CNN) was modified and used to achieve voxel mapping from the input H-scan US image to underlying scatterer size. Preliminary studies were conducted using homogeneous gelatin-based tissue-mimicking phantom materials embedded with acoustic scatterers of varying size (15 to 250 μm) and concentrations (0.1 to 1%). Two additional phantoms were embedded with 63 or 125 μm-sized microspheres and used to test CNN estimation accuracy. In vitro results indicate that 3D H-scan US imaging can visualize the spatial distribution of acoustic scatterers of varying size at different concentrations (R 2 > 0.85, p < 0.03). The result of scatterer size estimation reveals that a CNN can achieve an average mapping accuracy of 93.3%. Overall, our preliminary in vitro findings reveal that 3D H-scan US imaging allows the visualization of tissue scatterer patterns and incorporation of a CNN can be used to help estimate size of the acoustic scattering objects.

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Adaptive attenuation correction during H-scan ultrasound imaging using K-means clustering

Cited by 32 publications

References 21 publications

A Mixed Optimization Method Based on Adaptive Kalman Filter and Wavelet Neural Network for INS/GPS During GPS Outages

A Mixed Optimization Method Based on Adaptive Kalman Filter and Wavelet Neural Network for INS/GPS During GPS Outages

Medical Images Segmentation Based on Unsupervised Algorithms: A Review

3-D H-Scan Ultrasound Imaging and Use of a Convolutional Neural Network for Scatterer Size Estimation

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