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image-Based ultrasound Speed Estimation: Phantom and Human Liver Studies

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Open Journal of Radiology 2023年第2期13卷 101-112页

作者： Jianfeng Chen Junguo Bian Zuhaib Khokhar Mohamed Belal Emad Allam Department of Radiology and Medical Imaging Stritch School of Medicine Loyola University Medical Center Chicago Illinois USA

Purpose: A novel image-based method for speed of sound (SoS) estimation is proposed and experimentally validated on a tissue-mimicking ultrasound phantom and normal human liver in vivo using linear and curved array transducers. Methods: When the beamforming SoS settings are adjusted to match the real tissue’s SoS, the ultrasound image at regions of interest will be in focus and the image quality will be optimal. Based on this principle, both a tissue-mimicking ultrasound phantom and normal human liver in vivo were used in this study. ultrasound image was acquired using different SoS settings in beamforming channels ranging from 1420 m/sec to 1600 m/sec. Two regions of interest (ROIs) were selected. One was in a fully developed speckle region, while the other contained specular reflectors. We evaluated the image quality of these two ROIs in images acquired at different SoS settings in beamforming channels by using the normalized autocorrelation function (ACF) of the image data. The values of the normalized ACF at a specific lag as a function of the SoS setting were computed. Subsequently, the soft tissue’s SoS was determined from the SoS setting at the minimum value of the normalized ACF. Results: The value of the ACF as a function of the SoS setting can be computed for phantom and human liver images. SoS in soft tissue can be determined from the SoS setting at the minimum value of the normalized ACF. The estimation results show that the SoS of the tissue-mimicking phantom is 1460 m/sec, which is consistent with the phantom manufacturer’s specification, and the SoS of the normal human liver is 1540 m/sec, which is within the range of the SoS in a healthy human liver in vivo. Conclusion: Soft tissue’s SoS can be determined by analyzing the normalized ACF of ultrasound images. The method is based on searching for a minimum of the normalized ACF of ultrasound image data with a specific lag among different SoS settings in beamforming channels.

关键词： ultrasound image Normalized Autocorrelation Function (ACF) Speed of Sound (SoS)

Difficulty-aware bi-network with spatial attention constrained graph for axillary lymph node segmentation

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Science China(Information Sciences) 2022年第9期65卷 74-85页

作者： Qing XU Xiaoming XI Xianjing MENG Zheyun QIN Xiushan NIE Yongjian WU Dongsheng ZHOU Yi QU Chenglong LI Yilong YIN School of Software Shandong University School of Computer Science and Technology Shandong Jianzhu University School of Computer Science and Technology Shandong University of Finance and Economics Shandong Provinvial Qianfoshan Hospital The First Hospital Affiliated with Shandong First Medical University Department of Geriatrics Qilu Hospital of Shandong University

Axillary lymph node(ALN) segmentation in ultrasound images is important for the diagnosis and treatment of breast cancer. Recently, deep learning methods for automatic medical image segmentation have improved significantly. However, two problems arise.(1) A unified model is often employed to segment all images without considering the difficulty diversity.(2) The relationship between elements in the learned class probability map is disregarded. To address these two issues, we propose a novel difficulty-aware binetwork with a spatial attention constrained graph. First, a difficulty grading module(DGM) is developed to learn the difficulty grade of input images. Based on the difficulty grade of images, a novel bi-network architecture is proposed to segment the image adaptively using different branches. In complex branches,a novel spatial attention module(SAM) and graph-based energy with spatial attention constraint are proposed. The learned spatial attention map can provide additional discriminative information. Moreover,the graph-based segmentation framework can capture the relationship between pixels, further improving the segmentation performance for complex images. We conducted an experiment on our ultrasound database using 216 cases. The overall dice similarity coefficient, Jaccard coefficient, volumetric overlap error, and false positive rate are 83.41%, 74.4%, 12.02%, and 13.36% for ALN segmentation, respectively. The comparison results demonstrated that the proposed method outperforms other deep learning methods.

关键词： ultrasound image axillary lymph nodes segmentation difficulty-aware segmentation graph with spatial attention

Estimation of muscle pennation angle in ultrasound images using the beamlet transform

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Journal of Shanghai University(English Edition) 2010年第1期14卷 34-38页

作者：施俊王锐玲 School of Communication and Information Engineering Shanghai University

ultrasound imaging has been widely used to investigate the architecture properties of skeletal muscle, including the measurement of the pennation angle. In this study, we propose a beamlet-based algorithm to detect the straight line- shaped patterns of aponeurosis, fascicle or bone, and then to quantify the pennation angle ix1 ultrasound images. The results demonstrate that the proposed algorithm can well detect the pennatoin angles in thirty ultrasound images with the correlation coefficient of 0.945, the standard root mean square error of 0.682°, and the relative root mean square error of 3.498%. The results suggest that this beamlet-based algorithm provides an alternative approach for the orientation estimation in muscu- loskeletal ultrasound images.

关键词： skeletal muscle pennation angle ultrasound image beamlet transform

Multi-Level Fusion in ultrasound for Cancer Detection Based on Uniform LBP Features

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Computers, Materials & Continua 2021年第3期66卷 3363-3382页

作者： Diyar Qader Zeebaree Adnan Mohsin Abdulazeez Dilovan Asaad Zebari Habibollah Haron Haza Nuzly Abdull Hamed Research Center of Duhok Polytechnic University DuhokKurdistan RegionIraq Presidency of Duhok Polytechnic University Duhok Kurdistan RegionIraq Center of Scientific Research and Development Nawroz University DuhokKurdistan RegionIraq School of Computing University Teknologi MalaysiaJohorMalaysia

Collective improvement in the acceptable or desirable accuracy level of breast cancer image-related pattern recognition using various schemes remains *** the combination of multiple schemes to achieve superior ultrasound image pattern recognition by reducing the speckle noise,an enhanced technique is not *** purpose of this study is to introduce a features-based fusion scheme based on enhancement uniform-Local Binary Pattern(LBP)and filtered noise *** surmount the above limitations and achieve the aim of the study,a new descriptor that enhances the LBP features based on the new threshold has been *** paper proposes a multi-level fusion scheme for the auto-classification of the static ultrasound images of breast cancer,which was attained in two ***,several images were generated from a single image using the pre-processing *** andWiener filterswere utilized to lessen the speckle noise and enhance the ultrasound image *** strategy allowed the extraction of a powerful feature by reducing the overlap between the benign and malignant image ***,the fusion mechanism allowed the production of diverse features from different filtered *** feasibility of using the LBP-based texture feature to categorize the ultrasound images was *** effectiveness of the proposed scheme is tested on 250 ultrasound images comprising 100 and 150 benign and malignant images,*** proposed method achieved very high accuracy(98%),sensitivity(98%),and specificity(99%).As a result,the fusion process that can help achieve a powerful decision based on different features produced from different filtered images improved the results of the new descriptor of LBP features in terms of accuracy,sensitivity,and specificity.

关键词： Breast cancer ultrasound image local binary pattern feature extraction noise reduction filters fusion

Ultrasonographic Segmentation of Fetal Lung with Deep Learning

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Journal of Biosciences and Medicines 2021年第1期9卷 146-153页

作者： Jintao Yin Jiawei Li Qinghua Huang Yucheng Cao Xiaoqian Duan Bing Lu Xuedong Deng Qingli Li Jiangang Chen Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Department of Medical ultrasound Fudan University Shanghai Cancer Center Department of Oncology Shanghai Medical College Fudan University Shanghai China School of Mechanical Engineering Northwestern Polytechnical University Xi’an China Center for Medical ultrasound Nanjing Medical University Affiliated Suzhou Hospital Suzhou China

The morbidity and mortality of the fetus is related closely with the neonatal respiratory morbidity, which was caused by the immaturity of the fetal lung primarily. The amniocentesis has been used in clinics to evaluate the maturity of the fetal lung, which is invasive, expensive and time-consuming. Ultrasonography has been developed to examine the fetal lung quantitatively in the past decades as a non-invasive method. However, the contour of the fetal lung required by existing studies was delineated in manual. An automated segmentation approach could not only improve the objectiveness of those studies, but also offer a quantitative way to monitor the development of the fetal lung in terms of morphological parameters based on the segmentation. In view of this, we proposed a deep learning model for automated fetal lung segmentation and measurement. The model was constructed based on the U-Net. It was trained by 3500 data sets augmented from 250 ultrasound images with both the fetal lung and heart manually delineated, and then tested on 50 ultrasound data sets. With the proposed method, the fetal lung and cardiac area were automatically segmented with the accuracy, average IoU, sensitivity and precision being 0.98, 0.79, 0.881 and 0.886, respectively.

关键词： Fetal Lung Fetal Heart ultrasound image Segmentation Deep Learning

Cheetah:A Library for Parallel ultrasound Beamforming in Multi-Core Systems

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Journal of Applied Mathematics and Physics 2015年第8期3卷 1056-1061页

作者： David Romero-Laorden Carlos Julián Martín-Arguedas Javier Villazón-Terrazas Oscar Martinez-Graullera Matilde Santos Penas César Gutierrez-Fernandez Ana Jiménez Martín ITEFI Spanish National Research CouncilMadridSpain Department of Electronics University of AlcaláAlcaláde HenaresSpain Department of Computer Architecture and Automation Complutense University of MadridMadridSpain

Developing new imaging methods needs to establish some proofs of concept before implementing them on real-time scenarios. Nowadays, the high computational power reached by multi-core CPUs and GPUs have driven the development of software-based beamformers. Taking this into account, a library for the fast generation of ultrasound images is presented. It is based on Synthetic Aperture Imaging Techniques (SAFT) and it is fast because of the use of parallel computing techniques. Any kind of transducers as well as SAFT techniques can be defined although it includes some pre-built SAFT methods like 2R-SAFT and TFM. Furthermore, 2D and 3D imaging (slice- based or full volume computation) is supported along with the ability to generate both rectangular and angular images. For interpolation, linear and polynomial schemes can be chosen. The versatility of the library is ensured by interfacing it to Matlab, Python and any programming language over different operating systems. On a standard PC equipped with a single NVIDIA Quadro 4000 (256 cores), the library is able to calculate 262,144 pixels in ≈105 ms using a linear transducer with 64 elements, and 2,097,152 voxels in ≈ 5 seconds using a matrix transducer with 121 elements when TFM is applied.

关键词： GPGPU ultrasound image Beamforming Array Transducer

Ultrasonographic Segmentation of Fetal Lung with Deep Learning

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Ultrasonographic Segmentation of Fetal Lung with Deep Learni...