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Liu Y, He B, Zhang Y, Lang X, Yao R, Pan L. A Study on a Parameter Estimator for the Homodyned K Distribution Based on Table Search for Ultrasound Tissue Characterization. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:970-981. [PMID: 36631331 DOI: 10.1016/j.ultrasmedbio.2022.11.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 11/27/2022] [Accepted: 11/30/2022] [Indexed: 06/17/2023]
Abstract
OBJECTIVE The homodyned K (HK) distribution is considered to be the most suitable distribution in the context of tissue characterization; therefore, the search for a rapid and reliable parameter estimator for HK distribution is important. METHODS We propose a novel parameter estimator based on a table search (TS) for HK parameter estimates. The TS estimator can inherit the strength of conventional estimators by integrating various features and taking advantage of the TS method in a rapid and easy operation. Performance of the proposed TS estimator was evaluated and compared with that of XU (the estimation method based on X and U statistics) and artificial neural network (ANN) estimators. DISCUSSION The simulation results revealed that the TS estimator is superior to the XU and ANN estimators in terms of normalized standard deviations and relative root mean squared errors of parameter estimation, and is faster. Clinical experiments found that the area under the receiver operating curve for breast lesion classification using the parameters estimated by the TS estimator could reach 0.871. CONCLUSION The proposed TS estimator is more accurate, reliable and faster than the state-of-the-art XU and ANN estimators and has great potential for ultrasound tissue characterization based on the HK distribution.
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Affiliation(s)
- Yang Liu
- Department of Electronic Engineering, Information School, Yunnan University, Kunming, Yunnan, China
| | - Bingbing He
- Department of Electronic Engineering, Information School, Yunnan University, Kunming, Yunnan, China.
| | - Yufeng Zhang
- Department of Electronic Engineering, Information School, Yunnan University, Kunming, Yunnan, China
| | - Xun Lang
- Department of Electronic Engineering, Information School, Yunnan University, Kunming, Yunnan, China
| | - Ruihan Yao
- Department of Electronic Engineering, Information School, Yunnan University, Kunming, Yunnan, China
| | - Lingrui Pan
- Department of Electronic Engineering, Information School, Yunnan University, Kunming, Yunnan, China
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Yao R, Zhang Y, Wu K, Li Z, He M, Fengyue B. Quantitative assessment for characterization of breast lesion tissues using adaptively decomposed ultrasound RF images. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Su R, Li X, Liu Y, Cui W, Xie P, Han Y. Evaluation of the Brain Function State During Mild Cognitive Impairment Based on Weighted Multiple Multiscale Entropy. Front Aging Neurosci 2021; 13:625081. [PMID: 34393752 PMCID: PMC8362600 DOI: 10.3389/fnagi.2021.625081] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 06/23/2021] [Indexed: 11/13/2022] Open
Abstract
The mild cognitive impairment (MCI) stage plays an essential role in preventing the progression of older adults to Alzheimer's disease. In this study, neurofeedback training (NFT) is applied to improve MCI brain cognitive function. To assess the improvement effect, a novel algorithm called Weighted Multiple Multiscale Entropy (WMMSE) is proposed to extract and analyze the electroencephalogram (EEG) features of patients with MCI. To overcome the information loss problem of traditional multiscale entropy (MSE), WMMSE fully considered the correlation of the sequence and the contribution of each sequence to the total entropy. The experimental group composed of 39 patients with MCI was subjected to NFT for 10 days during two sessions. The control group included 21 patients with MCI without any intervention. The Lempel-Ziv complexity (LZC) was used for primary assessment, and WMMSE was used to accurately analyze the effect of NFT. The results show that the WMMSE values of F4, C3, C4, O1, and T5 channels post-NFT are higher compared with pre-NFT and significant differences (P < 0.05). Moreover, the cognitive subscale of the Montreal Cognitive Assessment (MoCA) results shows that the post-NFT score is higher than the pre-NFT in the vast majority of the patients with MCI and significant differences (P < 0.05). When compared with the control group, the WMMSE values of the experimental group increased in each channel. Therefore, the NFT intervention method contributes to brain cognitive functional recovery, and WMMSE can be used as a biomarker to evaluate the state of MCI brain cognitive function.
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Affiliation(s)
- Rui Su
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China
| | - Xin Li
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China
| | - Yi Liu
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China
| | - Wei Cui
- Handan First Central Hospital, Handan, China
| | - Ping Xie
- Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
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Wei M, Du Y, Wu X, Su Q, Zhu J, Zheng L, Lv G, Zhuang J. A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:5894010. [PMID: 33062038 PMCID: PMC7547332 DOI: 10.1155/2020/5894010] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 09/01/2020] [Accepted: 09/15/2020] [Indexed: 12/14/2022]
Abstract
The classification of benign and malignant based on ultrasound images is of great value because breast cancer is an enormous threat to women's health worldwide. Although both texture and morphological features are crucial representations of ultrasound breast tumor images, their straightforward combination brings little effect for improving the classification of benign and malignant since high-dimensional texture features are too aggressive so that drown out the effect of low-dimensional morphological features. For that, an efficient texture and morphological feature combing method is proposed to improve the classification of benign and malignant. Firstly, both texture (i.e., local binary patterns (LBP), histogram of oriented gradients (HOG), and gray-level co-occurrence matrixes (GLCM)) and morphological (i.e., shape complexities) features of breast ultrasound images are extracted. Secondly, a support vector machine (SVM) classifier working on texture features is trained, and a naive Bayes (NB) classifier acting on morphological features is designed, in order to exert the discriminative power of texture features and morphological features, respectively. Thirdly, the classification scores of the two classifiers (i.e., SVM and NB) are weighted fused to obtain the final classification result. The low-dimensional nonparameterized NB classifier is effectively control the parameter complexity of the entire classification system combine with the high-dimensional parametric SVM classifier. Consequently, texture and morphological features are efficiently combined. Comprehensive experimental analyses are presented, and the proposed method obtains a 91.11% accuracy, a 94.34% sensitivity, and an 86.49% specificity, which outperforms many related benign and malignant breast tumor classification methods.
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Affiliation(s)
- Mengwan Wei
- College of Engineering, Huaqiao University, Quanzhou 362021, China
| | - Yongzhao Du
- College of Engineering, Huaqiao University, Quanzhou 362021, China
- School of Medicine, Huaqiao University, Quanzhou 362021, China
- Collaborative Innovation Center for Maternal and Infant Health Service Application Technology, Quanzhou Medical College, Quanzhou, China
| | - Xiuming Wu
- The First Hospital of Quanzhou, Fujian Medical University, Quanzhou 350005, China
| | - Qichen Su
- Collaborative Innovation Center for Maternal and Infant Health Service Application Technology, Quanzhou Medical College, Quanzhou, China
- Department of Medical Ultrasonics, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
| | - Jianqing Zhu
- College of Engineering, Huaqiao University, Quanzhou 362021, China
| | - Lixin Zheng
- College of Engineering, Huaqiao University, Quanzhou 362021, China
| | - Guorong Lv
- Collaborative Innovation Center for Maternal and Infant Health Service Application Technology, Quanzhou Medical College, Quanzhou, China
- Department of Medical Ultrasonics, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
| | - Jiafu Zhuang
- Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences, 362216 Quanzhou, China
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Tai H, Khairalseed M, Hoyt K. 3-D H-Scan Ultrasound Imaging and Use of a Convolutional Neural Network for Scatterer Size Estimation. ULTRASOUND IN MEDICINE & BIOLOGY 2020; 46:2810-2818. [PMID: 32653207 PMCID: PMC7484237 DOI: 10.1016/j.ultrasmedbio.2020.06.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 05/25/2020] [Accepted: 06/01/2020] [Indexed: 05/29/2023]
Abstract
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 (3-D) 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 (4 DL7, 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 3-D H-scan US imaging can visualize the spatial distribution of acoustic scatterers of varying size at different concentrations (R2 > 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 3-D 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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Affiliation(s)
- Haowei Tai
- Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX, USA
| | - Mawia Khairalseed
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA
| | - Kenneth Hoyt
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA; Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX, USA.
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Classification of Benign and Malignant Breast Tumors Using H-Scan Ultrasound Imaging. Diagnostics (Basel) 2019; 9:diagnostics9040182. [PMID: 31717382 PMCID: PMC6963514 DOI: 10.3390/diagnostics9040182] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 10/29/2019] [Accepted: 11/07/2019] [Indexed: 12/28/2022] Open
Abstract
Breast cancer is one of the most common cancers among women worldwide. Ultrasound imaging has been widely used in the detection and diagnosis of breast tumors. However, due to factors such as limited spatial resolution and speckle noise, classification of benign and malignant breast tumors using conventional B-mode ultrasound still remains a challenging task. H-scan is a new ultrasound technique that images the relative size of acoustic scatterers. However, the feasibility of H-scan ultrasound imaging in the classification of benign and malignant breast tumors has not been investigated. In this paper, we proposed a new method based on H-scan ultrasound imaging to classify benign and malignant breast tumors. Backscattered ultrasound radiofrequency signals of 100 breast tumors were used (48 benign and 52 malignant cases). H-scan ultrasound images were constructed with the radiofrequency signals by matched filtering using Gaussian-weighted Hermite polynomials. Experimental results showed that benign breast tumors had more red components, while malignant breast tumors had more blue components in H-scan ultrasound images. There were significant differences between the RGB channels of H-scan ultrasound images of benign and malignant breast tumors. We conclude H-scan ultrasound imaging can be used as a new method for classifying benign and malignant breast tumors.
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