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Yang Z, Mu Y, Wang Y, He F, Shi L, Fang Z, Zhang J, Zhang Q, Geng G, Zhang S. Characterization of a Novel TtLEA2 Gene From Tritipyrum and Its Transformation in Wheat to Enhance Salt Tolerance. FRONTIERS IN PLANT SCIENCE 2022; 13:830848. [PMID: 35444677 PMCID: PMC9014267 DOI: 10.3389/fpls.2022.830848] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 02/11/2022] [Indexed: 05/12/2023]
Abstract
Late embryogenesis-abundant (LEA) proteins are critical in helping plants cope with salt stress. "Y1805" is a salt-tolerant Tritipyrum. We identified a "Y1805"-specific LEA gene that was expressed highly and sensitively under salt stress using transcriptome analysis. The novel group 2 LEA gene (TtLEA2-1) was cloned from "Y1805." TtLEA2-1 contained a 453 bp open reading frame encoding an 151-amino-acid protein that showed maximum sequence identity (77.00%) with Thinopyrum elongatum by phylogenetic analysis. It was mainly found to be expressed highly in the roots by qRT-PCR analysis and was located in the whole cell. Forty-eight candidate proteins believed to interact with TtLEA2-1 were confirmed by yeast two-hybrid analysis. These interacting proteins were mainly enriched in "environmental information processing," "glycan biosynthesis and metabolism," and "carbohydrate metabolism." Protein-protein interaction analysis indicated that the translation-related 40S ribosomal protein SA was the central node. An efficient wheat transformation system has been established. A coleoptile length of 2 cm, an Agrobacteria cell density of 0.55-0.60 OD600, and 15 KPa vacuum pressure were ideal for common wheat transformation, with an efficiency of up to 43.15%. Overexpression of TaLEA2-1 in wheat "1718" led to greater height, stronger roots, and higher catalase activity than in wild type seedlings. TaLEA2-1 conferred enhanced salt tolerance in transgenic wheat and may be a valuable gene for genetic modification in crops.
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Affiliation(s)
- Zhifen Yang
- College of Agriculture, Guizhou University, Guiyang, China
| | - Yuanhang Mu
- College of Agriculture, Guizhou University, Guiyang, China
| | - Yiqin Wang
- College of Agriculture, Guizhou University, Guiyang, China
| | - Fang He
- College of Agriculture, Guizhou University, Guiyang, China
- Guizhou Subcenter of National Wheat Improvement Center, Guiyang, China
| | - Luxi Shi
- College of Agriculture, Guizhou University, Guiyang, China
| | - Zhongming Fang
- College of Agriculture, Guizhou University, Guiyang, China
| | - Jun Zhang
- College of Agriculture, Guizhou University, Guiyang, China
| | - Qingqin Zhang
- College of Agriculture, Guizhou University, Guiyang, China
| | - Guangdong Geng
- College of Agriculture, Guizhou University, Guiyang, China
- *Correspondence: Guangdong Geng,
| | - Suqin Zhang
- College of Agriculture, Guizhou University, Guiyang, China
- Guizhou Subcenter of National Wheat Improvement Center, Guiyang, China
- Suqin Zhang,
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Zhou J, Pan F, Li W, Hu H, Wang W, Huang Q. Feature Fusion for Diagnosis of Atypical Hepatocellular Carcinoma in Contrast- Enhanced Ultrasound. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2022; 69:114-123. [PMID: 34487493 DOI: 10.1109/tuffc.2021.3110590] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Contrast-enhanced ultrasound (CEUS) is generally employed for focal liver lesions (FLLs) diagnosis. Among the FLLs, atypical hepatocellular carcinoma (HCC) is difficult to distinguish from focal nodular hyperplasia (FNH) in CEUS video. For this reason, we propose and evaluate a feature fusion method to resolve this problem. The proposed algorithm extracts a set of hand-crafted features and the deep features from the CEUS cine clip data. The hand-crafted features include the spatial-temporal feature based on a novel descriptor called Velocity-Similarity and Dissimilarity Matching Local Binary Pattern (V-SDMLBP), and the deep features from a 3-D convolution neural network (3D-CNN). Then the two types of features are fused. Finally, a classifier is employed to diagnose HCC or FNH. Several classifiers have achieved excellent performance, which demonstrates the superiority of the fused features. In addition, compared with general CNNs, the proposed fused features have better interpretability.
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Wen H, Zheng W, Li M, Li Q, Liu Q, Zhou J, Liu Z, Chen X. Multiparametric Quantitative US Examination of Liver Fibrosis: A Feature-engineering and Machine-learning Based Analysis. IEEE J Biomed Health Inform 2021; 26:715-726. [PMID: 34329172 DOI: 10.1109/jbhi.2021.3100319] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Quantitative ultrasound (QUS), which is commonly used to extract quantitative features from the ultrasound radiofrequency (RF) data or the RF envelope signals for tissue characterization, is becoming a promising technique for noninvasive assessments of liver fibrosis. However, the number of feature variables examined and finally used in the existing QUS methods is typically small, to some extent limiting the diagnostic performance. Therefore, this paper devises a new multiparametric QUS (MP-QUS) method which enables the extraction of a large number of feature variables from US RF signals and allows for the use of feature-engineering and machinelearning based algorithms for liver fibrosis assessment. In the MP-QUS, eighty-four feature variables were extracted from multiple QUS parametric maps derived from the RF signals and the envelope data. Afterwards, feature reduction and selection were performed in turn to remove the feature redundancy and identify the best combination of features in the reduced feature set. Finally, a variety of machine-learning algorithms were tested for classifying liver fibrosis with the selected features, based on the results of which the optimal classifier was established and used for final classification. The performance of the proposed MPQUS method for staging liver fibrosis was evaluated on an animal model, with histologic examination as the reference standard. The mean accuracy, sensitivity, specificity and area under the receiver-operating-characteristic curve achieved by MP-QUS are respectively 83.38%, 86.04%, 80.82% and 0.891 for recognizing significant liver fibrosis, and 85.50%, 88.92%, 85.24% and 0.924 for diagnosing liver cirrhosis. The proposed MP-QUS method paves a way for its future extension to assess liver fibrosis in human subjects.
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Multiswarm heterogeneous binary PSO using win-win approach for improved feature selection in liver and kidney disease diagnosis. Comput Med Imaging Graph 2018; 70:135-154. [DOI: 10.1016/j.compmedimag.2018.10.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Revised: 09/26/2018] [Accepted: 10/12/2018] [Indexed: 01/26/2023]
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Bharti P, Mittal D, Ananthasivan R. Characterization of chronic liver disease based on ultrasound images using the variants of grey-level difference matrix. Proc Inst Mech Eng H 2018; 232:884-900. [DOI: 10.1177/0954411918796531] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Chronic liver diseases are fifth leading cause of fatality in developing countries. Early diagnosis is important for timely treatment and to salvage life. Ultrasound imaging is frequently used to examine abnormalities of liver. However, ambiguity lies in visual interpretation of liver stages on ultrasound images. This difficult visualization problem can be solved by analysing extracted textural features from images. Grey-level difference matrix, a texture feature extraction method, can provide information about roughness of liver surface, sharpness of liver borders and echotexture of liver parenchyma. In this article, the behaviour of variants of grey-level difference matrix in characterizing liver stages is investigated. The texture feature sets are extracted by using variants of grey-level difference matrix based on two, three, five and seven neighbouring pixels. Thereafter, to take the advantage of complementary information from extracted feature sets, feature fusion schemes are implemented. In addition, hybrid feature selection (combination of ReliefF filter method and sequential forward selection wrapper method) is used to obtain optimal feature set in characterizing liver stages. Finally, a computer-aided system is designed with the optimal feature set to classify liver health in terms of normal, chronic liver, cirrhosis and hepatocellular carcinoma evolved over cirrhosis. In the proposed work, experiments are performed to (1) identify the best approximation of derivative (forward, central or backward); (2) analyse the performance of individual feature sets of variants of grey-level difference matrix; (3) obtain optimal feature set by exploiting the complementary information from variants of grey-level difference matrix and (4) analyse the performance of proposed method in comparison with existing feature extraction methods. These experiments are carried out on database of 754 segmented regions of interest formed by clinically acquired ultrasound images. The results show that classification accuracy of 94.5% is obtained by optimal feature set having complementary information from variants of grey-level difference matrix.
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Affiliation(s)
- Puja Bharti
- Department of Electrical & Instrumental Engineering, Thapar Institute of Engineering & Technology, Patiala, India
| | - Deepti Mittal
- Department of Electrical & Instrumental Engineering, Thapar Institute of Engineering & Technology, Patiala, India
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Bharath R, Rajalakshmi P, Mateen MA. Multi-modal framework for automatic detection of diagnostically important regions in nonalcoholic fatty liver ultrasonic images. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2018.03.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Automated quantification of ultrasonic fatty liver texture based on curvelet transform and SVD. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2017.12.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Bharath R, Rajalakshmi P. Deep scattering convolution network based features for ultrasonic fatty liver tissue characterization. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:1982-1985. [PMID: 29060283 DOI: 10.1109/embc.2017.8037239] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Accumulation of excess fat in the liver tissue is the leading cause for dysfunction of liver, which can lead to the diseases from fibrosis to end stage cirrhosis. Hence, early detection of fatty liver becomes crucial in avoiding the liver from permanent failure. Depending on the concentration of fat in the tissue, the liver is classified as Normal, Grade 1, Grade 2 and Grade 3 respectively. The texture of liver tissue in ultrasound image is so specific to the concentration of fat, hence classifying the fatty liver is formulated as a texture discrimination problem. In this paper, we present an automated algorithm for grading the tissue of a fatty liver based on the features obtained from the invariant scattering convolution network (ISCN). ISCN, which involves cascade of modulus complex wavelet transforms and averaging operations results in scattering coefficients (SC), these coefficients will give stable invariant representations and also maps the texture of fatty liver image to a discriminative manifold giving good features for classification. SC are of high dimension and hence a compact representation feature is obtained by summing all the SC coefficients. Summed SC features along with cubic SVM classifier gave an accuracy of 96.6% in automatically categorizing the fatty content present in the tissue of a liver.
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Liu X, Song JL, Wang SH, Zhao JW, Chen YQ. Learning to Diagnose Cirrhosis with Liver Capsule Guided Ultrasound Image Classification. SENSORS 2017; 17:s17010149. [PMID: 28098774 PMCID: PMC5298722 DOI: 10.3390/s17010149] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2016] [Revised: 12/27/2016] [Accepted: 01/10/2017] [Indexed: 12/13/2022]
Abstract
This paper proposes a computer-aided cirrhosis diagnosis system to diagnose cirrhosis based on ultrasound images. We first propose a method to extract a liver capsule on an ultrasound image, then, based on the extracted liver capsule, we fine-tune a deep convolutional neural network (CNN) model to extract features from the image patches cropped around the liver capsules. Finally, a trained support vector machine (SVM) classifier is applied to classify the sample into normal or abnormal cases. Experimental results show that the proposed method can effectively extract the liver capsules and accurately classify the ultrasound images.
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Affiliation(s)
- Xiang Liu
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 201203, China.
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
| | - Jia Lin Song
- Department of ultrasound, Changzheng Hospital Affiliated to Second Military Medical University, Shanghai 200003, China.
| | - Shuo Hong Wang
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 201203, China.
| | - Jing Wen Zhao
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 201203, China.
| | - Yan Qiu Chen
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 201203, China.
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Bharti P, Mittal D, Ananthasivan R. Computer-aided Characterization and Diagnosis of Diffuse Liver Diseases Based on Ultrasound Imaging: A Review. ULTRASONIC IMAGING 2017; 39:33-61. [PMID: 27097589 DOI: 10.1177/0161734616639875] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Diffuse liver diseases, such as hepatitis, fatty liver, and cirrhosis, are becoming a leading cause of fatality and disability all over the world. Early detection and diagnosis of these diseases is extremely important to save lives and improve effectiveness of treatment. Ultrasound imaging, a noninvasive diagnostic technique, is the most commonly used modality for examining liver abnormalities. However, the accuracy of ultrasound-based diagnosis depends highly on expertise of radiologists. Computer-aided diagnosis systems based on ultrasound imaging assist in fast diagnosis, provide a reliable "second opinion" for experts, and act as an effective tool to measure response of treatment on patients undergoing clinical trials. In this review, we first describe appearance of liver abnormalities in ultrasound images and state the practical issues encountered in characterization of diffuse liver diseases that can be addressed by software algorithms. We then discuss computer-aided diagnosis in general with features and classifiers relevant to diffuse liver diseases. In later sections of this paper, we review the published studies and describe the key findings of those studies. A concise tabular summary comparing image database, features extraction, feature selection, and classification algorithms presented in the published studies is also exhibited. Finally, we conclude with a summary of key findings and directions for further improvements in the areas of accuracy and objectiveness of computer-aided diagnosis.
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Affiliation(s)
- Puja Bharti
- 1 Department of Electrical and Instrumentation Engineering, Thapar University, Patiala, India
| | - Deepti Mittal
- 1 Department of Electrical and Instrumentation Engineering, Thapar University, Patiala, India
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Aggarwal K, Bhamrah MS, Ryait HS. The identification of liver cirrhosis with modified LBP grayscaling and Otsu binarization. SPRINGERPLUS 2016; 5:322. [PMID: 27065292 PMCID: PMC4788659 DOI: 10.1186/s40064-016-1970-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2015] [Accepted: 03/03/2016] [Indexed: 11/10/2022]
Abstract
Liver cirrhosis is considered as one of the most common diseases in healthcare. The widely accepted technology for the diagnosis of liver cirrhosis is via ultrasound imaging. This paper presents a technique for detecting the cirrhosis of liver through ultrasound images. The region of interest has been selected from these ultrasound images and endorsed from a radiologist. The identification of liver cirrhosis is finally detected through modified local binary pattern and OTSU methods. Experimental results from the proposed method demonstrated its feasibility and applicability for high performance cirrhotic liver identification.
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Affiliation(s)
- Karan Aggarwal
- Department of Electronics and Communication Engineering, Punjabi University, Patiala, India
| | - Manjit Singh Bhamrah
- Department of Electronics and Communication Engineering, Punjabi University, Patiala, India
| | - Hardeep Singh Ryait
- Electronics and Communication Engineering Department, BBSBC, Fatehgarh Sahib, India
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12
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Alivar A, Danyali H, Helfroush MS. Hierarchical classification of normal, fatty and heterogeneous liver diseases from ultrasound images using serial and parallel feature fusion. Biocybern Biomed Eng 2016. [DOI: 10.1016/j.bbe.2016.07.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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13
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Lee WL, Chang K, Hsieh KS. Unsupervised segmentation of lung fields in chest radiographs using multiresolution fractal feature vector and deformable models. Med Biol Eng Comput 2015; 54:1409-22. [PMID: 26530048 DOI: 10.1007/s11517-015-1412-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Accepted: 10/19/2015] [Indexed: 10/22/2022]
Abstract
Segmenting lung fields in a chest radiograph is essential for automatically analyzing an image. We present an unsupervised method based on multiresolution fractal feature vector. The feature vector characterizes the lung field region effectively. A fuzzy c-means clustering algorithm is then applied to obtain a satisfactory initial contour. The final contour is obtained by deformable models. The results show the feasibility and high performance of the proposed method. Furthermore, based on the segmentation of lung fields, the cardiothoracic ratio (CTR) can be measured. The CTR is a simple index for evaluating cardiac hypertrophy. After identifying a suspicious symptom based on the estimated CTR, a physician can suggest that the patient undergoes additional extensive tests before a treatment plan is finalized.
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Affiliation(s)
- Wen-Li Lee
- Department of Healthcare Information and Management, Ming Chuan University, Taoyuan, 333, Taiwan, ROC.
| | - Koyin Chang
- Department of Healthcare Information and Management, Ming Chuan University, Taoyuan, 333, Taiwan, ROC
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