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Baruah HG, Nath VK, Hazarika D, Hatibaruah R. Local bit-plane neighbour dissimilarity pattern in non-subsampled shearlet transform domain for bio-medical image retrieval. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:1609-1632. [PMID: 35135220 DOI: 10.3934/mbe.2022075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
This paper introduces a novel descriptor non-subsampled shearlet transform (NSST) local bit-plane neighbour dissimilarity pattern (NSST-LBNDP) for biomedical image retrieval based on NSST, bit-plane slicing and local pattern based features. In NSST-LBNDP, the input image is first decomposed by NSST, followed by introduction of non-linearity on the NSST coefficients by computing local energy features. The local energy features are next normalized into 8-bit values. The multiscale NSST is used to provide translational invariance and has flexible directional sensitivity to catch more anisotropic information of an image. The normalised NSST subband features are next decomposed into bit-plane slices in order to capture very fine to coarse subband details. Then each bit-plane slices of all the subbands are encoded by exploiting the dissimilarity relationship between each neighbouring pixel and its adjacent neighbours. Experiments on two computed tomography (CT) and one magnetic resonance imaging (MRI) image datasets confirms the superior results of NSST-LBNDP when compared to many recent well known relevant descriptors both in terms of average retrieval precision (ARP) and average retrieval recall (ARR).
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Affiliation(s)
- Hilly Gohain Baruah
- Department of Electronics and Communication Engineering, School of Engineering, Tezpur University, Napaam, Tezpur, Assam 784028, India
| | - Vijay Kumar Nath
- Department of Electronics and Communication Engineering, School of Engineering, Tezpur University, Napaam, Tezpur, Assam 784028, India
| | - Deepika Hazarika
- Department of Electronics and Communication Engineering, School of Engineering, Tezpur University, Napaam, Tezpur, Assam 784028, India
| | - Rakcinpha Hatibaruah
- Department of Electronics and Communication Engineering, School of Engineering, Tezpur University, Napaam, Tezpur, Assam 784028, India
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Samuel AZ, Horii S, Ando M, Takeyama H. Deconstruction of Obscure Features in SVD-Decomposed Raman Images from P. chrysogenum Reveals Complex Mixing of Spectra from Five Cellular Constituents. Anal Chem 2021; 93:12139-12146. [PMID: 34445869 DOI: 10.1021/acs.analchem.1c02942] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Raman imaging has transcended in recent times from being an analytical tool to a molecular profiling technique. Biomedical applications of this technique often rely on singular-value decomposition (SVD), principal component analysis (PCA), etc. for data analysis. These methods, however, obliterate the molecular information contained in the original Raman data leading to speculative interpretations based on relative intensities. In the present study, SVD analysis of the Raman images from Penicillium chrysogenum resulted in 11 spectral components and corresponding images with highly distorted spectral features and complex image contrast, respectively. To interpret the SVD results in molecular terms, we have developed a combined multivariate approach. By applying this methodology, we have successfully extracted the contribution of five biomolecular constituents of the P. chrysogenum filamentous cell to the SVD vectors. Molecular interpretability will help SVD/PCA surpass the realm of variance-based classification to a more meaningful molecular domain.
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Affiliation(s)
- Ashok Zachariah Samuel
- Research Organization for Nano and Life Innovations, Waseda University, 513, Wasedatsurumaki-cho, Shinjuku-ku, Tokyo 162-0041, Japan
| | - Shumpei Horii
- Computational Bio Big-Data Open Innovation Laboratory, AIST-Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan.,Department of Advanced Science Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Masahiro Ando
- Research Organization for Nano and Life Innovations, Waseda University, 513, Wasedatsurumaki-cho, Shinjuku-ku, Tokyo 162-0041, Japan
| | - Haruko Takeyama
- Computational Bio Big-Data Open Innovation Laboratory, AIST-Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan.,Department of Life Science and Medical Bioscience, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan.,Institute for Advanced Research of Biosystem Dynamics, Waseda Research Institute for Science and Engineering, Graduate School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
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A Framework for Classification of Gabor Based Frequency Selective Bone Radiographs Using CNN. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-021-05339-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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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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5
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Ding H, He Q, Zhou Y, Dan G, Cui S. An Individual Finger Gesture Recognition System Based on Motion-Intent Analysis Using Mechanomyogram Signal. Front Neurol 2017; 8:573. [PMID: 29167655 PMCID: PMC5682314 DOI: 10.3389/fneur.2017.00573] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2017] [Accepted: 10/12/2017] [Indexed: 12/03/2022] Open
Abstract
Motion-intent-based finger gesture recognition systems are crucial for many applications such as prosthesis control, sign language recognition, wearable rehabilitation system, and human–computer interaction. In this article, a motion-intent-based finger gesture recognition system is designed to correctly identify the tapping of every finger for the first time. Two auto-event annotation algorithms are firstly applied and evaluated for detecting the finger tapping frame. Based on the truncated signals, the Wavelet packet transform (WPT) coefficients are calculated and compressed as the features, followed by a feature selection method that is able to improve the performance by optimizing the feature set. Finally, three popular classifiers including naive Bayes (NBC), K-nearest neighbor (KNN), and support vector machine (SVM) are applied and evaluated. The recognition accuracy can be achieved up to 94%. The design and the architecture of the system are presented with full system characterization results.
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Affiliation(s)
- Huijun Ding
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Guangdong, China
| | - Qing He
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Guangdong, China
| | - Yongjin Zhou
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Guangdong, China
| | - Guo Dan
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Guangdong, China.,Center for Neurorehabilitation, Shenzhen Institute of Neuroscience, Guangdong, China
| | - Song Cui
- Institute of High Performance Computing, Singapore, Singapore
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Ding H, He Q, Zeng L, Zhou Y, Shen M, Dan G. Motion intent recognition of individual fingers based on mechanomyogram. Pattern Recognit Lett 2017. [DOI: 10.1016/j.patrec.2017.01.012] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Ahmadvand A, Daliri MR. Rotation invariant texture classification using extended wavelet channel combining and LL channel filter bank. Knowl Based Syst 2016. [DOI: 10.1016/j.knosys.2016.01.015] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Dong Y, Tao D, Li X. Nonnegative Multiresolution Representation-Based Texture Image Classification. ACM T INTEL SYST TEC 2015. [DOI: 10.1145/2738050] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Effective representation of image texture is important for an image-classification task. Statistical modelling in wavelet domains has been widely used to image texture representation. However, due to the intraclass complexity and interclass diversity of textures, it is hard to use a predefined probability distribution function to fit adaptively all wavelet subband coefficients of different textures. In this article, we propose a novel modelling approach, Heterogeneous and Incrementally Generated Histogram (HIGH), to indirectly model the wavelet coefficients by use of four local features in wavelet subbands. By concatenating all the HIGHs in all wavelet subbands of a texture, we can construct a nonnegative multiresolution vector (NMV) to represent a texture image. Considering the NMV’s high dimensionality and nonnegativity, we further propose a Hessian regularized discriminative nonnegative matrix factorization to compute a low-dimensional basis of the linear subspace of NMVs. Finally, we present a texture classification approach by projecting NMVs on the low-dimensional basis. Experimental results show that our proposed texture classification method outperforms seven representative approaches.
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Affiliation(s)
- Yongsheng Dong
- Chinese Academy of Sciences, Xi'an, Shaanxi, P. R. China
| | - Dacheng Tao
- Chinese Academy of Sciences, Xi'an, Shaanxi, P. R. China
| | - Xuelong Li
- Chinese Academy of Sciences, Xi'an, Shaanxi, P. R. China
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Riaz F, Ribeiro MD, Pimentel-Nunes P, Coimbra MT. Integral scale histogram local binary patterns for classification of narrow-band gastroenterology images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2013:3714-7. [PMID: 24110537 DOI: 10.1109/embc.2013.6610350] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The introduction of various novel imaging technologies such as narrow-band imaging have posed novel image processing challenges to the design of computer assisted decision systems. In this paper, we propose an image descriptor referred to as integrated scale histogram local binary patterns. We propagate an aggregated histogram of local binary patterns of an image at various resolutions. This results in low dimensional feature vectors for the images while incorporating their multiresolution analysis. The descriptor was used to classify gastroenterology images into four distinct groups. Results produced by the proposed descriptor exhibit around 92% accuracy for classification of gastroenteroloy images outperforming other state-of-the-art methods, endorsing the effectiveness of the proposed descriptor.
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Dong Y, Tao D, Li X, Ma J, Pu J. Texture classification and retrieval using shearlets and linear regression. IEEE TRANSACTIONS ON CYBERNETICS 2015; 45:358-369. [PMID: 25029547 DOI: 10.1109/tcyb.2014.2326059] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Statistical modeling of wavelet subbands has frequently been used for image recognition and retrieval. However, traditional wavelets are unsuitable for use with images containing distributed discontinuities, such as edges. Shearlets are a newly developed extension of wavelets that are better suited to image characterization. Here, we propose novel texture classification and retrieval methods that model adjacent shearlet subband dependences using linear regression. For texture classification, we use two energy features to represent each shearlet subband in order to overcome the limitation that subband coefficients are complex numbers. Linear regression is used to model the features of adjacent subbands; the regression residuals are then used to define the distance from a test texture to a texture class. Texture retrieval consists of two processes: the first is based on statistics in contourlet domains, while the second is performed using a pseudo-feedback mechanism based on linear regression modeling of shearlet subband dependences. Comprehensive validation experiments performed on five large texture datasets reveal that the proposed classification and retrieval methods outperform the current state-of-the-art.
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Kim NC, So HJ. Comments on “SVD-based modeling for image texture classification using wavelet transform”. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2013; 22:5408. [PMID: 23974628 DOI: 10.1109/tip.2013.2279319] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
This correspondence points out that in [1] there are some errors in two important formulas and the performance of the proposed singular value decomposition (SVD) feature is severely overestimated. It also presents a modified SVD feature which competes with the Gabor feature.
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Dong Y, Ma J. Feature extraction through contourlet subband clustering for texture classification. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2011.12.059] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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13
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Riaz F, Silva FB, Ribeiro MD, Coimbra MT. Invariant Gabor texture descriptors for classification of gastroenterology images. IEEE Trans Biomed Eng 2012; 59:2893-904. [PMID: 22893374 DOI: 10.1109/tbme.2012.2212440] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Automatic classification of lesions for gastroenterology imaging scenarios poses novel challenges to computer-assisted decision systems, which are mostly attributed to the dynamics of the image acquisition conditions. Such challenges demand that automatic systems are able to give robust characterizations of tissues irrespective of camera rotation, zoom, and illumination gradients when viewing the inner surface of the gastrointestinal tract. In this paper, we study the invariance properties of Gabor filters and propose a novel descriptor, the autocorrelation Gabor features (AGF). We show that our proposed AGF is invariant to scale, rotation, and illumination changes in the images. We integrate these new features in a texton framework (Texton-AGF) to classify images from two complementary gastroenterology imaging scenarios (chromoendoscopy and narrow-band imaging) broadly into three different groups: normal, precancerous, and cancerous. Results show that they compare favorably to using state-of-the-art texture descriptors for both imaging modalities.
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Affiliation(s)
- Farhan Riaz
- Instituto de Telecomunicações, Department of Computer Science, Faculdade de Ciłncias da Universidade do Porto, Porto, Portugal.
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Dong Y, Ma J. Bayesian texture classification based on contourlet transform and BYY harmony learning of Poisson mixtures. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2012; 21:909-918. [PMID: 21947521 DOI: 10.1109/tip.2011.2168231] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
As a newly developed 2-D extension of the wavelet transform using multiscale and directional filter banks, the contourlet transform can effectively capture the intrinsic geometric structures and smooth contours of a texture image that are the dominant features for texture classification. In this paper, we propose a novel Bayesian texture classifier based on the adaptive model-selection learning of Poisson mixtures on the contourlet features of texture images. The adaptive model-selection learning of Poisson mixtures is carried out by the recently established adaptive gradient Bayesian Ying-Yang harmony learning algorithm for Poisson mixtures. It is demonstrated by the experiments that our proposed Bayesian classifier significantly improves the texture classification accuracy in comparison with several current state-of-the-art texture classification approaches.
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Affiliation(s)
- Yongsheng Dong
- Department of Information Science, School of Mathematical Sciences, Peking University, Beijing, China
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Díaz-Pernas F, Antón-Rodríguez M, Perozo-Rondón F, González-Ortega D. A multi-scale supervised orientational invariant neural architecture for natural texture classification. Neurocomputing 2011. [DOI: 10.1016/j.neucom.2011.06.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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16
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Identifying Potentially Cancerous Tissues in Chromoendoscopy Images. PATTERN RECOGNITION AND IMAGE ANALYSIS 2011. [DOI: 10.1007/978-3-642-21257-4_88] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Coudevylle N, Montaville P, Leonov A, Zweckstetter M, Becker S. Structural determinants for Ca2+ and phosphatidylinositol 4,5-bisphosphate binding by the C2A domain of rabphilin-3A. J Biol Chem 2008; 283:35918-28. [PMID: 18945677 DOI: 10.1074/jbc.m804094200] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Rabphilin-3A is a neuronal C2 domain tandem containing protein involved in vesicle trafficking. Both its C2 domains (C2A and C2B) are able to bind phosphatidylinositol 4,5-bisphosphate, a key player in the neurotransmitter release process. The rabphilin-3A C2A domain has previously been shown to bind inositol-1,4,5-trisphosphate (IP3; phosphatidylinositol 4,5-bisphosphate headgroup) in a Ca2+-dependent manner with a relatively high affinity (50 microm) in the presence of saturating concentrations of Ca2+. Moreover, IP3 and Ca2+ binding to the C2A domain mutually enhance each other. Here we present the Ca2+-bound solution structure of the C2A domain. Structural comparison with the previously published Ca2+-free crystal structure revealed that Ca2+ binding induces a conformational change of Ca2+ binding loop 3 (CBL3). Our IP3 binding studies as well as our IP3-C2A docking model show the active involvement of CBL3 in IP3 binding, suggesting that the conformational change on CBL3 upon Ca2+ binding enables the interaction with IP3 and vice versa, in line with a target-activated messenger affinity mechanism. Our data provide detailed structural insight into the functional properties of the rabphilin-3A C2A domain and reveal for the first time the structural determinants of a target-activated messenger affinity mechanism.
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Affiliation(s)
- Nicolas Coudevylle
- Department of NMR-based Structural Biology, Max-Planck-Institute for Biophysical Chemistry, Am Fassberg 11, 37077 Göttingen, Germany
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Zhang M, Shen J, Shang Z. Color Image Fuzzy Classification Algorithm with Salient Regions. ACTA ACUST UNITED AC 2008. [DOI: 10.3923/itj.2008.560.569] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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