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Automated acute lymphoblastic leukaemia detection system using microscopic images

IET IMAGE PROCESSING 2019年第13期13卷 2548-2553页

作者： Sukhia, Komal Nain Ghafoor, Abdul Riaz, Muhammad Mohsin Iltaf, Naima NUST Islamabad Pakistan COMSATS Islamabad CAST Islamabad Pakistan

An automatic and novel approach for acute lymphoblastic leukaemia classification is proposed. The proposed scheme is based on pre-processing and segmentation of white blood cell nuclei using expectation maximisation algorithm, feature extraction, feature selection using principal component analysis and classification using sparse representation. The accuracy of the proposed scheme significantly outperforms the existing schemes in terms of acute lymphoblastic leukaemia classification.

关键词： cellular biophysics principal component analysis expectation-maximisation algorithm feature extraction medical image processing image segmentation image classification blood cancer microscopic images automatic approach acute lymphoblastic leukaemia classification white blood cell nuclei expectation maximisation algorithm feature extraction principal component analysis automated acute lymphoblastic leukaemia detection system sparse representation

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HS remote sensing image restoration using fusion with MS images by EM algorithm

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IET SIGNAL PROCESSING 2017年第1期11卷 95-103页

作者： Ansari, Amir Danyali, Habibollah Helfroush, Mohammad Sadegh Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

Remote sensing images are widely used for different areas from mineral exploration to agricultural applications and poor quality of hyperspectral (HS) images will directly have adverse effect on these applications. In this study, a method is proposed to restore degraded HS images. To achieve this aim, another multispectral (MS) observation of the same scene is supposed to be available and restoration is fulfilled by fusion of HS images and MS images. The proposed method gains maximum a posteriori estimation and is based on expectation maximisation algorithm. Deblurring and denoising are performed separately. Deblurring is done in spatial domain via non-overlapping blocks, whereas denoising is implemented in wavelet domain. To represent the coefficients in wavelet domain, instead of multinormal model, Gaussian scale mixture is exploited. The proposed method is validated on airborne visible/infrared imaging spectrometer (AVIRIS) and HS digital imagery collection experiment (HYDICE) databases and experimental results signify that the proposed method outperforms state-of-the-art techniques cited in the literature and signal-to-noise ratio is improved as much as 15.71dB for Moffett database and 16.26dB for HYDICE database.

关键词： remote sensing image restoration image fusion hyperspectral imaging expectation-maximisation algorithm image denoising wavelet transforms Gaussian processes mixture models geophysical image processing HS remote sensing image restoration MS images EM algorithm mineral exploration agricultural application hyperspectral image quality degraded HS image restoration multispectral observation HS image-MS image fusion maximum a posteriori estimation expectation maximisation algorithm deblurring denoising spatial domain nonoverlapping blocks wavelet domain multinormal model Gaussian scale mixture airborne visible-infrared imaging spectrometer HS digital imagery collection experiment HYDICE database signal-to-noise ratio Moffett database

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On theoretical justification of the forward-backward algorithm for the variational learning of Bayesian hidden Markov models

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IET SIGNAL PROCESSING 2022年第6期16卷 674-679页

作者： Li, Tao Ma, Jinwen Peking Univ Dept Informat & Computat Sci Sch Math Sci & LMAM Beijing 100871 Peoples R China

In the variational learning process of a Bayesian Hidden Markov model, the forward-backward algorithm is heuristically applied without theoretical justification. This is potentially problematic, because the original derivation of the forward-backward algorithm implicitly requires the parameters to be normalized, which does not hold in the variational learning process of Bayesian HMM. In this paper, we prove that such a requirement is not necessary for the forward-backward algorithm to obtain the correct result. We prove the result from two perspectives. The first proof straightforwardly verifies that implementing the forward-backward algorithm with the unnormalised parameters is equivalent to implementing it with the normalized parameters. The second proof provides a new derivation of the forward-backward algorithm without hidden Markov assumptions and probabilistic meanings of the parameters. As a result, we justify that applying the forward-backward algorithm is theoretically correct and reasonable in the variational learning of Bayesian hidden Markov models.

关键词： expectation maximisation algorithm hidden Markov model variational technique

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Soft shape registration under Lie group frame

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IET COMPUTER VISION 2013年第6期7卷 437-447页

作者： Peng, Yaxin Lin, Wei Ying, Shihui Peng, Jigen Shanghai Univ Sch Sci Dept Math Shanghai 200444 Peoples R China Xi An Jiao Tong Univ Sch Math & Stat Inst Informat & Syst Sci Xian 710049 Peoples R China

In this study, the authors address a two-dimensional (2D) shape registration problem on data with anisotropic-scale deformation and noise. First, the model is formulated under the iterative closest point (ICP) framework, which is one of the most popular methods for shape registration. To overcome the effect of noise, the expectation maximisation algorithm is used to improve the model. Then, the structure of Lie groups is adopted to parameterise the proposed model, which provides a unified framework to deal with the shape registration problems. Such representation makes it possible to introduce some suitable constraints to the model, which improves the robustness of the algorithm. Thereby, the 2D shape registration problem is turned to an optimisation problem on the matrix Lie group. Furthermore, a sequence of quadratic programming is designed to approximate the solution for the model. Finally, several comparative experiments are carried out to validate that the authors' algorithm performs well in terms of robustness, especially in the presence of outliers.

关键词： expectation-maximisation algorithm image registration Lie groups quadratic programming quadratic programming matrix Lie group optimisation problem expectation maximisation algorithm ICP framework iterative closest point noise anisotropic-scale deformation 2D shape registration problem Lie group frame soft shape registration

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Inference of haplotypic phase and missing genotypes in polyploid organisms and variable copy number genomic regions

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BMC BIOINFORMATICS 2008年第1期9卷 1-9页

作者： Su, Shu-Yi White, Jonathan Balding, David J. Coin, Lachlan J. M. Imperial Coll Sch Med Dept Epidemiol & Publ Hlth London W2 1PG England Natl Inst Agr Bot Cambridge CB3 OLE England

Background: The power of haplotype-based methods for association studies, identification of regions under selection, and ancestral inference, is well-established for diploid organisms. For polyploids, however, the difficulty of determining phase has limited such approaches. Polyploidy is common in plants and is also observed in animals. Partial polyploidy is sometimes observed in humans (e. g. trisomy 21;Down's syndrome), and it arises more frequently in some human tissues. Local changes in ploidy, known as copy number variations (CNV), arise throughout the genome. Here we present a method, implemented in the software polyHap, for the inference of haplotype phase and missing observations from polyploid genotypes. PolyHap allows each individual to have a different ploidy, but ploidy cannot vary over the genomic region analysed. It employs a hidden Markov model (HMM) and a sampling algorithm to infer haplotypes jointly in multiple individuals and to obtain a measure of uncertainty in its inferences. Results: In the simulation study, we combine real haplotype data to create artificial diploid, triploid, and tetraploid genotypes, and use these to demonstrate that polyHap performs well, in terms of both switch error rate in recovering phase and imputation error rate for missing genotypes. To our knowledge, there is no comparable software for phasing a large, densely genotyped region of chromosome from triploids and tetraploids, while for diploids we found polyHap to be more accurate than fastPhase. We also compare the results of polyHap to SATlotyper on an experimentally haplotyped tetraploid dataset of 12 SNPs, and show that polyHap is more accurate. Conclusion: With the availability of large SNP data in polyploids and CNV regions, we believe that polyHap, our proposed method for inferring haplotypic phase from genotype data, will be useful in enabling researchers analysing such data to exploit the power of haplotype-based analyses.

关键词： Hide Markov Model expectation maximisation algorithm Impute Genotype Copy Number Variation Region Haplotypic Phase

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HANKEL-EM-SVD: a hybrid data dropout estimation technique for high voltage partial discharge signals

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2019年第6期13卷 824-835页

作者： Govindarajan, Suganya Subbaiah, Jayalalitha Krithivasan, Kannan Natarajan, Muthukumar SASTRA Deemed Be Univ Sch EEE Thanjavur Tamil Nadu India SASTRA Deemed Be Univ Tata Realty Srinivasa Ramanujam Res Cell Data Sci Lab Thanjavur India

Partial discharge (PD) measurement and characterisation is the most effective method to assess the insulation conditions of equipment for its diagnosis. A significant challenge in this area is PD denoising. Despite recent advancements in denoising algorithms, data drops out because of inappropriate threshold selection during denoising. Processing PD signals with missing data leads to an inaccurate assessment of the insulation conditions, indicating recovering such missed data is a potential research scope under the moonlight. This study addresses the issues of recovering such data dropouts by considering the PD data vector matrix as a low-rank matrix that needs completion by Hankel-matrix decomposition methods. After finding the probabilistic estimate of the missing values using an expectation maximisation algorithm, singular-value decomposition is used to find the actual missing values by soft-thresholding the singular values of the matrix. After testing this technique on simulated PD data, it is implemented to test SASTRA-High-Voltage Laboratory data, showing root mean square error (RMSE) 0.77% and mean absolute error 0.84%. The efficiency of this technique is confirmed when tested with large-sized noise free PD data of 36 samples from the Laboratory of BOLOGNA University (with RMSE varying from 0.15 to 0.31% and mean absolute error varying from 0.29 to 0.6%).

关键词： mean square error methods expectation-maximisation algorithm signal denoising partial discharge measurement partial discharges singular value decomposition HANKEL-EM-SVD high voltage partial discharge signals insulation conditions PD denoising inappropriate threshold selection inaccurate assessment missed data potential research scope PD-data vector-matrix low-rank matrix Hankel-matrix decomposition methods probabilistic estimate expectation maximisation algorithm singular-value decomposition actual missing values singular values simulated PD data SASTRA-High-Voltage Laboratory data root mean square mean absolute error hybrid data dropout estimation technique partial discharge measurement partial discharge characterisation PD signal processing BOLOGNA University

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Robust external calibration of terrestrial laser scanner and digital camera for structural monitoring

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JOURNAL OF APPLIED GEODESY 2019年第2期13卷 105-134页

作者： Omidalizarandi, Mohammad Kargoll, Boris Paffenholz, Jens-Andre Neumann, Ingo Leibniz Univ Hannover Geodet Inst Nienburger Str 1 D-30167 Hannover Germany

In the last two decades, the integration of a terrestrial laser scanner (TLS) and digital photogrammetry, besides other sensors integration, has received considerable attention for deformation monitoring of natural or man-made structures. Typically, a TLS is used for an area-based deformation analysis. A high-resolution digital camera may be attached on top of the TLS to increase the accuracy and completeness of deformation analysis by optimally combining points or line features extracted both from three-dimensional (3D) point clouds and captured images at different epochs of time. For this purpose, the external calibration parameters between the TLS and digital camera needs to be determined precisely. The camera calibration and internal TLS calibration are commonly carried out in advance in the laboratory environments. The focus of this research is to highly accurately and robustly estimate the external calibration parameters between the fused sensors using signalised target points. The observables are the image measurements, the 3D point clouds, and the horizontal angle reading of a TLS. In addition, laser tracker observations are used for the purpose of validation. The functional models are determined based on the space resection in photogrammetry using the collinearity condition equations, the 3D Helmert transformation and the constraint equation, which are solved in a rigorous bundle adjustment procedure. Three different adjustment procedures are developed and implemented: (1) an expectation maximization (EM) algorithm to solve a Gauss-Helmert model (GHM) with grouped t-distributed random deviations, (2) a novel EM algorithm to solve a corresponding quasi-Gauss-Markov model (qGMM) with t-distributed pseudo-misclosures, and (3) a classical least-squares procedure to solve the GHM with variance components and outlier removal. The comparison of the results demonstrates the precise, reliable, accurate and robust estimation of the parameters in particular by the sec

关键词： Terrestrial laser scanner digital camera external calibration Gauss-Helmert model quasi-Gauss-Markov model adaptive robust estimation expectation maximisation algorithm structural monitoring

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Bayesian and non-Bayesian probabilistic models for medical image analysis

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IMAGE AND VISION COMPUTING 2003年第10期21卷 851-864页

作者： Bromiley, PA Thacker, NA Scott, MLJ Pokric, M Lacey, AJ Cootes, TF Univ Manchester Sch Med Imaging Sci & Biomed Engn Div Manchester M13 9PT Lancs England

Bayesian approaches to data analysis are popular in machine vision, and yet the main advantage of Bayes theory, the ability to incorporate prior knowledge in the form of the prior probabilities, may lead to problems in some quantitative tasks. In this paper we demonstrate examples of Bayesian and non-Bayesian techniques from the area of magnetic resonance image (MRI) analysis. Issues raised by these examples are used to illustrate difficulties in Bayesian methods and to motivate an approach based on frequentist methods. We believe this approach to be more suited to quantitative data analysis, and provide a general theory for the use of these methods in learning (Bayes risk) systems and for data fusion. Proofs are given for the more novel aspects of the theory. We conclude with a discussion of the strengths and weaknesses, and the fundamental suitability, of Bayesian and non-Bayesian approaches for MRI analysis in particular, and for machine vision systems in general. (C) 2003 Elsevier B.V. All rights reserved.

关键词： Bayesian and non-Bayesian probabilistic models magnetic resonance image analysis expectation maximisation algorithm

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Image segmentation by a new weighted Student's t-mixture model

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IET IMAGE PROCESSING 2013年第3期7卷 240-251页

作者： Zhang, Hui Wu, Qing Ming Jonathan Thanh Minh Nguyen Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Jiangsu Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Nanjing Jiangsu Peoples R China

In this study, the authors introduce a new weighted Student's t-mixture model (WSMM) for image segmentation. Gaussian distribution and Student's t-distribution are the two commonly used probabilities in the finite mixture model (FMM). The Student's t-mixture model has come to be regarded as an alternative to Gaussian mixture models, as it is heavily tailed and more robust for outliers. Moreover, the pixels are considered independent of each other in the FMM. Although some existing methods incorporate the spatial relationship between neighbouring pixels, they do not consider the relationship between spatial information and clustering information, thus those reported methods remain sensitive to noise. The advantages of the authors method are as follows: first, the authors introduce WSMM to incorporate the local spatial information, pixel intensity value and clustering information in an image. Second, the authors model is simple, easy to implement and has a good balance between noise insensitiveness and image detail preservation. Third, they adopt the gradient method and expectation maximisation algorithm, which allow for simultaneous estimation of optimal parameters. Finally, the most useful statistical tool for image segmentation, the well-known hidden Markov random field model, is a special case of their model. Thus, their method is general enough for model-based techniques construction. Experimental results on synthetic and real images demonstrate the improved robustness and effectiveness of their approach.

关键词： expectation-maximisation algorithm Gaussian distribution gradient methods hidden Markov models image segmentation pattern clustering image segmentation weighted student t-mixture model WSMM Gaussian distribution student t-distribution finite mixture model FMM Gaussian mixture models neighbouring pixels clustering information local spatial information pixel intensity value noise insensitiveness image detail preservation gradient method expectation maximisation algorithm statistical tool hidden Markov random field model model-based techniques construction synthetic images real images expectation-maximisation algorithm Gaussian distribution gradient methods hidden Markov models image segmentation pattern clustering image segmentation weighted student t-mixture model WSMM Gaussian distribution student t-distribution finite mixture model FMM Gaussian mixture models neighbouring pixels clustering information local spatial information pixel intensity value noise insensitiveness image detail preservation gradient method expectation maximisation algorithm statistical tool hidden Markov random field model model-based techniques construction synthetic images real images

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Bayesian and non-Bayesian probabilistic models for medical image analysis

Bayesian and non-Bayesian probabilistic models for medical i...

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Symposium on Probabilistic Models in Computer Vision

作者： Bromiley, PA Thacker, NA Scott, MLJ Pokric, M Lacey, AJ Cootes, TF Univ Manchester Sch Med Imaging Sci & Biomed Engn Div Manchester M13 9PT Lancs England

关键词： Bayesian and non-Bayesian probabilistic models magnetic resonance image analysis expectation maximisation algorithm

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