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Cheriyan MM, Michael PA, Kumar A. Blind source separation with mixture models - A hybrid approach to MR brain classification. Magn Reson Imaging 2018; 54:137-147. [PMID: 30172941 DOI: 10.1016/j.mri.2018.08.023] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Revised: 08/29/2018] [Accepted: 08/30/2018] [Indexed: 11/28/2022]
Abstract
The development of automated segmentation approaches, which do not suffer from excessive computational burden and intra- and inter-observer variability, is the holy grail of multispectral MR image classification. A new segmentation approach to the data set of MR brain images using a combination of Independent Component Analysis (ICA) with a generalized version of the popular Gaussian Mixture Model (GMM) for unsupervised classification is proposed to be superior to conventional methods in this paper. We propose to optimize the parameters of the mixture model using a meta-heuristic approach like the Particle Swarm Optimization (PSO) to escape the problem of local traps (maxima or minima). Experiments were carried out initially on a synthetic MR Brainweb image set as proof of concept and subsequently on 152 sets of clinical MR images with T1w, T2w and FLAIR sequences. The major advantage of the proposed algorithm is the increased accuracy of lesion classification - average of 94.79% (±1.7) against 85.85% (±3.1) without ICA. As a result of the incorporation of ICA, the inherent computational overhead was also lowered as evidenced by faster convergence. Comparative studies using quantitative and qualitative analysis against conventional algorithms establish the superiority of the proposed approach.
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Affiliation(s)
| | | | - Anil Kumar
- Amrita Institute of Medical Sciences, Kochi, Kerala, India
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Du WB, Zhou XL, Jusup M, Wang Z. Physics of transportation: Towards optimal capacity using the multilayer network framework. Sci Rep 2016; 6:19059. [PMID: 26791580 PMCID: PMC4726168 DOI: 10.1038/srep19059] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2015] [Accepted: 12/02/2015] [Indexed: 11/09/2022] Open
Abstract
Because of the critical role of transportation in modern times, one of the most successful application areas of statistical physics of complex networks is the study of traffic dynamics. However, the vast majority of works treat transportation networks as an isolated system, which is inconsistent with the fact that many complex networks are interrelated in a nontrivial way. To mimic a realistic scenario, we use the framework of multilayer networks to construct a two-layered traffic model, whereby the upper layer provides higher transport speed than the lower layer. Moreover, passengers are guided to travel along the path of minimal travelling time and with the additional cost they can transfer from one layer to another to avoid congestion and/or reach the final destination faster. By means of numerical simulations, we show that a degree distribution-based strategy, although facilitating the cooperation between both layers, can be further improved by enhancing the critical generating rate of passengers using a particle swarm optimisation (PSO) algorithm. If initialised with the prior knowledge from the degree distribution-based strategy, the PSO algorithm converges considerably faster. Our work exemplifies how statistical physics of complex networks can positively affect daily life.
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Affiliation(s)
- Wen-Bo Du
- School of Electronic and Information Engineering, Beihang University, Beijing 100191, P.R.China
| | - Xing-Lian Zhou
- School of Electronic and Information Engineering, Beihang University, Beijing 100191, P.R.China
| | - Marko Jusup
- Faculty of Sciences, Kyushu University, Fukuoka 819-0395, Japan
| | - Zhen Wang
- Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Fukuoka 816-8580, Japan
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Chen Z, Zhang J, Du WB, Lordan O, Tang J. Optimal Allocation of Node Capacity in Cascade-Robustness Networks. PLoS One 2015; 10:e0141360. [PMID: 26496705 PMCID: PMC4619834 DOI: 10.1371/journal.pone.0141360] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Accepted: 10/07/2015] [Indexed: 11/18/2022] Open
Abstract
The robustness of large scale critical infrastructures, which can be modeled as complex networks, is of great significance. One of the most important means to enhance robustness is to optimize the allocation of resources. Traditional allocation of resources is mainly based on the topology information, which is neither realistic nor systematic. In this paper, we try to build a framework for searching for the most favorable pattern of node capacity allocation to reduce the vulnerability to cascading failures at a low cost. A nonlinear and multi-objective optimization model is proposed and tackled using a particle swarm optimization algorithm (PSO). It is found that the network becomes more robust and economical when less capacity is left on the heavily loaded nodes and the optimized network performs better resisting noise. Our work is helpful in designing a robust economical network.
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Affiliation(s)
- Zhen Chen
- School of Electronic and Information Engineering, Beihang University, Beijing 100191, People’s Republic of China
- Beijing Key Laboratory for Network-based Cooperative Air Traffic Management, Beijing 100191, People’s Republic of China
| | - Jun Zhang
- School of Electronic and Information Engineering, Beihang University, Beijing 100191, People’s Republic of China
- Beijing Key Laboratory for Network-based Cooperative Air Traffic Management, Beijing 100191, People’s Republic of China
| | - Wen-Bo Du
- School of Electronic and Information Engineering, Beihang University, Beijing 100191, People’s Republic of China
- Beijing Key Laboratory for Network-based Cooperative Air Traffic Management, Beijing 100191, People’s Republic of China
- School of Engineering & IT, University of New South Wales at the Australian Defence Force Academy, Canberra, Australia
| | - Oriol Lordan
- Universitat Politècnica de Catalunya-BarcelonaTech, C/Colom no. 11, Terrassa 08222, Spain
| | - Jiangjun Tang
- School of Engineering & IT, University of New South Wales at the Australian Defence Force Academy, Canberra, Australia
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He W, Juette A, Denton ERE, Oliver A, Martí R, Zwiggelaar R. A Review on Automatic Mammographic Density and Parenchymal Segmentation. Int J Breast Cancer 2015; 2015:276217. [PMID: 26171249 PMCID: PMC4481086 DOI: 10.1155/2015/276217] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2015] [Revised: 04/21/2015] [Accepted: 05/17/2015] [Indexed: 01/03/2023] Open
Abstract
Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models.
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Affiliation(s)
- Wenda He
- Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK
| | - Arne Juette
- Department of Radiology, Norfolk & Norwich University Hospital, Norwich NR4 7UY, UK
| | - Erika R. E. Denton
- Department of Radiology, Norfolk & Norwich University Hospital, Norwich NR4 7UY, UK
| | - Arnau Oliver
- Department of Architecture and Computer Technology, University of Girona, 17071 Girona, Spain
| | - Robert Martí
- Department of Architecture and Computer Technology, University of Girona, 17071 Girona, Spain
| | - Reyer Zwiggelaar
- Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK
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Liu C, Du WB, Wang WX. Particle swarm optimization with scale-free interactions. PLoS One 2014; 9:e97822. [PMID: 24859007 PMCID: PMC4032252 DOI: 10.1371/journal.pone.0097822] [Citation(s) in RCA: 118] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Accepted: 04/24/2014] [Indexed: 12/01/2022] Open
Abstract
The particle swarm optimization (PSO) algorithm, in which individuals collaborate with their interacted neighbors like bird flocking to search for the optima, has been successfully applied in a wide range of fields pertaining to searching and convergence. Here we employ the scale-free network to represent the inter-individual interactions in the population, named SF-PSO. In contrast to the traditional PSO with fully-connected topology or regular topology, the scale-free topology used in SF-PSO incorporates the diversity of individuals in searching and information dissemination ability, leading to a quite different optimization process. Systematic results with respect to several standard test functions demonstrate that SF-PSO gives rise to a better balance between the convergence speed and the optimum quality, accounting for its much better performance than that of the traditional PSO algorithms. We further explore the dynamical searching process microscopically, finding that the cooperation of hub nodes and non-hub nodes play a crucial role in optimizing the convergence process. Our work may have implications in computational intelligence and complex networks.
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Affiliation(s)
- Chen Liu
- School of Electronic and Information Engineering, Beihang University, Beijing, People's Republic of China
| | - Wen-Bo Du
- School of Electronic and Information Engineering, Beihang University, Beijing, People's Republic of China
- * E-mail:
| | - Wen-Xu Wang
- School of Management, Beijing Normal University, Beijing, People's Republic of China
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Oliver A, Freixenet J, Martí R, Pont J, Pérez E, Denton ERE, Zwiggelaar R. A novel breast tissue density classification methodology. ACTA ACUST UNITED AC 2008; 12:55-65. [PMID: 18270037 DOI: 10.1109/titb.2007.903514] [Citation(s) in RCA: 176] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large kappa = 0.81 and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment.
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Affiliation(s)
- A Oliver
- Institute of Informatics and Applications, Unversity of Girona, 17071 Girona, Spain.
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Abstract
Particle swarm optimisation (PSO) has been enormously successful. Within little more
than a decade hundreds of papers have reported successful applications of PSO. In fact, there
are so many of them, that it is difficult for PSO practitioners and researchers to have a clear
up-to-date vision of what has been done in the area of PSO applications. This brief paper
attempts to fill this gap, by categorising a large number of publications dealing with PSO
applications stored in the IEEE Xplore database at the time of writing.
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