| 1. |
- Sbarra, AN, et al.
(författare)
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Mapping routine measles vaccination in low- and middle-income countries
- 2021
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 589:7842, s. 415-
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Tidskriftsartikel (refereegranskat)abstract
- The safe, highly effective measles vaccine has been recommended globally since 1974, yet in 2017 there were more than 17 million cases of measles and 83,400 deaths in children under 5 years old, and more than 99% of both occurred in low- and middle-income countries (LMICs)1–4. Globally comparable, annual, local estimates of routine first-dose measles-containing vaccine (MCV1) coverage are critical for understanding geographically precise immunity patterns, progress towards the targets of the Global Vaccine Action Plan (GVAP), and high-risk areas amid disruptions to vaccination programmes caused by coronavirus disease 2019 (COVID-19)5–8. Here we generated annual estimates of routine childhood MCV1 coverage at 5 × 5-km2pixel and second administrative levels from 2000 to 2019 in 101 LMICs, quantified geographical inequality and assessed vaccination status by geographical remoteness. After widespread MCV1 gains from 2000 to 2010, coverage regressed in more than half of the districts between 2010 and 2019, leaving many LMICs far from the GVAP goal of 80% coverage in all districts by 2019. MCV1 coverage was lower in rural than in urban locations, although a larger proportion of unvaccinated children overall lived in urban locations; strategies to provide essential vaccination services should address both geographical contexts. These results provide a tool for decision-makers to strengthen routine MCV1 immunization programmes and provide equitable disease protection for all children.
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| 2. |
- Alipoor, Mohammad, 1983
(författare)
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A novel biomarker discovery method on protemic data for ovarian cancer classification
- 2010
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Ingår i: 18th Iranian Conference on Electrical Engineering (ICEE), 2010. - 9781424467600
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Konferensbidrag (refereegranskat)abstract
- In this paper a novel combinational feature selection method on high throughput SELDI-TOF mass-spectroscopy data for ovarian cancer classification is developed. The proposed method includes 3 steps: dataset normalization, dimensionality reduction using feature filtering, selecting the most informative features utilizing binary particle swarm optimization. Indeed, the method employs a combination of filter and wrapper feature selection methods to find features with high discriminatory power. The algorithm is successfully validated using a well-known ovarian cancer proteomic dataset. Results of applying the method are superior to state of the art methods in proteomic pattern recognition. It reduces extremely high dimensionality of proteomic data to 3 dimensional and linearly separable data. Therefore, proposed system clearly outperforms previous works in both respects of accuracy and number of required features; witch may lead in high accuracy and high speed diagnosis procedure.
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| 3. |
- Alipoor, Mohammad, 1983, et al.
(författare)
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A Novel Framework for repeated measurements in diffusion tensor imaging
- 2016
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Ingår i: 3rd (ACM) Int'l Conf. on Biomedical and Bioinformatics Engineering (ICBBE 2016). - New York, NY, USA : ACM. - 9781450348249 ; Part F125793, s. 1-6
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Konferensbidrag (refereegranskat)abstract
- In the context of diffusion tensor imaging (DTI), the utility of making repeated measurements in each diffusion sensitizing direction has been the subject of numerous stud-ies. One can estimate the true signal value using either the raw complex-valued data or the real-valued magnitudesignal. While conventional methods focus on the former strategy, this paper proposes a new framework for acquiring/processing repeated measurements based on the latter strategy. The aim is to enhance the DTI processing pipeline by adding a diffusion signal estimator (DSE). This permits us to exploit the knowledge of the noise distribution to estimate the true signal value in each direction. An extensive study of the proposed framework, including theoretical analysis, experiments with synthetic data, performance evaluation and comparisons is presented.Our results show that the precision of estimated diffusionparameters is dependent on the number of available samplesand the manner in which the DSE accounts for noise. Theproposed framework improves the precision in estimationof diffusion parameters given a sufficient number of uniquemeasurements. This encourages future work with rich realdatasets and downstream applications.
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| 4. |
- Alipoor, Mohammad, 1983
(författare)
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A novel high performance iris and pupil localization method
- 2010
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Ingår i: 6th Iranian Conf. on Machine Vision and Image Processing (MVIP), 2010.
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Konferensbidrag (refereegranskat)abstract
- Iris detection is a computationally intensive task in the overall iris biometric processing. In this paper we proposed a technique to localize the iris and the pupil in eye images efficiently and accurately. This paper includes three stages: The first stage is related to finding the centre and radius of pupil. In this stage, the problem of pupil non-uniformity which may appear in some images is solved and the pupil is detected. In the second stage a new approach, based on circular arc search, is proposed to extract iris boundary. The last stage includes wavelet-based feature extraction and classifier design. Our approach has been applied on the CASIA standard database. High accuracy of the proposed iris localization method resulted in a high performance iris recognition system.
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| 5. |
- Alipoor, Mohammad, 1983
(författare)
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A novel logarithmic edge detection algorithm
- 2010
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Ingår i: 6th Iranian Conf. on Machine Vision and Image Processing (MVIP), 2010.
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Konferensbidrag (refereegranskat)abstract
- In this paper a novel logarithmic edge detection algorithm is presented. The algorithm is an extended and modified version of PLIP Sobel edge detection algorithm. Six new kernels are suggested to achieve a higher level of independence from scene illumination and provide obvious distinction between edge and non-edge pixels. We present experimental results for this method, and compare results of the algorithm against several leading edge detection methods such as Sobel, Canny and conventional logarithmic edge detection. To compare results objectively, we computed edginess judging index (EJI) for edge detection algorithms. The proposed technique is effective, as demonstrated by computer simulations, conceptually straight forward, and easy to use.
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| 6. |
- Alipoor, Mohammad, 1983
(författare)
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A novel method for mass spectrometry data representation and analysis
- 2010
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Ingår i: 17th Iranian Conference of Biomedical Engineering (ICBME), 2010. - 9781424474837
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Konferensbidrag (refereegranskat)abstract
- In this paper a novel representation/analysis method on high throughput SELDI-TOF mass-spectroscopy data is developed. To avoid complexity of conventional methods, mass spectrum is converted to an intensity image and then image processing techniques is implemented to solve the cancer classification problem. The proposed system benefits a thoroughly novel and efficient idea to design an image-based pattern recognition system for cancer classification. The system is successfully validated using a well-known ovarian cancer proteomic dataset. Results of applying the method are comparable to state of the art methods in proteomic pattern recognition.
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| 7. |
- Alipoor, Mohammad, 1983
(författare)
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Computational Diffusion MRI: Optimal Gradient Encoding Schemes
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Diffusion-weighted magnetic resonance imaging (dMRI) is a non-invasivestructural imaging technique that provides information about tissue microstructures.Quantitative measures derived from dMRI reflect pathologicaland developmental changes in living tissues such as human brain. Such parametersare increasingly used in diagnostic and prognostic procedures andthis has motivated several studies to investigate their estimation accuracyand precision. The precision of an estimated parameter is dependent on theapplied gradient encoding scheme (GES). An optimal GES is one that minimizesthe variance of the estimated parameter(s). This thesis focuses onoptimal GES design for the following dMRI models: second and fourth-orderdiffusion tensor imaging (DTI), ADC imaging and diffusion kurtosis imaging(DKI). A unified framework is developed that comprises three steps. Inthe first step, the original problem is formulated as an optimal experimentdesign problem. The optimal experiment design is the one that minimizesthe condition number (K-optimal) or the determinant (D-optimal) of thecovariance matrix of the estimated parameters. This yields a non-convexoptimization problem. In the second step, the problem is re-formulated as asemi-definite programming (SDP) problem by introducing new decision variablesand convex relaxation. In the final step, the SDP problem is solvedand the original decision variables are recovered. The proposed framework iscomprehensive; it can be applied to DTI, DKI, K-optimal design, D-optimaldesign, single-shell and multi-shell acquisitions and to optimizing directionsand b-values.The main contributions of this thesis include: (i) proof that by uniformlydistributing gradient encoding directions one obtains a D-optimal designboth for DKI and DTI; (ii) proof that the traditionally used icosahedral GESis D-optimal for DTI; (iii) proof that there exist rotation-invariant GESs thatare not uniformly distributed; and (iv) proof that there exist GESs that areD-optimal for DTI and DKI simultaneously. A simple algorithm is presntedthat can compute uniformly distributed GESs. In contrast to previousmethods, the proposed solution is strictly rotation-invariant. The practicalimpact/utility of the proposed method is demonstrated using Monte Carlosimulations. The results show that the precision of parameters estimatedusing the proposed approach can be as much as 25% better than that estimatedby state-of-the-art methods. Validation of these findings using realdata and extension to non-linear estimators/diffusion models provide scopefor future work.
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| 8. |
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| 9. |
- Alipoor, Mohammad, 1983
(författare)
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Designing edge detection filters using Particle Swarm Optimization
- 2010
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Ingår i: 18th Iranian Conference on Electrical Engineering (ICEE), 2010.
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Konferensbidrag (refereegranskat)abstract
- This paper presents a novel edge detection method based on Particle Swarm Optimization. Unlike classical filters that are set by intuitive knowledge, a new filter is proposed on the basis of evolutionary computation. A proper synthetic training image and its edge map are used to find an optimum edge filter. The advantage of this method is that an effective edge detection filter can be easily constructed. Provided results certify that our proposed method outperforms commonly used edge detection algorithms.
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| 10. |
- Alipoor, Mohammad, 1983, et al.
(författare)
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Determinant of the information matrix: a new rotation invariant optimality metric to design gradient encoding schemes
- 2015
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Ingår i: 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, United States, 16-19 April 2015. - 1945-8452. - 9781479923748 ; , s. 462-465
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Konferensbidrag (refereegranskat)abstract
- Minimum condition number (CN) gradient encoding schemewas introduced to diffusion MRI community more than adecade ago. It’s computation requires tedious numerical optimization which usually leads to sub-optimal solutions. TheCN does not reflect any benefits in acquiring more measurements, i.e. it’s optimal value is constant for any numberof measurements. Further, it is variable under rotation. Inthis paper we (i) propose an accurate method to computeminimum condition number scheme; and (ii) introduce determinant of the information matrix (DIM) as a new optimality metric that scales with number of measurements anddoes reflect what one would gain from acquiring more measurements. Theoretical analysis shows that DIM is rotationinvariant. Evaluations on state-of-the-art encoding schemesproves the relevance and superiority of the proposed metriccompared to condition number.
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