| 1. |
- Chodorowski, Artur, 1959, et al.
(författare)
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Histology-based oral lesion discrimination
- 2009
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Ingår i: Medicinteknikdagarna 2009 - Book of Abstracts, Sept 28-29, 2009, Västerås, Sweden.
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Konferensbidrag (refereegranskat)
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| 2. |
- Persson, Mikael, 1959, et al.
(författare)
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Advances in Neuro Diagnostic based on Microwave Technology, Transcranial Magnetic Stimulation and EEG source localization
- 2011
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Ingår i: Asia Pacific Microwave Conference, (APMC 2011;Melbourne, VIC; 5 - 8 December 2011). - 9780858259744 ; , s. 469-472
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Konferensbidrag (refereegranskat)abstract
- Advances in neuro diagnostics based on microwave antenna system in terms of a helmet including a set of broad band patch antennas is presented. It is shown that classification algorithms can be used to detect internal bleeding in stroke patients. Transcranial magnetic stimulation has traditionally been used for brain mapping and treatment of depression. In this paper we discuss the use of the method for neuro diagnostics with the help of integrated image guidance. Surgical therapy has become an important therapeutic alternative for some patients with medically intractable epilepsy. Electroencephalography and the associated model based diagnostics as a non-invasive diagnostic tool is also discussed.
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| 3. |
- Qaiser, Mahmood, 1981, et al.
(författare)
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A Comparative Study of Automated Segmentation Methods for Use in a Microwave Tomography System for Imaging Intracerebral Hemorrhage in Stroke Patients
- 2015
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Ingår i: Journal of Electromagnetic Analysis and Applications. - : Scientific Research Publishing, Inc.. - 1942-0749 .- 1942-0730. ; 7, s. 152-167
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Tidskriftsartikel (refereegranskat)abstract
- Microwave technology offers the possibility for pre-hospital stroke detection as we have pre- viously demonstrated using non-imaging diagnostics. The focus in this paper is on image-based diagnostics wherein the technical and computational complexities of image reconstruction are a challenge for clinical realization. Herein we investigate whether information about a patient’s brain anatomy obtained prior to a stroke event can be used to facilitate image-based stroke diag- nostics. A priori information can be obtained by segmenting the patient’s head tissues from mag- netic resonance images. Expert manual segmentation is presently the gold standard, but it is labo- rious and subjective. A fully automatic method is thus desirable. This paper presents an evaluation of several such methods using both synthetic magnetic resonance imaging (MRI) data and real da- ta from four healthy subjects. The segmentation was performed on the full 3D MRI data, whereas the electromagnetic evaluation was performed using a 2D slice. The methods were evaluated in terms of: i) tissue classification accuracy over all tissues with respect to ground truth, ii) the accu- racy of the simulated electromagnetic wave propagation through the head, and iii) the accuracy of the image reconstruction of the hemorrhage. The segmentation accuracy was measured in terms of the degree of overlap (Dice score) with the ground truth. The electromagnetic simulation accu- racy was measured in terms of signal deviation relative to the simulation based on the ground truth. Finally, the image reconstruction accuracy was measured in terms of the Dice score, relative error of dielectric properties, and visual comparison between the true and reconstructed intrace- rebral hemorrhage. The results show that accurate segmentation of tissues (Dice score = 0.97) from the MRI data can lead to accurate image reconstruction (relative error = 0.24) for the intra- cerebral hemorrhage in the subject’s brain. They also suggest that accurate automated segmenta- tion can be used as a surrogate for manual segmentation and can facilitate the rapid diagnosis of intracerebral hemorrhage in stroke patients using a microwave imaging system.
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| 4. |
- Qaiser, Mahmood, 1981, et al.
(författare)
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A fully automatic unsupervised segmentation framework for the brain tissues in MR images
- 2014
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Ingår i: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. - : SPIE. - 1605-7422. - 9780819498311 ; 9038
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Konferensbidrag (refereegranskat)abstract
- This paper presents a novel fully automatic unsupervised framework for the segmentation of brain tissues in magnetic resonance (MR) images. The framework is a combination of our proposed Bayesian-based adaptive mean shift (BAMS), a priori spatial tissue probability maps and fuzzy c-means. BAMS is applied to cluster the tissues in the joint spatialintensity feature space and then a fuzzy c-means algorithm is employed with initialization by a priori spatial tissue probability maps to assign the clusters into three tissue types; white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The proposed framework is validated on multimodal synthetic as well as on real T1-weighted MR data with varying noise characteristics and spatial intensity inhomogeneity. The performance of the proposed framework is evaluated relative to our previous method BAMS and other existing adaptive mean shift framework. Both of these are based on the mode pruning and voxel weighted k-means algorithm for classifying the clusters into WM, GM and CSF tissue. The experimental results demonstrate the robustness of the proposed framework to noise and spatial intensity inhomogeneity, and that it exhibits a higher degree of segmentation accuracy in segmenting both synthetic and real MR data compared to competing methods.
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| 5. |
- Qaiser, Mahmood, 1981, et al.
(författare)
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A novel Bayesian approach to adaptive mean shift segmentation of brain images
- 2012
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Ingår i: Proceedings - IEEE Symposium on Computer-Based Medical Systems. - 1063-7125. - 9781467320511
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Konferensbidrag (refereegranskat)abstract
- We present a novel adaptive mean shift (AMS) algorithm for the segmentation of tissues in magnetic resonance (MR) brain images. In particular we introduce a novel Bayesian approach for the estimation of the adaptive kernel bandwidth and investigate its impact on segmentation accuracy. We studied the three class problem where the brain tissues are segmented into white matter, gray matter and cerebrospinal fluid. The segmentation experiments were performed on both multi-modal simulated and real patient T1-weighted MR volumes with different noise characteristics and spatial inhomogeneities. The performance of the algorithm was evaluated relative to several competing methods using real and synthetic data. Our results demonstrate the efficacy of the proposed algorithm and that it can outperform competing methods, especially when the noise and spatial intensity inhomogeneities are high.
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| 6. |
- Qaiser, Mahmood, 1981, et al.
(författare)
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Automated MRI brain tissue segmentation based on mean shift and fuzzy c-means using a priori tissue probability maps
- 2015
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Ingår i: IRBM. - : Elsevier BV. - 1959-0318. ; 36:3, s. 185-196
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a novel fully automated unsupervised framework for the brain tissue segmentation in magnetic resonance (MR) images. The framework is a combination of Bayesian-based adaptive mean shift, a priori spatial tissue probability maps and fuzzy c-means. Mean shift is employed to cluster the tissues in the joint spatial-intensity feature space and then a fuzzy c-means is applied with initialization by a priori spatial tissue probability maps to assign the clusters into three tissue types; white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The proposed framework is validated on a synthetic T1-weighted MR image with varying noise characteristics and spatial intensity inhomogeneity, obtained from the BrainWeb database as well as on 38 real T1-weighted MR images, obtained from the IBSR repository. The performance of the proposed framework is evaluated relative to the three widely used brain segmentation toolboxes: FAST, SPM and PVC, and the adaptive mean shift (AMS) and classical fuzzy c-means methods. The experimental results demonstrate the robustness of the proposed framework, and that it exhibits a higher degree of segmentation accuracy in segmenting both synthetic and real T1-weighted MR images compared to all competing methods.
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| 7. |
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| 8. |
- Qaiser, Mahmood, 1981, et al.
(författare)
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On the Fully Automatic Construction of a Realistic Head Model for EEG Source Localization
- 2013
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Ingår i: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. Osaka, Japan, 3-7 July 2013. - 1557-170X. - 9781457702167 ; , s. 3331-3334
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Konferensbidrag (refereegranskat)abstract
- Accurate multi-tissue segmentation of magnetic resonance (MR) images is an essential first step in the construction of a realistic finite element head conductivity model (FEHCM) for electroencephalography (EEG) source localization. All of the segmentation approaches proposed to date for this purpose require manual intervention or correction and are thus laborious, time-consuming, and subjective. In this paper we propose and evaluate a fully automatic method based on a hierarchical segmentation approach (HSA) incorporating Bayesian-based adaptive mean-shift segmentation (BAMS). An evaluation of HSA-BAMS, as well as two reference methods, in terms of both segmentation accuracy and the source localization accuracy of the resulting FEHCM is also presented. The evaluation was performed using (i) synthetic 2D multi-modal MRI head data and synthetic EEG (generated for a prescribed source), and (ii) real 3D T1-weighted MRI head data and real EEG data (with expert determined source localization). Expert manual segmentation served as segmentation ground truth. The results show that HSA-BAMS outperforms the two reference methods and that it can be used as a surrogate for manual segmentation for the construction of a realistic FEHCM for EEG source localization.
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| 9. |
- Qaiser, Mahmood, 1981, et al.
(författare)
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Unsupervised Segmentation of Head Tissues from Multi-modal MR Images for EEG Source Localization
- 2015
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Ingår i: Journal of Digital Imaging. - : Springer Science and Business Media LLC. - 1618-727X .- 0897-1889. ; 28:4, s. 499-514
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we present and evaluate an automatic unsupervised segmentation method, hierarchical segmenta- tion approach (HSA)–Bayesian-based adaptive mean shift (BAMS), for use in the construction of a patient-specific head conductivity model for electroencephalography (EEG) source localization. It is based on a HSA and BAMS for segmenting the tissues from multi-modal magnetic resonance (MR) head images. The evaluation of the proposed method was done both directly in terms of segmentation accuracy and indirectly in terms of source localization accuracy. The direct evaluation was performed relative to a commonly used reference method brain extraction tool (BET)–FMRIB’s automated segmenta- tion tool (FAST) and four variants of the HSA using both synthetic data and real data from ten subjects. The synthetic data includes multiple realizations of four different noise levels and several realizations of typical noise with a 20 % bias field level. The Dice index and Hausdorff distance were used to measure the segmentation accuracy. The indirect evaluation was performed relative to the reference method BET-FAST using synthetic two-dimensional (2D) multimodal magnetic resonance (MR) data with 3 % noise and synthetic EEG (generated for a prescribed source). The source localiza- tion accuracy was determined in terms of localization error and relative error of potential. The experimental results dem- onstrate the efficacy of HSA-BAMS, its robustness to noise and the bias field, and that it provides better segmentation accuracy than the reference method and variants of the HSA. They also show that it leads to a more accurate localization accuracy than the commonly used reference method and sug- gest that it has potential as a surrogate for expert manual segmentation for the EEG source localization problem.
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| 10. |
- Shirvany, Yazdan, 1980, et al.
(författare)
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Non-invasive EEG source localization using particle swarm optimization: A clinical experiment
- 2012
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Ingår i: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. - 1557-170X. - 9781424441198 ; , s. 6232-6235
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Konferensbidrag (refereegranskat)abstract
- One of the most important steps of pre-surgical diagnosis in patients with medically intractable epilepsy is to find the precise location of the epileptogenic foci. An Electroencephalography (EEG) is a non-invasive standard tool used at epilepsy surgery center for pre-surgical diagnosis. In this paper a modified particle swarm optimization (MPSO) method is applied to a real EEG data, i.e., a somatosensory evoked potentials (SEPs) measured from a healthy subject, to solve the EEG source localization problem. A high resolution 1 mm hexahedra finite element volume conductor model of the subject's head was generated using T1-weighted magnetic resonance imaging data. An exhaustive search pattern and the MPSO method were then applied to the peak of the averaged SEPs data. The non-invasive EEG source analysis methods localized the somatosensory cortex area where our clinical expert expected the received SEPs. The proposed inverse problem solver found the global minima with acceptable accuracy and reasonable number of iterations.
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