Brain Image & Lesions Registration
and 3D Reconstruction in DICOM
MRI Images
C.P. Loizou1, C. Papacharalambous1, G. Samaras1, E.
Kyriakou2, T. Kasparis1, M. Patziaris3, E. Eracleous3,
C.S. Pattichis4
1Cyprus
University of Technology, Department of Electrical, Computer Engineering
and Informatics
2Frederick University, Department of Computer Science, Limassol, Cyprus
3Ayios Therissos Medical Diagnostic Centre, Nicosia, Cyprus
4University of Cyprus, Department of Computer Science
Outline
1. Introduction-Multiple sclerosis (MS), 3D Registration &
Reconstruction
2. Objective
3. Materials & Methods
1.
2.
3.
4.
5.
6.
Study group, Phantom and MRI Image Acquisition
MRI image preprocessing and filtering
Lesion Segmentation
Registration & Reconstruction
3D Volume Estimation
Evaluation Metrics & Statistical Analysis
4. Results
5. Discussion, Concluding Remarks, Future Directions
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1 Introduction-MS, 3D
Registration, Reconstruction
• MS: Refers to scars
particularly in the white matter
of the brain with or without
neurological symptoms. [1]-[4]
• 3D Imaging systems follow up
the development of the
disease [1]-[4]
[1] C.P. Loizou et al., Quantitative texture analysis…, J Neuroarad, 2015; [2] N. Chumchob, et al., ‘A robust affine image..”, Int. J. Numr.
Anal. & Model., 2009; [3] C. Kumar, et al., “3D Reconstruction of brain tumor from 2D MRI’s … ”, Int. J. Advanced Research Electron. &
Commun. Engin., 2014; [4] M. Arakeri, et al., “An effective and efficient approach to 3D… ”, Int. J. Signal Proces. & Image Proces., 2013.
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2 Objective-Motivation
Propose/evaluate a brain MRI registration and
3D reconstruction system for the 3D
reconstruction of DICOM brain images and
lesions from MS subjects. Validation on
calibrated 3D MRI models and real DICOM
MRI brain images and MS lesions.
Motivation:
Prevention is better than cure
Individuals at risk can be identified
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Flow diagram analysis of the proposed registration and 3D
reconstruction method in DICOM brain MRI images.
[1] C.P. Loizou et al., Quantitative texture analysis…, J Neuroarad, 2015
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3.1 Materials & Methods-Study group &
Dicom MRI Image Acquisition
• ACR MRI Phantom (17x17x10 mm
rectangle grid, cylinders and other
points of interest)
• T2-weigted MRI DICOM using the
Phillips scanner
• Acquisitions at two different time
points (T0 and T1)
American College of Radiology
(ACR) MRI Phantom [1]
56th slice of the ACR
MRI phantom
Phillips Achieva MRI
scanner 3.0 T
[1] American College of Radiology. Phantom Test Guidance, 2005,
http://www.acr.org/~/media/ACR/Documents/Accreditation/MRI/LargePhantomGuidance.pdf
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3.2 Materials & Methods-MRI image
preprocessing, filtering, lesion
segmentation
• Image histogram equalisation prior
reconstruction [2]
• Wiener filtering [3]
• Manual segmentation of lesions by an MS
neurologist and confirmed by another radiologist
[2] C. P. Loizou, S. Petroudi, I. Seimenis, M. Pantziaris, C.S. Pattichis, “Quantitative texture analysis of brain white matter
lesions derived from T2-weighted MR images in MS patients with clinically isolated syndrome,” J. Neuroradiol., vol. 42, no. 2,
pp. 99-114, 2015. [3] M. Martin-Fernandez, et al., “Sequential anisotropic Wiener filtering applied to 3D MRI data,” Magn.
Res. Imag., vol. 25, no. 2, pp. 278-292, 2007.
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3.3 Materials & Methods-MRI
Registration, 3D Reconstruction and
volume estimation
• Time points T0 and T1 were registered using a
non-rigid image registration method [8]
• Generate a 3D volume using iso-surface
rendering [9], [10]
• MS lesions were also registered and
reconstructed similarly
• Volume estimation [9]
[8] J. P. Thirion, “Image matching as a diffusion process: … ,” Medic. Image Anal., vol. 2, no. 3, pp. 243–260, 1998.
[9] R. M. Sherekar, A. Pawar, “A MATLAB image processing approach …..”, Americ. J. Mechan. Engin. and Autom., vol. 1,
no. 5, pp. 48-53, 2014. [10] C. Koniaris, et al., “Survey of Texture mapping techniques ….”, J. Computer Graphics Techniques,
vol. 3, no. 2, pp. 18-60, 2014.
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3.4 Materials & Methods-Evaluation
Metrics
Following evaluation metrics were used to assess the
proposed registration and 3D reconstruction methods [1]:
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True positive rate (TPR),
True-negative rate (TNR),
False positive rate (FPR),
False negative rate (FNR),
Accuracy (ACC%),
Mean square error (MSE),
Correlation coefficient (ρ),
CXentroid (C),
Perimeter (P) and
volume (V).
[1] C.P. Loizou et al., Quantitative texture analysis…, J Neuroarad, 2015
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4.1 Results – Phantom and
MRI Registration
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4.2 Results – Statistical
Evaluation 1
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4.3 Results – Statistical
Evaluation 2
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5. Discussion
• No other studies found in the literature for 3D MRI
brain registration & reconstruction on DICOM images
• 3D Slices and 3D lesions accurately reconstructed and
inserted to the brain volume
• Integrated software and may be used to detect, extract
and follow-up MS lesion in MS subjects in longitudinal
studies,
• Analyzing the evolution of MS and their impact on
surrounding structures.
• Used
for
treatment
planning,
therapeutically
monitoring, surgery and pathological brain modelling.
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5.1 Conclusion and future
directions
The evaluation showed that the proposed
method may also be reliably used in the
registration and 3D reconstruction of brain MRI
images. However, further work in a larger
number of images, as well multiple observers’
evaluation is needed for further validating the
proposed method for the clinical practice.
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Thank You
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