- •Biological and Medical Physics, Biomedical Engineering
- •Medical Image Processing
- •Preface
- •Contents
- •Contributors
- •1.1 Medical Image Processing
- •1.2 Techniques
- •1.3 Applications
- •1.4 The Contribution of This Book
- •References
- •2.1 Introduction
- •2.2 MATLAB and DIPimage
- •2.2.1 The Basics
- •2.2.2 Interactive Examination of an Image
- •2.2.3 Filtering and Measuring
- •2.2.4 Scripting
- •2.3 Cervical Cancer and the Pap Smear
- •2.4 An Interactive, Partial History of Automated Cervical Cytology
- •2.5 The Future of Automated Cytology
- •2.6 Conclusions
- •References
- •3.1 The Need for Seed-Driven Segmentation
- •3.1.1 Image Analysis and Computer Vision
- •3.1.2 Objects Are Semantically Consistent
- •3.1.3 A Separation of Powers
- •3.1.4 Desirable Properties of Seeded Segmentation Methods
- •3.2 A Review of Segmentation Techniques
- •3.2.1 Pixel Selection
- •3.2.2 Contour Tracking
- •3.2.3 Statistical Methods
- •3.2.4 Continuous Optimization Methods
- •3.2.4.1 Active Contours
- •3.2.4.2 Level Sets
- •3.2.4.3 Geodesic Active Contours
- •3.2.5 Graph-Based Methods
- •3.2.5.1 Graph Cuts
- •3.2.5.2 Random Walkers
- •3.2.5.3 Watershed
- •3.2.6 Generic Models for Segmentation
- •3.2.6.1 Continuous Models
- •3.2.6.2 Hierarchical Models
- •3.2.6.3 Combinations
- •3.3 A Unifying Framework for Discrete Seeded Segmentation
- •3.3.1 Discrete Optimization
- •3.3.2 A Unifying Framework
- •3.3.3 Power Watershed
- •3.4 Globally Optimum Continuous Segmentation Methods
- •3.4.1 Dealing with Noise and Artifacts
- •3.4.2 Globally Optimal Geodesic Active Contour
- •3.4.3 Maximal Continuous Flows and Total Variation
- •3.5 Comparison and Discussion
- •3.6 Conclusion and Future Work
- •References
- •4.1 Introduction
- •4.2 Deformable Models
- •4.2.1 Point-Based Snake
- •4.2.1.1 User Constraint Energy
- •4.2.1.2 Snake Optimization Method
- •4.2.2 Parametric Deformable Models
- •4.2.3 Geometric Deformable Models (Active Contours)
- •4.2.3.1 Curve Evolution
- •4.2.3.2 Level Set Concept
- •4.2.3.3 Geodesic Active Contour
- •4.2.3.4 Chan–Vese Deformable Model
- •4.3 Comparison of Deformable Models
- •4.4 Applications
- •4.4.1 Bone Surface Extraction from Ultrasound
- •4.4.2 Spinal Cord Segmentation
- •4.4.2.1 Spinal Cord Measurements
- •4.4.2.2 Segmentation Using Geodesic Active Contour
- •4.5 Conclusion
- •References
- •5.1 Introduction
- •5.2 Imaging Body Fat
- •5.3 Image Artifacts and Their Impact on Segmentation
- •5.3.1 Partial Volume Effect
- •5.3.2 Intensity Inhomogeneities
- •5.4 Overview of Segmentation Techniques Used to Isolate Fat
- •5.4.1 Thresholding
- •5.4.2 Selecting the Optimum Threshold
- •5.4.3 Gaussian Mixture Model
- •5.4.4 Region Growing
- •5.4.5 Adaptive Thresholding
- •5.4.6 Segmentation Using Overlapping Mosaics
- •5.6 Conclusions
- •References
- •6.1 Introduction
- •6.2 Clinical Context
- •6.3 Vessel Segmentation
- •6.3.1 Survey of Vessel Segmentation Methods
- •6.3.1.1 General Overview
- •6.3.1.2 Region-Growing Methods
- •6.3.1.3 Differential Analysis
- •6.3.1.4 Model-Based Filtering
- •6.3.1.5 Deformable Models
- •6.3.1.6 Statistical Approaches
- •6.3.1.7 Path Finding
- •6.3.1.8 Tracking Methods
- •6.3.1.9 Mathematical Morphology Methods
- •6.3.1.10 Hybrid Methods
- •6.4 Vessel Modeling
- •6.4.1 Motivation
- •6.4.1.1 Context
- •6.4.1.2 Usefulness
- •6.4.2 Deterministic Atlases
- •6.4.2.1 Pioneering Works
- •6.4.2.2 Graph-Based and Geometric Atlases
- •6.4.3 Statistical Atlases
- •6.4.3.1 Anatomical Variability Handling
- •6.4.3.2 Recent Works
- •References
- •7.1 Introduction
- •7.2 Linear Structure Detection Methods
- •7.3.1 CCM for Imaging Diabetic Peripheral Neuropathy
- •7.3.2 CCM Image Characteristics and Noise Artifacts
- •7.4.1 Foreground and Background Adaptive Models
- •7.4.2 Local Orientation and Parameter Estimation
- •7.4.3 Separation of Nerve Fiber and Background Responses
- •7.4.4 Postprocessing the Enhanced-Contrast Image
- •7.5 Quantitative Analysis and Evaluation of Linear Structure Detection Methods
- •7.5.1 Methodology of Evaluation
- •7.5.2 Database and Experiment Setup
- •7.5.3 Nerve Fiber Detection Comparison Results
- •7.5.4 Evaluation of Clinical Utility
- •7.6 Conclusion
- •References
- •8.1 Introduction
- •8.2 Methods
- •8.2.1 Linear Feature Detection by MDNMS
- •8.2.2 Check Intensities Within 1D Window
- •8.2.3 Finding Features Next to Each Other
- •8.2.4 Gap Linking for Linear Features
- •8.2.5 Quantifying Branching Structures
- •8.3 Linear Feature Detection on GPUs
- •8.3.1 Overview of GPUs and Execution Models
- •8.3.2 Linear Feature Detection Performance Analysis
- •8.3.3 Parallel MDNMS on GPUs
- •8.3.5 Results for GPU Linear Feature Detection
- •8.4.1 Architecture and Implementation
- •8.4.2 HCA-Vision Features
- •8.4.3 Linear Feature Detection and Analysis Results
- •8.5 Selected Applications
- •8.5.1 Neurite Tracing for Drug Discovery and Functional Genomics
- •8.5.2 Using Linear Features to Quantify Astrocyte Morphology
- •8.5.3 Separating Adjacent Bacteria Under Phase Contrast Microscopy
- •8.6 Perspectives and Conclusions
- •References
- •9.1 Introduction
- •9.2 Bone Imaging Modalities
- •9.2.1 X-Ray Projection Imaging
- •9.2.2 Computed Tomography
- •9.2.3 Magnetic Resonance Imaging
- •9.2.4 Ultrasound Imaging
- •9.3 Quantifying the Microarchitecture of Trabecular Bone
- •9.3.1 Bone Morphometric Quantities
- •9.3.2 Texture Analysis
- •9.3.3 Frequency-Domain Methods
- •9.3.4 Use of Fractal Dimension Estimators for Texture Analysis
- •9.3.4.1 Frequency-Domain Estimation of the Fractal Dimension
- •9.3.4.2 Lacunarity
- •9.3.4.3 Lacunarity Parameters
- •9.3.5 Computer Modeling of Biomechanical Properties
- •9.4 Trends in Imaging of Bone
- •References
- •10.1 Introduction
- •10.1.1 Adolescent Idiopathic Scoliosis
- •10.2 Imaging Modalities Used for Spinal Deformity Assessment
- •10.2.1 Current Clinical Practice: The Cobb Angle
- •10.2.2 An Alternative: The Ferguson Angle
- •10.3 Image Processing Methods
- •10.3.1 Previous Studies
- •10.3.2 Discrete and Continuum Functions for Spinal Curvature
- •10.3.3 Tortuosity
- •10.4 Assessment of Image Processing Methods
- •10.4.1 Patient Dataset and Image Processing
- •10.4.2 Results and Discussion
- •10.5 Summary
- •References
- •11.1 Introduction
- •11.2 Retinal Imaging
- •11.2.1 Features of a Retinal Image
- •11.2.2 The Reason for Automated Retinal Analysis
- •11.2.3 Acquisition of Retinal Images
- •11.3 Preprocessing of Retinal Images
- •11.4 Lesion Based Detection
- •11.4.1 Matched Filtering for Blood Vessel Segmentation
- •11.4.2 Morphological Operators in Retinal Imaging
- •11.5 Global Analysis of Retinal Vessel Patterns
- •11.6 Conclusion
- •References
- •12.1 Introduction
- •12.1.1 The Progression of Diabetic Retinopathy
- •12.2 Automated Detection of Diabetic Retinopathy
- •12.2.1 Automated Detection of Microaneurysms
- •12.3 Image Databases
- •12.4 Tortuosity
- •12.4.1 Tortuosity Metrics
- •12.5 Tracing Retinal Vessels
- •12.5.1 NeuronJ
- •12.5.2 Other Software Packages
- •12.6 Experimental Results and Discussion
- •12.7 Summary and Future Work
- •References
- •13.1 Introduction
- •13.2 Volumetric Image Visualization Methods
- •13.2.1 Multiplanar Reformation (2D slicing)
- •13.2.2 Surface-Based Rendering
- •13.2.3 Volumetric Rendering
- •13.3 Volume Rendering Principles
- •13.3.1 Optical Models
- •13.3.2 Color and Opacity Mapping
- •13.3.2.2 Transfer Function
- •13.3.3 Composition
- •13.3.4 Volume Illumination and Illustration
- •13.4 Software-Based Raycasting
- •13.4.1 Applications and Improvements
- •13.5 Splatting Algorithms
- •13.5.1 Performance Analysis
- •13.5.2 Applications and Improvements
- •13.6 Shell Rendering
- •13.6.1 Application and Improvements
- •13.7 Texture Mapping
- •13.7.1 Performance Analysis
- •13.7.2 Applications
- •13.7.3 Improvements
- •13.7.3.1 Shading Inclusion
- •13.7.3.2 Empty Space Skipping
- •13.8 Discussion and Outlook
- •References
- •14.1 Introduction
- •14.1.1 Magnetic Resonance Imaging
- •14.1.2 Compressed Sensing
- •14.1.3 The Role of Prior Knowledge
- •14.2 Sparsity in MRI Images
- •14.2.1 Characteristics of MR Images (Prior Knowledge)
- •14.2.2 Choice of Transform
- •14.2.3 Use of Data Ordering
- •14.3 Theory of Compressed Sensing
- •14.3.1 Data Acquisition
- •14.3.2 Signal Recovery
- •14.4 Progress in Sparse Sampling for MRI
- •14.4.1 Review of Results from the Literature
- •14.4.2 Results from Our Work
- •14.4.2.1 PECS
- •14.4.2.2 SENSECS
- •14.4.2.3 PECS Applied to CE-MRA
- •14.5 Prospects for Future Developments
- •References
- •15.1 Introduction
- •15.2 Acquisition of DT Images
- •15.2.1 Fundamentals of DTI
- •15.2.2 The Pulsed Field Gradient Spin Echo (PFGSE) Method
- •15.2.3 Diffusion Imaging Sequences
- •15.2.4 Example: Anisotropic Diffusion of Water in the Eye Lens
- •15.2.5 Data Acquisition
- •15.3 Digital Processing of DT Images
- •15.3.2 Diagonalization of the DT
- •15.3.3 Gradient Calibration Factors
- •15.3.4 Sorting Bias
- •15.3.5 Fractional Anisotropy
- •15.3.6 Other Anisotropy Metrics
- •15.4 Applications of DTI to Articular Cartilage
- •15.4.1 Bovine AC
- •15.4.2 Human AC
- •References
- •Index
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Under these circumstances, the utility of the K metric is questionable. Since it computes tortuosity differently, by emphasizing contributions of high curvature, it is not directly comparable to any of the other methods and seems to have limited applicability to scoliotic angles.
10.5 Summary
Current clinical approaches to spinal deformity assessment and treatment are based on manual (printed film or computer screen) measurement of plane radiographs, along with limited use of other modalities such as CT/MRI or back shape analysis. The Cobb angle is currently the standard clinical metric for assessing the severity of a scoliotic curve. It reduces the 3D curvature to a single angle, measured at the upper and lower vertebral endplates of the curve. The Cobb angle is a key parameter used in surgical decision-making, yet measurement variability studies have demonstrated that it is a relatively ‘noisy’ measure (Sect. 10.2.1). The alternative, the Ferguson angle, includes lateral deviation at the apex of the deformity but the geometric centres of the vertebrae are difficult to establish from a plane radiograph (Sect. 10.2.2), especially when the vertebrae are wedge-shaped [32].
Given these uncertainties in manual measurement and the increasing availability of digitized medical images, there are emerging opportunities for the development of medical image processing techniques to assess spinal deformities. Both discrete and continuum representations of spinal curvature on a vertebral level-by-level basis offer the potential for better reproducibility and sensitivity so that the progression of disease can be followed using automated or semi-automated selection of anatomical landmarks such as the vertebral canal landmark detection approach demonstrated here. Image processing approaches also offer the potential to develop new metrics which use data from all of the vertebrae in a scoliotic curve rather than only two or three manually selected vertebrae.
One practical issue around the development of new spinal deformity assessment techniques is how they compare with existing clinical measures, and for this reason we included a comparison of several new metrics (Cobb equivalent 1, Cobb equivalent 2 and tortuosity metrics) with manual Cobb measurements for a group of AIS patients. This comparison showed that a single manual Cobb measurement by a single observer is subject to significant measurement variability, which results in scatter when comparing manual and Cobb-equivalent measures (Fig. 10.12). However, when a group of manual measurements of the same image are averaged, there is much closer agreement between manual Cobb and Cobbequivalent metrics (Fig. 10.14). Further, the Cobb-equivalent 1, Cobb-equivalent 2 and coronal tortuosity metrics are all closely correlated. These initial results show that continuum and discrete representations of entire thoracolumbar spinal curves can be interrogated to yield simple clinical measures which agree closely with current manual measurements, but more work is required to extend the comparison to 3D (sagittal and axial planes), and to other clinical measures than the Cobb angle.
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The image processing metrics which we presented here were based on semiautomated landmark detection in the vertebral canal, which is a high-contrast landmark on transverse CT slices; however, semi-automated detection of the anterior vertebral column would be a valuable direction for future study, as the anterior column in scoliosis tends to be more deformed than the posterior region.
We note again that although CT is not current clinical practice for scoliosis assessment (except in the case of keyhole surgery planning), advances in CT scanner technology have dramatically reduced radiation dose compared to earlier scanners [6], and CT or biplanar radiography (with their associated advantages of 3D reconstruction with good bony resolution) may become more common. One issue with CT is the relatively large difference in deformity magnitude between supine and standing postures (which in itself is a potentially valuable indicator of spine flexibility). A move toward 3D imaging modalities is likely considering the increasing realisation of the need to consider scoliosis as a 3D deformity [60].
There is much potential for future development of image processing algorithms based on 3D imaging modalities for improved assessment and treatment of spinal deformities. New metrics can assist in surgical planning by highlighting 3D aspects of the deformity, by feeding into biomechanical analysis tools (such as finite element simulations of scoliosis [61], and by interfacing with existing classification systems [39, 62, 63] to provide automated classification.
References
1.Nash, C.L. Jr., Gregg, E.C., Brown, R.H., et al.: Risks of exposure to X-rays in patients undergoing long-term treatment for scoliosis. J. Bone Joint Surg Am. 61, 371–374 (1979)
2.Levy, A.R., Goldberg, M.S., Mayo, N.E., et al.: Reducing the lifetime risk of cancer from spinal radiographs among people with adolescent idiopathic scoliosis. Spine (Phila. Pa. 1976) 21, 1540–1547 (1996); discussion 1548
3.Kamimura, M., Kinoshita, T., Itoh, H., et al.: Preoperative CT examination for accurate and safe anterior spinal instrumentation surgery with endoscopic approach. J. Spinal Disord. Tech. 15, 47–51 (2002); discussion 51–42
4.Abul-Kasim, K., Overgaard, A., Maly, P., et al.: Low-dose helical computed tomography (CT) in the perioperative workup of adolescent idiopathic scoliosis. Eur. Radiol. 19, 610–618 (2009)
5.Aaro, S., Dahlborn, M.: Estimation of vertebral rotation and the spinal and rib cage deformity in scoliosis by computer tomography. Spine 6, 460–467 (1981)
6.Ho, E.K., Upadhyay, S.S., Ferris, L., et al.: A comparative study of computed tomographic and plain radiographic methods to measure vertebral rotation in adolescent idiopathic scoliosis. Spine 17, 771–774 (1992)
7.Krismer, M., Sterzinger, W., Haid, C., et al.: Axial rotation measurement of scoliotic vertebrae by means of computed tomography scans. Spine 21, 576–581 (1996)
8.Krismer, M., Chen, A.M., Steinlechner, M., et al.: Measurement of vertebral rotation: a comparison of two methods based on CT scans. J. Spinal Disord. 12, 126–130 (1999)
9.Gocen, S., Havitcioglu, H., Alici, E.: A new method to measure vertebral rotation from CT scans. Eur. Spine J. 8, 261–265 (1999)
10.Adam, C.J., Askin, G.N.: Automatic measurement of vertebral rotation in idiopathic scoliosis. Spine (Phila. Pa. 1976) 31, E80–E83 (2006)
246 |
C. Adam and G. Dougherty |
11.Perie, D., Sales de Gauzy, J., Curnier, D., et al.: Intervertebral disc modeling using a MRI method: migration of the nucleus zone within scoliotic intervertebral discs. Magn. Reson. Imag. 19, 1245–1248 (2001)
12.Perie, D., Curnier, D., de Gauzy, J.S.: Correlation between nucleus zone migration within scoliotic intervertebral discs and mechanical properties distribution within scoliotic vertebrae. Magn. Reson. Imag. 21, 949–953 (2003)
13.Violas, P., Estivalezes, E., Briot, J., et al.: Objective quantification of intervertebral disc volume properties using MRI in idiopathic scoliosis surgery. Magn. Reson. Imag. 25, 386–391 (2007)
14.Wessberg, P., Danielson, B.I., Willen, J.: Comparison of Cobb angles in idiopathic scoliosis on standing radiographs and supine axially loaded MRI. Spine (Phila. Pa. 1976) 31, 3039–3044 (2006)
15.Adam, C., Izatt, M., Askin, G.: Design and evaluation of an MRI compatible axial compression device for 3D assessment of spinal deformity and flexibility in AIS. Stud. Health Technol. Inform. 158, 38–43 (2010)
16.Willner, S.: Moir´e topography for the diagnosis and documentation of scoliosis. Acta Orthop. Scand. 50, 295–302 (1979)
17.Stokes, I.A., Moreland, M.S.: Measurement of the shape of the surface of the back in patients with scoliosis. The standing and forward-bending positions. J. Bone Joint Surg. Am. 69, 203–211 (1987)
18.Turner-Smith, A.R., Harris, J.D., Houghton, G.R., et al.: A method for analysis of back shape in scoliosis. J. Biomech. 21, 497–509 (1988)
19.Weisz, I., Jefferson, R.J., Turner-Smith, A.R., et al.: ISIS scanning: a useful assessment technique in the management of scoliosis. Spine (Phila. Pa. 1976) 13, 405–408 (1988)
20.Theologis, T.N., Fairbank, J.C., Turner-Smith, A.R., et al.: Early detection of progression in adolescent idiopathic scoliosis by measurement of changes in back shape with the integrated shape imaging system scanner. Spine 22, 1223–1227 (1997); discussion 1228
21.Hackenberg, L., Hierholzer, E., Potzl, W., et al.: Rasterstereographic back shape analysis in idiopathic scoliosis after posterior correction and fusion. Clin. Biomech. (Bristol, Avon) 18, 883–889 (2003)
22.Berryman, F., Pynsent, P., Fairbank, J., et al.: A new system for measuring three-dimensional back shape in scoliosis. Eur. Spine. J. 17, 663–672 (2008)
23.Shannon, T.M.: Development of an apparatus to evaluate Adolescent Idiopathic Scoliosis by dynamic surface topography. Stud. Health Technol. Inform. 140, 121–127 (2008)
24.Zubovic, A., Davies, N., Berryman, F., et al.: New method of scoliosis deformity assessment: ISIS2 System. Stud. Health Technol. Inform. 140, 157–160 (2008)
25.Drerup, B., Ellger, B., Meyer zu Bentrup, F.M., et al.: Functional raster stereographic images: A new method for biomechanical analysis of skeletal geometry. Orthopade 30, 242–250 (2001)
26.Wong, H.K., Balasubramaniam, P., Rajan, U., et al.: Direct spinal curvature digitization in scoliosis screening – a comparative study with Moir´e contourgraphy. J. Spinal Disord. 10, 185–192 (1997)
27.Cobb, J.R.: Outline for the study of scoliosis. American Academy of Orthopedic Surgeons Instructional Course Lectures (1948)
28.Genant, H.K., Wu, C.Y., van Kuijk, C., et al.: Vertebral fracture assessment using a semiquantitative technique. J. Bone Miner Res. 8, 1137–1148 (1993)
29.Polly, D.W., Jr., Kilkelly, F.X., McHale, K.A., et al.: Measurement of lumbar lordosis. Evaluation of intraobserver, interobserver, and technique variability. Spine (Phila. Pa. 1976) 21, 1530–1535 (1996); discussion 1535–1536
30.Adam, C.J., Izatt, M.T., Harvey, J.R., et al.: Variability in Cobb angle measurements using reformatted computerized tomography scans. Spine 30, 1664–1669 (2005)
31.Ferguson, A.B.: Roentgen diagnosis of the extremities and spine, pp. 414–415. Hoeber, New York (1949)
32.Diab, K.M., Sevastik, J.A., Hedlund, R., et al.: Accuracy and applicability of measurement of the scoliotic angle at the frontal plane by Cobb’s method, by Ferguson’s method and by a new method. Eur. Spine J. 4, 291–295 (1995)
10 Applications of Medical Image Processing in the Diagnosis and Treatment... |
247 |
33.Dougherty, G., Johnson, M.J.: Assessment of scoliosis by direct measurement of the curvature of the spine. Proc. SPIE 7260, 72603Q (2009). doi:10.1117/12.806655
34.Stokes, I.A., Aronson, D.D., Ronchetti, P.J., et al.: Reexamination of the Cobb and Ferguson angles: Bigger is not always better. J. Spinal Disord. 6, 333–338 (1993)
35.Gupta, M.C., Wijesekera, S., Sossan, A., et al.: Reliability of radiographic parameters in neuromuscular scoliosis. Spine 32, 691–695 (2007)
36.Chockalingam, N., Dangerfield, P.H., Giakas, G., et al.: Computer-assisted Cobb measurement of scoliosis. Eur. Spine J. 11, 353–357 (2002)
37.Cheung, J., Wever, D.J., Veldhuizen, A.G., et al.: The reliability of quantitative analysis on digital images of the scoliotic spine. Eur. Spine J. 11, 535–542 (2002)
38.Zhang, J., Lou, E., Hill, D.L., et al.: Computer-aided assessment of scoliosis on posteroanterior radiographs. Med. Biol. Eng. Comput. 48, 185–195 (2010)
39.Stokes, I.A., Aronsson, D.D.: Computer-assisted algorithms improve reliability of King classification and Cobb angle measurement of scoliosis. Spine 31, 665–670 (2006)
40.Gerard, O., Lelong, P., Planells-Rodriguez, M., et al.: Semi-automatic landmark detection in digital X-ray images of the spine. Stud. Health Technol. Inform. 88, 132–135 (2002)
41.Mitchell, H.L., Ang, K.S.: Non-rigid surface shape registration to monitor change in back surface topography. Stud. Health Technol. Inform. 158, 29–33 (2010)
42.Hart, W.E., Goldbaum, M., Cote, B., et al.: Measurement and classification of retinal vascular tortuosity. Int. J. Med. Inform. 53, 239–252 (1999)
43.Dougherty, G., Varro, J.: A quantitative index for the measurement of the tortuosity of blood vessels. Med. Eng. Phys. 22, 567–574 (2000)
44.Bullitt, E., Gerig, G., Pizer, S.M., et al.: Measuring tortuosity of the intracerebral vasculature from MRA images. IEEE Trans. Med. Imag. 22, 1163–1171 (2003)
45.Johnson, M.J., Dougherty, G.: Robust measures of three-dimensional vascular tortuosity based on the minimum curvature of approximating polynomial spline fits to the vessel mid-line. Med. Eng. Phys. 29, 677–690 (2007)
46.Dougherty, G., Johnson, M.J.: Clinical validation of three-dimensional tortuosity metrics based on the minimum curvature of approximating polynomial splines. Med. Eng. Phys. 30, 190–198 (2008)
47.Dougherty, G., Johnson, M.J., Wiers, M.D.: Measurement of retinal vascular tortuosity and its application to retinal pathologies. Med. Biol. Eng. Comput. 48, 87–95 (2010)
48.Capowski, J.J., Kylstra, J.A., Freedman, S.F.: A numeric index based on spatial frequency for the tortuosity of retinal vessels and its application to plus disease in retinopathy of prematurity. Retina 15, 490–500 (1995)
49.Grisan, E., Foracchia, M., Ruggeri, A.: A novel method for the automatic grading of retinal vessel tortuosity. IEEE Trans. Med. Imag. 27, 310–319 (2008)
50.Wallace, D.K.: Computer-assisted quantification of vascular tortuosity in retinopathy of prematurity. Trans. Am. Ophthalmol. Soc. 105, 594–615 (2007)
51.Benhamou, S.: How to reliably estimate the tortuosity of an animal’s path: straightness, sinuosity, or fractal dimension? J. Theor. Biol. 229, 209–220 (2004)
52.Smedby, O., Hogman, N., Nilsson, S., et al.: Two-dimensional tortuosity of the superficial femoral artery in early atherosclerosis. J. Vasc. Res. 30, 181–191 (1993)
53.Kimball, B.P., Bui, S., Dafopoulos, N.: Angiographic features associated with acute coronary artery occlusion during elective angioplasty. Can. J. Cardiol. 6, 327–332 (1990)
54.Brinkman, A.M., Baker, P.B., Newman, W.P., et al.: Variability of human coronary artery geometry: an angiographic study of the left anterior descending arteries of 30 autopsy hearts. Ann. Biomed. Eng. 22, 34–44 (1994)
55.Patasius, M., Marozas, V., Lukosevicius, A., et al.: Model based investigation of retinal vessel tortuosity as a function of blood pressure: Preliminary results. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2007, 6460–6463 (2007)
56.Kamimura, M., Kinoshita, T., Itoh, H., et al.: Preoperative CT examination for accurate and safe anterior spinal instrumentation surgery with endoscopic approach. J. Spinal Disord. Tech. 15, 47–51 (2002)
248 |
C. Adam and G. Dougherty |
57.Schick, D.: Computed tomography radiation doses for paediatric scoliosis scans. Internal report commissioned by QUT/Mater Health Services Paediatric Spine Research Group from Queensland Health Biomedical Technology Services (2004)
58.Torell, G., Nachemson, A., Haderspeck-Grib, K., et al.: Standing and supine Cobb measures in girls with idiopathic scoliosis. Spine 10, 425–427 (1985)
59.Yazici, M., Acaroglu, E.R., Alanay, A., et al.: Measurement of vertebral rotation in standing versus supine position in adolescent idiopathic scoliosis. J. Pediatr. Orthop. 21, 252–256 (2001)
60.Krawczynski, A., Kotwicki, T., Szulc, A., et al.: Clinical and radiological assessment of vertebral rotation in idiopathic scoliosis. Ortop. Traumatol. Rehabil. 8, 602–607 (2006)
61.Little, J.P., Adam, C.J.: The effect of soft tissue properties on spinal flexibility in scoliosis: biomechanical simulation of fulcrum bending. Spine 34, E76–82 (2009)
62.King, H.A., Moe, J.H., Bradford, D.S., et al..: The selection of fusion levels in thoracic idiopathic scoliosis. J. Bone Joint Surg. Am. 65, 1302–1313 (1983)
63.Lenke, L.G., Betz, R.R., Harms, J., et al.: Adolescent idiopathic scoliosis: a new classification to determine extent of spinal arthrodesis. J. Bone Joint Surg. Am. 83-A, 1169–1181 (2001)