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Table 2 Summary of mid-era literature

From: Spinal vertebrae localization and analysis on disproportionality in curvature using radiography—a comprehensive review

Sr. no. Author Year Pre-processing Technique Diseases Image type Dataset Results Evaluation metrics
1 Brejl and Sonka [19] 2000 Manual contouring and landmarking. Shape-variant Hough and edge-based object MRI thorax 55 images (15 training) 1.8 ± 0.6 1.0 ± 0.3 1.8 ± 0.5 Mean error of approximate location mean error of accurate border detection
2 Tezmol et al. [20] 2002 Gaussian smoothed image and unsharp masking Customize Hough transform Cervical vertebrae X-ray NHANES II 50 images 72.06/80 average 4.16 LMP falling in boundary box orientation error
3 Peng et al. [22] 2006 Model-based search method and intensity profiling polynomial function Center point extended profiling canny edge Intervertebral disc MRI scans 5 Sets of images 94% successful
4 Lin [23] 2007 3D Bezier curves Multilayer feed-forward, back-propagation artificial neural network King classification Spine deformity X-ray 37 images Highest rn = 0.83 at 2 hidden layers highest rn = 0.75 at 1 hidden layer Identification rate = correctly identified pattern / total validating patterns
5 Xu et al. [25] 2008 9 morphometric landmark-point corner guided Dynamic programming, partial shape matching Vertebral shapes X-rays NHANES-II 900 images Lowest precision of PSM is above 85% Precision= TP / (TP + FP)
6 Tobias et al. [27] 2009 Vertebra coordinate system intensity information Generalized Hough transform progressive adaptation method Vertebrae segmentation CT images 64 patients 1.12 ± 1.04 mm Mean point-to-surface error
7 Ribeiro et al. [28] 2010 Manually delineated plateaus setting 180 gabor filter bank and ANN Cervical X-rays 41 images 0.91–0.92 high overlap success rate Overlap between detected and manually delineated plateaus
8 Anitha and Prabhu [29] 2011 Anisotropic filtering Gradient vector field Snake and Hough transform providing slope Scoliosis X-Ray 250 images Intra-observer error is eliminated through true identification of the required end vertebras.
9 Larhmam et al. [30] 2012 Manual ROI histogram equalization canny and Sobel Modified Hough, template matching, and contrast limited adaptive histogram equalization Cervical vertebrae X-ray NHANES-II 200 images 89% Global accuracy
10 Sardjono et al. [31] 2013 Manual Cobb multiple X-ray stitched to get whole spine Auto Cobb angle by charged-particle model (CPM) piece-wise linear curve fitting cubic spline and polynomial curve Scoliosis Frontal radiographs 36 images R2 of 0.9124 and 0.9175 Mean absolute error
11 Rasoulian et al. [32] 2013 GMM and PCA Expectation maximization, Gaussian filter, and canny Vertebrae shape CT scans 32 images Distance error 1.38 ± 0.56 Mean point-to- surface error