Table 2 Summary of mid-era literature
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 |