Table 1.
Different nodule detection system using feature engineering approach in lung CT images
| Sl. No. | First author with publication year | Nodule detection process | Performance |
|---|---|---|---|
| 1. | Gong et al. (2018) [111] | 3-D tensor-filtering algorithm with local feature analysis followed by 3-D level-set segmentation | Sensitivity = 79.3% at an average of 4FPs/scan in LUNA16 database and sensitivity = 84.62% at an average of 2.8 FPs/scan in ANODE09 database |
| 2. | Farahani et al. (2018) [107] | Kernelized fuzzy c-means (MSFCM) clustering algorithm followed by morphological operation | Accuracy = 96.5%, sensitivity = 93.2%, specificity = 98.1% |
| 3. | Zhang et al. (2017) [70] | 3-D skeletonization based feature, voxel removal rate (VRR) | Avg. accuracy = 93.6%, sensitivity = 89.3% with 2.1 false-positive rate (FPR) per subject |
| 4. | Alam et al. (2017) [113] | Atlas-based segmentation method | Sensitivity = 100% |
| 5. | Narayanan et al. (2017) [102] | Intensity-based thresholding with morphological processing | Specificity of 3 false positives per case/patient on average and sensitivity in CT image is 87.86% |
| 6. | Jaffar et al. (2017) [115] | Multi-scale filter based on the eigenvalues of Hessian matrices | Accuracy = 98.7% and sensitivity = 97.5% |
| 7. | Aresta et al. (2017) [103] |
(1) Solid nodules are detected slice-wise inside a fixed-width sliding window with stride 1 followed by the Otsu threshold and logical OR operation. (2) Non-solid nodules are detected using LoG filter |
Sensitivity is 57.4% with 4 FPs/scan |
| 8. | Yuan et al. (2017) [122] | Semi-supervised based learning algorithm for automatic detection of GGO nodules | Recognition rate is in the range from 91 to 100% |
| 9. | Qiu et al. (2016) [42] | Gestalt psychology principle | Accuracy = 91.29% |
| 10. | Froz et al. (2016) [120] | Artificial crawlers and rose diagram–based method | Mean accuracy (mACC) = 94.30%, mean sensitivity (mSEN) = 91.86%, and mean specificity (mSPC) = 94.78% and mean area under the receiver operating characteristic (mROC) = 0.922 |
| 11. | Mehre et al. (2016) [104] | Combinations of thresholds and structuring elements | Sensitivity = 92.91% with 3 FP/scan |
| 12. | Nithila and Kumar (2016) [106] | Fuzzy c-means clustering with three clusters, viz., low, medium, and high | Accuracy is 98% for solid nodules, 99.5% for part-solid nodule, and 97.2% for non-solid nodules |
| 13. | Javaid et al. (2016) [75] | K-means algorithm | Accuracy = 96.22%, sensitivity = 91.65% with 3.19 FPs per case, and sensitivity = 83.33% for small size nodule |
| 14. | Gonga et al. (2016) [88] | 3-D dot filtering combined with dynamic self-adaptive template matching algorithm | Sensitivity = 90.24% with 4.54 FPs/scan in LIDC dataset, sensitivity = 84.1% with 5.59FPs/scan in ANODE09 dataset |
| 15. | Taşci and Uğur (2015) [123] | α-hull method | Accuracy = 95.88% and area under curve (AUC) = 0.9679 |
| 16. | Leemput et al. (2015) [62] | Shell filter | Overall system score was 0.336 |
| 17. | Novo et al. (2015) [116] | Central adaptive medialness technique with Hessian matrix | Sensitivity = 88.65%, |
| 18. | Yokota et al. (2015) [129] | Density and gradient information | Accuracy = 93.0% |
| 19. | Han et al. (2014) [121] | “Low level” vector quantization (VQ) algorithm | Overall sensitivity = 82.7%,specificity = 4 FPs/scan, and sensitivity = 89.2% at 4.14 FPs/scan for juxtapleural nodules |
| 20. | Ciompi et al. (2014) [133] | Bag-of-Frequencies (BoF) feature descriptor | Area under the ROC curve for spiculation (Az) = 0.907 (for axial),0.903 (for sagittal), and 0.911(for coronal) images |
| 21. | Santos et al. (2014) [68] | Hessian matrix with Tsallis’s and Shannon’s Q entropy features | Accuracy = 88.4%, sensitivity = 90.6%, specificity = 85% and false positives per exam is 1.17 |
| 22. | Filho et al. (2013) [45] | Quality threshold (QT) algorithm | Accuracy = 97.55%, sensitivity = 85.91%, specificity = 97.70%, with a false positive rate of 1.82 per exam and 0.008 per slice and area under the free-response operating characteristic is of 0.8062 |
| 23. | Choi and Choi (2013) [48] | Multi-scale dot enhancement filter with angular histogram of surface normals (AHSN) feature | Accuracy = 97.4%, overall sensitivity = 97.5% with 6.76 FPs/scan, and specificity = 97.7% |
| 24. | Keshani et al. (2013) [99] | ACM segmentation followed by 3-D averaging feature | Overall detection rate is 89% with 7.3 false positives per scan |
| 25. | Wang et al. (2013) [124] | 3-D matrix pattern technique | Overall sensitivity = 98.2% with 9.1 FPs per section |
| 26. | Assefa et al. (2013) [114] | Template matching algorithm with multi-resolution feature analysis technique | The detection rate is 81.212% |
| 27. | Cascio et al. (2012) [67] | Stable 3-D mass-spring model (MSM) is combined with spline curves | The detection rate of the system is 97% with 6.1 FPs/CT. A reduction to 2.5 FPs/CT is achieved at 88% sensitivity |
| 28. | Netto et al. (2012) [108] | Growing neural gas (GNG) clustering algorithm | The methodology ensures that nodules of reasonable size be found with sensitivity = 86%, specificity = 91%, and a mean accuracy of 91% |
| 29. | Choi et al. (2012) [79] | Optimal multiple thresholding and rule-based pruning | 94.1% sensitivity at 5.45 false positives per scan |
| 30. | Soltaninejad et al. (2012) [46] | 2-D stochastic features and 3-D anatomical features | 90% detection rate with 5.63FPs/scan |
| 31. | Dhara and Mukhopadhyay (2012) [57] | Geometry based diffusion (GBD) and selective enhancement filter | Volumetric overlap (VO) mean = 0.98 (solid nodule) and 0.93 for GGO nodule. Hausdorff distance (HD) mean = 9.4 (solid nodule) and 7.8 for GGO nodule |
| 32. | Chen et al. (2012) [117] | Blob-like structure enhancement (BSE) filter followed by an LoG filter | Average TPR = 93.6% with 12.3FPs/case |
| 33. | Farag et al. (2011) [112] | Template matching technique with AAM method | Sensitivity = 85% and specificity = 99% using SIFT feature descriptor |
| 34. | Hosseini et al. (2011) [109] | Gaussian interval type-2 membership functions (IT2MFs) | Average ROC accuracy is 95% with a 99% confidence interval (CI) of [92–99]% |
| 35. | Suárez-Cuenca et al. (2011) [105] | 3-D region-growing algorithm | 71.8% of nodules detected at 0.8 false positives per case, 75.5% at 1.6 FPs/case, and 80% at 3.4 FPs/case, respectively |
| 36. | Antonelli et al. (2011) [110] | Combined the output of three segmentation algorithms (1) robust fuzzy c-means (RFCM), (2) iterative threshold, and (3) region growing | Sensitivity = 92.5% and specificity = 83.5% |
| 37. | Pu et al. (2010) [80] | “Break-and-Repair” technique | RMS error (mm) 1.08 ± 0.45 and overlapping (%) 69.91 ± 9.43 |
| 38. | Messay et al. (2010) [81] | Combined intensity thresholding and morphological processing | Sensitivity = 82.66%, with an average of 3 FPs per CT scan/case |
| 39. | Namin et al. (2010) [60] | Shape index (SI) feature | Sensitivity = 88%, with 10.3 FPs per subject |
| 40. | Sousa et al. (2009) [66] | 3-D skeletonization algorithm | Sensitivity = 84.84%, specificity = 96.15%, and accuracy = 95.21% |
| 41. | Retico et al. (2009) [125] | 3-D directional-gradient concentration (DGC) algorithm, followed by a morphological opening operation | Sensitivity = 72% and the FP rejection ability of the system is up to 99% |
| 42. | Murphy et al. (2009) [119] | Shape index and curvedness features | Sensitivity = 80%, with an average of 4.2 FPs per scan |
| 43. | Ye et al. (2009) [86] | Shape index map and dot map | Average detection rate of 90.2%, with approximately 8.2 FP/scan |
| 44. | Retico et al. (2009) [118] | Dot enhancement filter and the “normals” to the pleura surface technique | Sensitivity in the 80–85% range is achieved with an average number of 6–9 FP per scan in their dataset and 67% sensitivity is achieved at 8 FP per scan in ANODE09 database |
| 45. | Choi and Choi (2009) [78] | Thresholding and shape information | 85.11% detection rate with 1.13 FPs per scan |