Table 2.
Summary of reviewed works related to classical segmentation in mammogram image.
| Subcategory | Related works | Year | Technique | Filter | Database | Evaluation metric |
|---|---|---|---|---|---|---|
| RM | [77] | 1999 | Adaptive and region growing | Gaussian | UMH | 98.0% accuracy |
| RM | [102] | 2001 | Region growing | Kalman | DDSM | 93.0% ROC with adaptive module and 86.0% ROC without the adaptive module |
| RM | [97] | 2001 | Partial loss of region | Sober | Japanese | 97.0% true positive |
| RM | [99] | 2004 | Region growing | MIAS | 90.0% TPR, and 1.3 FTR per image | |
| RM | [103] | 2004 | Contour searching | MAGIC-5 | 85.6 ± 08% ROC | |
| RM | [87] | 2005 | Region growing | ANN | MIAS | 92.5% accuracy |
| RM | [90] | 2006 | Morphological algorithm | Median | MIAS | 95.0% detection rate |
| RM | [75] | 2010 | Harris corner | Median | MIAS | 93.0% segmentation accuracy |
| RM | [91] | 2010 | Region growing | DDSM | 78.0% sensitivity and 4.0% false positive | |
| RM | [92] | 2010 | Watershed | Morphological | DDSM | Mean standard 0.93 ± 0.03 |
| RM | [74] | 2011 | Thresholding | Median | MIAS | 99.0% segmentation accuracy |
| RM | [82] | 2012 | Region growing | Contrast | MIAS | 94.59% sensitivity and 3.90 false positive |
| RM | [86] | 2012 | Morphological | Median | MIAS | 95.0% detection rate |
| RM | [96] | 2012 | Region growing | Adaptive | DDSM | 97.2% sensitivity and 1.83% false positive |
| RM | [80] | 2012 | Seed point selection | Mathematical morphology | NCSM | 98.0% accuracy |
| RM | [81] | 2013 | Morphological gradient watershed | Adaptive median | MIAS and NMR | 95.3% positive for MIAS and 94.0% for NMR |
| RM | [101] | 2013 | Improved watershed | Median | MIAS | 92.0% accuracy |
| RM | [76] | 2013 | Otsu | Morphological | DEMS | 95.06% accuracy |
| RM | [95] | 2014 | Marker-controlled watershed | Sober | MIAS | 90.83% detection rate and 91.3% ROC |
| RM | [84] | 2014 | Wavelet and genetic algorithm | Wiener | MIAS and DDSM | 79.2 ± 8% mean and standard deviation |
| RM | [98] | 2014 | Watershed transformation | MSKE | 90.47% sensitivity, 75.0% specificity, and 84.848% accuracy | |
| RM | [79] | 2015 | Morphological operators | Alternating sequential filter | MIAS | 99.2% sensitivity and 99.0% accuracy |
| RM | [83] | 2017 | Region growing | Sliding window | MIAS | 91.3% accuracy |
| RM | [100] | 2017 | Region growing | Median | MIAS | 94.0% accuracy |
| RM | [93] | 2017 | Watershed | Morphological | DDSM | 80.5% similarity index, 75.7% overlap value |
| RM | [94] | 2017 | Bimodal-level set formulation | MIAS | 96.72% precision and 97.22% recall | |
| RM | [88] | 2018 | Hidden Markov and region growing | MIAS | 91.92% accuracy and 8.07% error | |
| RM | [89] | 2018 | Watershed combined with k-NN | Sober | MIAS | 83.33% accuracy |
| RM | [78] | 2018 | Region growing | Gaussian | DDSM | 98.1% sensitivity, 97.8% specificity, and 90.0% accuracy |
| RM | [85] | 2019 | Watershed | MIAS | 94.0% false detection and 18.0% positive detection | |
|
| ||||||
| TM | [118] | 2001 | Otsu thresholding | Morphological | MIAS | 1.7188 ME1, 0.0083 ME2, and 0.8702 MHD |
| TM | [120] | 2001 | Otsu | Median | MIAS | 96.55% accuracy, 96.97% sensitivity, and 96.29% specificity |
| TM | [113] | 2011 | MIAS | 97.0% accuracy, 97.03% specificity, and 97.0% sensitivity | ||
| TM | [117] | 2012 | Histogram thresholding | Morphological | DDSM | 96.0% detection rate and 90.0% accuracy |
| TM | [119] | 2012 | Kittler's optimal thresholding | BCCCF | 92.0% to 95.0% Spearman and 6.9% average density | |
| TM | [109] | 2013 | Otsu | Median | ||
| TM | [108] | 2014 | Rough set theory | Median | MIAS | |
| TM | [107] | 2014 | Otsu thresholding | Morphological and median | DDSM | |
| TM | [114] | 2014 | Threshold and evolutionary | Average | DDSM | 95.2% accuracy |
| TM | [110] | 2014 | Otsu | Median | MIAS | |
| TM | [115] | 2015 | Global threshold | Median | MIAS | 92.86% accuracy and acceptable level of 4.97% |
| TM | [111] | 2015 | Global thresholding and merging | Wiener | 82.0% accuracy and 18.0% error detection | |
| TM | [105] | 2016 | Morphological threshold | Median | MIAS | 94.54% accuracy and 5.45% false identification |
| TM | [106] | 2016 | Adaptive threshold | 91.5% accuracy for SVM and 70.0% accuracy for k-NN | ||
| TM | [121] | 2016 | Otsu | Morphological | WHC and DDSM | 100.0% accuracy for WHC and 91.30% for DDSM |
| TM | [104] | 2017 | Otsu | Clahe | MIAS | 96.0% accuracy |
| TM | [116] | 2017 | Histogram and edge detection | Gaussian | MIAS and EPIC | 98.8% accuracy (MIAS) and 91.5% (EPIC) |
| TM | [112] | 2018 | Adaptive global and local threshold | Meteorological | MIAS | 91.3% sensitivity and 0.71% false positive |
|
| ||||||
| EM | [128] | 2004 | Edge | 2-D | MIAS | 92.5% accuracy, 93.0% sensitivity, and 85.0% specificity |
| EM | [122] | 2006 | Edge | MAGIC-5 collaboration | 86.20% ROC and 82.0% sensitivity | |
| EM | [126] | 2009 | Histogram | Morphological | MIAS | 97.0% accuracy |
| EM | [133] | 2011 | Active contour | Binary homogeneity | MIAS | 99.6% CM, 98.7% CR, and 98.3% quality |
| EM | [131] | 2011 | Energy minimisation and contour | MIAS | 90.0% accuracy and 92.27% precision | |
| EM | [134] | 2011 | Edge | Median | KHCCJH | 94.1% accuracy (CC), 81.4% MLO, and 90.0% accuracy |
| EM | [130] | 2011 | Sobel, Prewitt, Laplacian | Adobe Photoshop | NCSM | 79.0% AUC for Sobel, 72.0% Prewitt, and 71.0% Laplacian |
| EM | [127] | 2012 | Edge | Median | MIAS | 83.9% accuracy |
| EM | [132] | 2014 | Active contour | 88.0% sensitivity | ||
| EM | [123] | 2015 | Dynamic graph cut | MIAS and DDSM | 98.88% sensitivity, 98.89% specificity, and 93.0% for negative values | |
| EM | [124] | 2015 | Canny edge detection | Median | MIAS, INbreast, and BCDR | 98.8% Dice boundary of 97.8% MIAS, 98.9% for boundary 89.6% INbreast, and 99.2% for boundary, and 91.9% BCDR |
| EM | [135] | 2017 | Cascade | Gabor | UHGL | 100.0% sensitivity and 3.4% false positives |
| EM | [129] | 2017 | Edge | NCSM | 84.0% AUC | |