TABLE 3.
Systematic review of image assisted method towards EI estimation.
| Study | Data analysis method | No of Food items |
Image analysis** |
Compared against*** |
Performance /accuracy |
Real-time analysis, platform |
|---|---|---|---|---|---|---|
| Martin (2009) [45] | Captures images of food selection, leftovers and a reference card, nutritionist analyses images to estimate El | NA | Manual | WFR | Free-living El: −152 ± 694 kcal/day | Phone app |
| Wu (2009) [46] | Recognizes foods in videos of eating recorded by a web camera, extracting SIFT features and utilizing pre-trained food model, predicts energy contents from standard fast food database | 101 | Automatic | DB | Not reported | NA |
| Zhu (2010) [29] | Segments images using hierarchical segmentation technique, classifies regions into food categories using SVM classifier, recognizes food items, estimates portion size through volume estimation of the food from which the energy contents determined | 19 | Automatic | NA | Food recognition: 95.8% | Phone app |
| Wazumi (2011) [47] | Segments images by hough transformation, extracts rotation invariant SPIN features, records food info in a food log system | 10 | Manual | NA | Food recognition: 78% | NA |
| Kong (2012) [48] | SIFT/statistical feature based food classification, then estimation of energy content of food items through 3D model reconstruction based volume estimation | NA | Automatic | NA | Food recognition: 92% | Phone app |
| Rahman (2012) [33] | Captures stereo pairs of images, performs feature matching between stereo images, creates a 3D model of food items to estimate volume of the food | 6 | Automatic | VWD† | Volume (ml) error: 7.7% | NA |
| Pouladzadeh (2014) [49] | Segments images, estimates food portion from SVM classifier, measures volume and calculates nutritional facts from tables. | 15 | Semi-automatic | DB | Food recognition : 92.21% (single food), 85% (non-mixed foods), 35-65% (mixed foods) Energy content (kcal) accuracy: 86% | NA |
| Myers (2015) [50] | Detects meal from single image, predicts foods, volume and size of foods using CNN, estimates energy content from USDA dataset | 2517 | Automatic | NA | NA | Real-time, phone app |
| McAllister (2015) [51] | Segments images, asks the participants to indicate the food area, estimates area covered by foods from regression model, computes energy content from selected region | 1 | Semi-automatic | DB | Energy content (kcal) of food item portions : 89.12% | NA |
| Zhang (2015) [52] | Segments images using hierarchical segmentation technique, classifies regions into food categories using SVM classifier, estimates portion size of the food to determine caloric contents | 15 | Automatic | NA | Food recognition: 85% | NA |
| Fang (2015) [53] | Estimates portion size automatically using geometric contextual information from the scene. | 19 | Automatic | WFR | Energy (kcal) error : 6% | NA |
| Sun (2015) [54] | Detects regular shaped utensils, segments food items based on color, texture measures, estimates volume of each food item using food-specific shape model, computes energy contents and nutrients using FNDDS database | 10 | Automatic | VSD | Volume (cm ) estimation error : 30% | NA |
| Pouladzadeh (2016) [55] | Segments images using graph-cut segmentation technique, classifies and recognizes food items using deep learning neural networks, measures portion size and calculates nutritional facts from tables. | 30 | Semi-automatic | DB † | Food recognition : 99% (single food) Energy content (kcal) estimation : NA | Real time, Phone app |
| Liao (2016) [56] | Acquires depth images, Filters noisy depth images, estimates volume of the foods, estimates mass of food intake using specific gravity function | 3 | Automatic | WFR | Food intake mass (g) error : 7.5% | NA |
| Hippocrate (2016) [57] | Estimates the diameter and the height of the food container and derives the food volume, given the food type, estimates the mass of the food in the image | 15 | Automatic | WFR | Mass (g) estimation error : 6.78% | NA |
| McClung (2017) [58] | Captures images using camera, estimates energy content by visual estimations from two estimator | NA | Manual | NA | NA | NA |
| Hassannejad (2017) [59] | Segments images by semi segmentation method interactive and user-dependent, creates a 3D model using a point-cloud based image modeling algorithm of food items to estimate volume of the food | 10 | Semi-automatic | NA | Volume (mm3) estimation accuracy: 92% | NA |
**
Note: Manual: Analyzed by trained individual; Semi-Automatic: Foods in the images are marked by the user before processing; Automatic: Fully automated analysis. NA, not available.
***
WFR: weighed food record; DB: Nutrition database/ Nutritional Fact labels; NA, not applicable.
†
VWD: measure volume using water displacement; VSD: measure volume using seed displacement