Fig. 1
Steps of the CHLOE process of image analysis. First the image features are computed using WNDCHRM, and then CHLOE is applied. The CHLOE input is the feature values computed by WNDCHRM, and its output is a file that contains the most likely outliers.
Fig. 2
Sample images of class "212" (first row), "406" (second row), "198" (third row), and "216" (fourth row) taken from the pollen dataset, and class "giantin" (last row).
Table 1
Image datasets used for the experiments. Each experiment includes one set of typical images, and one set of outlier images. The method is used to detect a single outlier image in the set of outlier images.
| Dataset | Typical class | Outlier class | Images in typical class |
|---|---|---|---|
| Pollen | 198 | 212 | 90 |
| Pollen | 212 | 198 | 90 |
| Pollen | 216 | 406 | 90 |
| Pollen | 406 | 216 | 90 |
| CHO | hoechst | giantin | 69 |
| CHO | gianti | hoechst | 69 |
| Hela | actin | dna | 98 |
| Hela | dna | Actin | 87 |
| Hela | golgpp | Er | 86 |
| Hela | er | golgpp | 85 |
| Fruit Fly | stage 4 to 6 | stage 13 to 16 | 90 |
| Fruit Fly | stage 13 to 16 | stage 4 to 6 | 90 |
| C. elegans TB | Day 0 | Day 6 | 112 |
| C. elegans TB | Day 0 | Day 10 | 112 |
| C. elegans TB | Day 0 | Day 12 | 112 |
| RNAi | Untreated | CG7825 | 1500 |
| RNAi | Untreated | CG8114 | 1500 |
| RNAi | Untreated | CG8711 | 1500 |
| OAI | Women | Men | 1000 |
| OAI | Men | Women | 600 |
Fig. 3
The average detection accuracy of the outlier images for all image classes in Table 1 (where q = 5 and j = 5). In each experiment one class of images was used as the collection of outlier images, and another class was used as the typical images as specified in Table 1. The detection accuracy reflects the ability of the method to correctly detect an outlier image in the set of typical images.
Fig. 4
The eight top images detected by CHLOE when using the pollen experiment. The outlier is ranked second among the 91 images used in the experiment.
Fig. 5
The detection accuracy of the outlier image as a function of the q value (where k = 10). Clearly, the higher the number of likely outlier gets larger, the higher probability that the actual outlier will be among the detected outliers. The downside of increasing the value of q is that the program will make more detections, and therefore will require more human labour to analyse the output manually. For instance, when q is equal to 10 it means that the experimentalist will need to examine 10 output samples manually.
Fig. 6
The detection accuracy of the outlier image as a function of the j value (where q = 5 and k = 10). When more outliers are placed among the typical images, the method has a higher chance of detecting one of them as the actual outlier.
Fig. 7
The detection accuracy of the outlier image as a function of the k value (where q = 5 and j = 5). A higher k value reduces the chance of detecting a single outlier that has no similar samples in the dataset.
Fig. 8
Detection accuracy of cells treated by knockdown of different genes among untreated cells. The detection accuracy increases with kas it leads to the rejection of single outliers that are not related to the different gene, but it starts to decrease when the method is not able to detect more than kself-similar outliers.
Fig. 9
Detection accuracy of C. elegans terminal bulb microscopy images at an older age detected in a set of images taken at a younger age.
Fig. 10
Detection accuracy of men knee x-rays among a dataset of women knee x-rays, and women knee x-rays among a dataset of men knee x-rays.