Schoening T et al. (2012), Semi-automated image analysis for the assessmen...

ECB-ART-42478

PLoS One 2012 Jan 01;76:e38179. doi: 10.1371/journal.pone.0038179.

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Semi-automated image analysis for the assessment of megafaunal densities at the Arctic deep-sea observatory HAUSGARTEN.

Schoening T , Bergmann M , Ontrup J , Taylor J , Dannheim J , Gutt J , Purser A , Nattkemper TW .

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Megafauna play an important role in benthic ecosystem function and are sensitive indicators of environmental change. Non-invasive monitoring of benthic communities can be accomplished by seafloor imaging. However, manual quantification of megafauna in images is labor-intensive and therefore, this organism size class is often neglected in ecosystem studies. Automated image analysis has been proposed as a possible approach to such analysis, but the heterogeneity of megafaunal communities poses a non-trivial challenge for such automated techniques. Here, the potential of a generalized object detection architecture, referred to as iSIS (intelligent Screening of underwater Image Sequences), for the quantification of a heterogenous group of megafauna taxa is investigated. The iSIS system is tuned for a particular image sequence (i.e. a transect) using a small subset of the images, in which megafauna taxa positions were previously marked by an expert. To investigate the potential of iSIS and compare its results with those obtained from human experts, a group of eight different taxa from one camera transect of seafloor images taken at the Arctic deep-sea observatory HAUSGARTEN is used. The results show that inter- and intra-observer agreements of human experts exhibit considerable variation between the species, with a similar degree of variation apparent in the automatically derived results obtained by iSIS. Whilst some taxa (e. g. Bathycrinus stalks, Kolga hyalina, small white sea anemone) were well detected by iSIS (i. e. overall Sensitivity: 87%, overall Positive Predictive Value: 67%), some taxa such as the small sea cucumber Elpidia heckeri remain challenging, for both human observers and iSIS.

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Genes referenced: LOC100887844 LOC100893907 LOC583082

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	Figure 1. Map of the HAUSGARTEN Observatory.The main sampling station (HAUSGARTEN IV) is located at the intersection of the red lines.
	Figure 2. Three samples of each of the eight taxa used for detection.From left to right: small white sponge, Kolga hyalina, Elpidia heckeri, Bathycrinus carpenterii, burrow hole, purple anemone, Bathycrinus stalk, small white sea anemone.
	Figure 3. The combination of human labels to gold standard labels.The left image shows a small white sea anemone with two human labels (as circles) which is not enough to create a gold standard label as a supporter count of was required (see text for details). The image in the middle shows a Kolga hyalina labeled by experts and its resulting gold standard label in between (as a cross). The right image shows a Bathycrinus carpenterii with human labels for the crown (blue) as well as the stalk (yellow). Both human label cliques have supporter and thus two gold standard labels are created.
	Figure 4. The complete (semi-)automated detection process.Different transects with several thousand images are stored in the BIIGLE online platform (top left). These images can be accessed by experts via the WWW (bottom left). For this experiment, a subset of one transect (marked green on the upper left) was shown to five experts to create a manually labelled training set for a group of pre-defined taxa. Those manual labels were at first used to optimize an image pre-processing for illumination correction (top middle). Afterwards, high dimensional feature vectors were extracted at the label positions to gain a training and test set for SVM optimization (bottom middle). The trained SVMs were then applied pixel-wise to the full field of view, to obtain a confidence value for each pixel and taxon (top right). These confidence values were then post-processed into a classification map, where each pixel is assigned to one taxon which allows taxon counts per image. These taxon counts can then be plotted along the length of the transect (bottom right).
	Figure 5. Successive classification with different SVMs.To prevent a time-consuming classification of each feature vector with all SVMs, the SVMs were ordered in a tree structure. The order of SVMs and the confidence thresholds as well as the blob sizes were tuned automatically according to the resulting Sensitivity and Positive Predictive Value.
	Figure 6. Illustration of the pre-processing.Image A is an original sample taken from the HAUSGARTEN IV transect. B - F show the effect of different kernel sizes M for the Gaussian filter. The kernel sizes are as follows: B: M = 11, C: M = 101, D: M = 701, E: M = 1101, F: M = 1401). The curves show the output of the cluster-indices, plotted against M. The first value (M = 0) represents the unfiltered image. The curves are as follows: blue: Chalinski-Harabasz, green: Index-I, yellow: Davies-Boudlin, pink: intra-cluster variance, red: inter-cluster variance. The bold, black line is the mean of the five measures. The cluster indices were normalized to the interval [0.1] and show a good correlation, supporting a reasonable selection of the value M = 701.
	Figure 7. Detection results for five species.From top to bottom: A: small white sea anemone, B: burrow, C: Bathycrinus stalk, D: Bathycrinus carpenterii, E: Kolga hyalina. Each unit on the x-axis represents an image of the transect (i. e. 70 images). The y-axis represents the object counts. Green bars stand for the amount of gold standard objects with 3. Blue bars represent the machine counts. The plots are normalized according to the maximum object count for each taxon individually. The correlation between the gold standard and machine counts are given in Table 2.
	Figure 8. Example of the final classification.On the left we show the original seafloor image with expert labels shown as colored squares. The colors encode taxa: red: Kolga hyalina, green: Elpidia heckeri, blue: Bathycrinus carpenterii, yellow: Bathycrinus stalk, pink: small white sea anemone, dark blue: small white sponge, dark red: purple anemone, dark green: Burrow, turquoise: background. On the right we show the images’ classification results. The same color code is used as for the expert positions labels on the left. Black regions were rejected by all SVMs.

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Davies, A cluster separation measure. 1979, Pubmed