Segmentation Approach Towards PCM Images 1137


Figure 9. a: Original image, (b) gold approximation, (c) saturation, (d) edge, (e) k-means, (f) adaptive thresholding, (g) texture, (h) watershed, (i) Kittler, (j) split-merge, (k) top-bottom-hat.


failure in the paragraph ahead. Watershed algorithm-based segmentation detected some parts of filaments, but led to significant under-segmentation as shown in Figure 8h. The split-and-merge algorithm-based segmentation was affected by halos as shown in Figure 8j. On one hand, it over- segmented the detected filaments. On the other hand, it significantly misclassified the filaments as background. However, the saturation channel, edge, k-means, adaptive thresholding, texture, top-bottom-hat filtering-based


algorithms segmented the filaments successfully with some over-segmentation as shown in Figure 8. As far as the image


in Figure 9 is concerned, such a scenario of very few or rare filaments may occur in the case of pin-floc state or normal state with small sludge volume index. Most of the algorithms failed to segment such images as shown in Figure 9. Unlike other algorithms, the top-bottom-hat filtering-based algo- rithm detected the filament correctly as shown in Figure 9k. However, it detected several false objects too. In conclusion,


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