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Detection of isolated nematodes in clutter environments using shape feature histograms

Abstract:

We present an approach for detection of isolated Caenohabditis Elegans nematodes in clutter environments. The method is based on shape feature histograms which describe the distribution of features of second-order derivative responses of linear image structures. The shape features are able to distinguish isolated from overlapping nematodes and clutter, thereby improving the automated image analysis of nematode populations where accurate assessment of shape is needed. An evaluation is performed on a database of manually segmented images. Shape continuity features proved to have the highest discriminative power. This is consistent with the morphological structure of this kind of organism. Our experiments suggest that similar techniques can be used for identification of other linear shaped biological objects.