Automatic Detection of Defective Zebrafish Embryos via Shape Analysis

Abstract

In this paper, we present a graph-based approach to automatically detect defective zebrafish embryos. Here, the zebrafish is segmented from the background using a texture descriptor and morphological operations. In this way, we can represent the embryo shape as a graph, for which we propose a vectorisation method to recover clique histogram vectors for classification. The clique histogram represents the distribution of one vertex with respect to its adjacent vertices. This treatment permits the use of a codebook approach to represent the graph in terms of a set of codewords that can be used for purposes of support vector machine classification. The experimental results show that the method is not only effective but also robust to occlusions and shape variations. represent the embryo shape as a graph, for which we propose a vectorisation method to recover clique histogram vectors for classification. The clique histogram represents the distribution of one vertex with respect to its adjacent vertices. This treatment permits the use of a codebook approach to represent the graph in terms of a set of codewords that can be used for purposes of support vector machine classification. The experimental results show that the method is not only effective but also robust to occlusions and shape variations.