Automatic Detection of Defective Zebrafish Embryos via Shape Analysis
View/ Open
Author(s)
Zhao, Haifeng
Zhou, Jun
Robles-Kelly, Antonio
Lu, Jingfeng
Yang, Jing-Yu
Griffith University Author(s)
Year published
2009
Metadata
Show full item recordAbstract
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 ...
View more >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.
View less >
View more >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.
View less >
Conference Title
2009 DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA 2009)
Copyright Statement
© 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subject
Computer vision