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. 2009 Nov 14;2009:327-31.

Hierarchical Image Classification in the Bioscience Literature

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Free PMC article

Hierarchical Image Classification in the Bioscience Literature

Daehyun Kim et al. AMIA Annu Symp Proc. .
Free PMC article

Abstract

Our previous work has shown that images appearing in bioscience articles can be classified into five types: Gel-Image, Image-of-Thing, Graph, Model, and Mix. For this paper, we explored and analyzed features strongly associated with each image type and developed a hierarchical image classification approach for classifying an image into one of the five types. First, we applied texture features to separate images into two groups: 1) a texture group comprising Gel Image, Image-of-Thing, and Mix, and 2) a non-texture group comprising Graph and Model. We then applied entropy, skewness, and uniformity for the first group, and edge difference, uniformity, and smoothness for the second group to classify images into specific types. Our results show that hierarchical image classification accurately divided images into the two groups during the initial classification and that the overall accuracy of the image classification was higher than that of our previous approach. In particular, the recall of hierarchical image classification was greatly improved due to the high accuracy of the initial classification.

Figures

Figure 1
Figure 1
Five image types
Figure 2
Figure 2
Hierarchical image organization
Figure 3
Figure 3
Skewness and entropy
Figure 4
Figure 4
Pseudo-code for first image classification
Figure 5
Figure 5
Pseudo-code for 2nd image classification in the texture group
Figure 6
Figure 6
Smoothness of Graph and Model
Figure 7
Figure 7
Pseudo-code for second image classification in the non-texture group

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