Extraction of Endoscopic Images for Biomedical Figure Classification

Abstract

Modality filtering is an important feature in biomedical image searching systems and may significantly improve the retrieval performance of the system. This paper presents a new method for extracting endoscopic image figures from photograph images in biomedical literature, which are found to have highly diverse content and large variability in appearance. Our proposed method consists of three main stages: tissue image extraction, endoscopic image candidate extraction, and ophthalmic image filtering. For tissue image extraction we use image patch level clustering and MRF relabeling to detect images containing skin/tissue regions. Next, we find candidate endoscopic images by exploiting the round shape characteristics that commonly appear in these images. However, this step needs to compensate for images where endoscopic regions are not entirely round. In the third step we filter out the ophthalmic images which have shape characteristics very similar to the endoscopic images. We do this by using text information, specifically, anatomy terms, extracted from the figure caption. We tested and evaluated our method on a dataset of 115,370 photograph figures, and achieved promising precision and recall rates of 87% and 84%, respectively.

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Cite this paper

@inproceedings{Xue2015ExtractionOE, title={Extraction of Endoscopic Images for Biomedical Figure Classification}, author={Zhiyun Xue and Daekeun You and Suchet K. Chachra and Sameer K. Antani and L. Rodney Long and Dina Demner and George R. Thoma}, year={2015} }