AISO: Annotation of Image Segments with Ontologies

Journal of Biomedical Semantics

Pankaj Jaiswal²  Laura Moore²  Laurel Cooper²  Sinisa Todorovic¹  Justin Preece²  Nikhil Tej Lingutla¹ 

[1] School of Electrical Engineering and Computer Science, Kelley Engineering Center, Oregon State University, Corvallis, OR 97331-2902, USA;Department of Botany and Plant Pathology, 2082 Cordley Hall, Oregon State University, Corvallis, OR 97331-2902, USA

关键词: Machine learning;    Image curation;    Computer vision;    Web services;    Plant anatomy;    Image segmentation;    Plant ontology;    Semantic web;    Image annotation;   

Others  :  1133360
DOI  :  10.1186/2041-1480-5-50

 received in 2014-09-13, accepted in 2014-11-26,  发布年份 2014

【 摘 要 】

Background

Large quantities of digital images are now generated for biological collections, including those developed in projects premised on the high-throughput screening of genome-phenome experiments. These images often carry annotations on taxonomy and observable features, such as anatomical structures and phenotype variations often recorded in response to the environmental factors under which the organisms were sampled. At present, most of these annotations are described in free text, may involve limited use of non-standard vocabularies, and rarely specify precise coordinates of features on the image plane such that a computer vision algorithm could identify, extract and annotate them. Therefore, researchers and curators need a tool that can identify and demarcate features in an image plane and allow their annotation with semantically contextual ontology terms. Such a tool would generate data useful for inter and intra-specific comparison and encourage the integration of curation standards. In the future, quality annotated image segments may provide training data sets for developing machine learning applications for automated image annotation.

Results

We developed a novel image segmentation and annotation software application, “Annotation of Image Segments with Ontologies” (AISO). The tool enables researchers and curators to delineate portions of an image into multiple highlighted segments and annotate them with an ontology-based controlled vocabulary. AISO is a freely available Java-based desktop application and runs on multiple platforms. It can be downloaded at http://www.plantontology.org/software/AISO webcite.

Conclusions

AISO enables curators and researchers to annotate digital images with ontology terms in a manner which ensures the future computational value of the annotated images. We foresee uses for such data-encoded image annotations in biological data mining, machine learning, predictive annotation, semantic inference, and comparative analyses.

【 授权许可】

   
2014 Lingutla et al.; licensee BioMed Central.

【 图 表 】

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