Format

Send to

Choose Destination
J Biomed Semantics. 2014 Dec 17;5(1):50. doi: 10.1186/2041-1480-5-50. eCollection 2014.

AISO: Annotation of Image Segments with Ontologies.

Author information

1
School of Electrical Engineering and Computer Science, Kelley Engineering Center, Oregon State University, Corvallis, OR 97331-2902 USA.
2
Department of Botany and Plant Pathology, Oregon State University, 2082 Cordley Hall, Corvallis, OR 97331-2902 USA.

Abstract

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.

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.

KEYWORDS:

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

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center