title: AISO: Annotation of Image Segments with Ontologies.
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.
authors.affiliation: School of Electrical Engineering and Computer Science, Kelley Engineering Center, Oregon State University, Corvallis, OR 97331-2902 USA.
authors.forename: Nikhil Tej
authors.initials: NT
authors.lastname: Lingutla
authors.affiliation: Department of Botany and Plant Pathology, Oregon State University, 2082 Cordley Hall, Corvallis, OR 97331-2902 USA.
authors.forename: Justin
authors.initials: J
authors.lastname: Preece
authors.affiliation: School of Electrical Engineering and Computer Science, Kelley Engineering Center, Oregon State University, Corvallis, OR 97331-2902 USA.
authors.forename: Sinisa
authors.initials: S
authors.lastname: Todorovic
authors.affiliation: Department of Botany and Plant Pathology, Oregon State University, 2082 Cordley Hall, Corvallis, OR 97331-2902 USA.
authors.forename: Laurel
authors.initials: L
authors.lastname: Cooper
authors.affiliation: Department of Botany and Plant Pathology, Oregon State University, 2082 Cordley Hall, Corvallis, OR 97331-2902 USA.
authors.forename: Laura
authors.initials: L
authors.lastname: Moore
authors.affiliation: Department of Botany and Plant Pathology, Oregon State University, 2082 Cordley Hall, Corvallis, OR 97331-2902 USA.
authors.forename: Pankaj
authors.initials: P
authors.lastname: Jaiswal
keywords: Computer vision; Image annotation; Image curation; Image segmentation; Machine learning; Plant anatomy; Plant ontology; Semantic web; Web services