AUTOMATIC ANNOTATION OF BIOLOGICAL IMAGES

Annotation of biological images is to label biological images or parts of the images, often using ontological or anatomical vocabularies. The task has become increasingly important along with the availability of large data sets of multidimensional and multiscale biological images in recent years. Examples include the annotation of the anatomical regions of gene expression patterns of Drosophila melanogaster (fruitfly), or the annotation of cellular or subcellular structures for other model organisms at different developmental stages. To automate the process in order to increase efficiency and consistency, the annotation problem can be formulated as a pattern recognition task with ontological or anatomical terms viewed as the targets to be recognized in the image. The task presents various objectives: we may annotate the entire image or many Regions-Of-Interest (ROI) in an image; the problem may be mutual-exclusive (when the image or region corresponds to one target) or multi-objective (when several annotation targets co-exist in an image). The task also has unique challenges such as skewed data distribution, morphological variety and big image size. In this chapter, we discuss algorithms and applications for automated annotation of biological images. We detail the algorithms for in situ fruitfly gene expression patterns during embryogenesis which is a multi-objective annotation problem. We also cover mutual-exclusive and ROI annotation applications. We demonstrate that extracting and selecting a concise set of good image features are essential for such applications and show effectiveness of our proposed algorithms.