Compound image segmentation of published biomedical figures | Zendy

Pengyuan Li | Zendy; Xiangying Jiang | Zendy; Chandra Kambhamettu | Zendy; Hagit Shatkay | Zendy

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Compound image segmentation of published biomedical figures

Author(s) -

Pengyuan Li,

Xiangying Jiang,

Chandra Kambhamettu,

Hagit Shatkay

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx611

Subject(s) - segmentation , computer science , market segmentation , component (thermodynamics) , image segmentation , artificial intelligence , information retrieval , image (mathematics) , code (set theory) , source code , data mining , computer vision , pattern recognition (psychology) , physics , set (abstract data type) , marketing , business , thermodynamics , programming language , operating system

Images convey essential information in biomedical publications. As such, there is a growing interest within the bio-curation and the bio-databases communities, to store images within publications as evidence for biomedical processes and for experimental results. However, many of the images in biomedical publications are compound images consisting of multiple panels, where each individual panel potentially conveys a different type of information. Segmenting such images into constituent panels is an essential first step toward utilizing images.

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