A Collaborative Biomedical Image-Mining Framework: Application on the Image Analysis of Microscopic Kidney Biopsies

• Published in: IEEE journal of biomedical and health informatics Vol. 17; no. 1; pp. 82 - 91
• Main Authors: Goudas, T., Doukas, C., Chatziioannou, A., Maglogiannis, I.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2013
• Subjects: Biomedical imaging; Biopsy - methods; Classification; Data mining; Data Mining - methods; Databases, Factual; Feature extraction; Humans; image microscopy; image mining; Image Processing, Computer-Assisted - methods; intelligent planning; Kidney; Kidney - pathology; kidney biopsies; Medical Informatics - methods; Microscopy - methods; Models, Theoretical; Planning; Web services; workflow manager
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/TITB.2012.2224666

The analysis and characterization of biomedical image data is a complex procedure involving several processing phases, such as data acquisition, preprocessing, segmentation, feature extraction, and classification. The proper combination and parameterization of the utilized methods are heavily relying on the given image dataset and experiment type. They may thus necessitate advanced image processing and classification knowledge and skills from the side of the biomedical expert. In this study, an application, exploiting web services and applying ontological modeling, is presented, to enable the intelligent creation of image-mining workflows. The described tool can be directly integrated to the RapidMiner, Taverna or similar workflow management platforms. A case study dealing with the creation of a sample workflow for the analysis of kidney biopsy microscopy images is presented to demonstrate the functionality of the proposed framework.