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Authors: Philipp Kainz 1 ; Harald Burgsteiner 2 ; Helmut Ahammer 3 and Martin Asslaber 1

Affiliations: 1 Medical University of Graz, Austria ; 2 FH JOANNEUM - University of Applied Sciences Graz, Austria ; 3 Medical University Graz, Austria

ISBN: Not Available

Keyword(s): Reservoir Computing, Recurrent Neural Networks, Classification, Medical Image Analysis, Haematopoietic Compartments, Bone Marrow.

Abstract: Background. Histomorphometry of haematopoiesis in the human bone marrow is a mandatory element in lots of daily diagnosis processes in pathology. The determination of relative quantities of the haematopoietic compartments is currently performed visually by the individual pathologist using conventional microscopy. Hence, intra- and inter-observer variability is unavoidable, but standardized quantitative methods are not available yet. Standard image processing methods are limited when it comes to automated classification of objects within a histological image but methods and paradigms of Computational Intelligence (CI) have the potential to overcome these barriers. Specific Aims. The proposed PhD project is intended to develop and implement a machine learning system for the automated quantification of objects in histological images. The major tasks are the development of a classifier based on the reservoir computing paradigm for automated pattern recognition and classification as w ell as its prototypical software implementation. Research Methods. Histological sections of human bone marrow are stained using histological standard techniques. Experienced pathologists will label the haematopoietic compartments in a software system and the data sets for the classifier are generated. We are going to train the algorithm on the labeled image patches in order to distinguish different cell classes. Expected Results. This classification system will contribute to the progress in digital pathology in terms of decreasing the overall intra- and inter-observer variability in the diagnostics of human bone marrow specimen. Furthermore, we emphasize the potential of CI algorithms in medical image analysis and pattern recognition. (More)

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Paper citation in several formats:
Kainz, P.; Burgsteiner, H.; Ahammer, H. and Asslaber, M. (2013). Automated Classification of Haematopoietic Compartments in the Human Bone Marrow using Reservoir Computing.In Doctoral Consortium - Doctoral Consortium, (IJCCI 2013) ISBN Not Available, pages 8-18

@conference{doctoral consortium13,
author={Philipp Kainz. and Harald Burgsteiner. and Helmut Ahammer. and Martin Asslaber.},
title={Automated Classification of Haematopoietic Compartments in the Human Bone Marrow using Reservoir Computing},
booktitle={Doctoral Consortium - Doctoral Consortium, (IJCCI 2013)},
isbn={Not Available},


JO - Doctoral Consortium - Doctoral Consortium, (IJCCI 2013)
TI - Automated Classification of Haematopoietic Compartments in the Human Bone Marrow using Reservoir Computing
SN - Not Available
AU - Kainz, P.
AU - Burgsteiner, H.
AU - Ahammer, H.
AU - Asslaber, M.
PY - 2013
SP - 8
EP - 18
DO -

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