The visual analysis of peripheral blood samples is an important test in the procedures for the diagnosis of leukemia. Automated systems based on artificial vision methods can speed up this operation and increase the accuracy and homogeneity of the response also in telemedicine applications. Unfortunately, there are not available public image datasets to test and compare such algorithms. In this paper, we propose a new public dataset of blood samples, specifically designed for the evaluation and the comparison of algorithms for segmentation and classification. For each image in the dataset, the classification of the cells is given, as well as a specific set of figures of merits to fairly compare the performances of different algorithms. This initiative aims to offer a new test tool to the image processing and pattern matching communities, direct to stimulating new studies in this important field of research.

ALL-IDB : the acute lymphoblastic leukemia image database for image processing / R. Donida Labati, V. Piuri, F. Scotti (PROCEEDINGS - INTERNATIONAL CONFERENCE ON IMAGE PROCESSING). - In: ICIP[s.l] : IEEE, 2011 Sep. - ISBN 9781457713033. - pp. 2045-2048 (( Intervento presentato al 18. convegno International Conference on Image Processing tenutosi a Brussels nel 2011 [10.1109/ICIP.2011.6115881].

ALL-IDB : the acute lymphoblastic leukemia image database for image processing

R. Donida Labati;V. Piuri;F. Scotti
2011-09

Abstract

The visual analysis of peripheral blood samples is an important test in the procedures for the diagnosis of leukemia. Automated systems based on artificial vision methods can speed up this operation and increase the accuracy and homogeneity of the response also in telemedicine applications. Unfortunately, there are not available public image datasets to test and compare such algorithms. In this paper, we propose a new public dataset of blood samples, specifically designed for the evaluation and the comparison of algorithms for segmentation and classification. For each image in the dataset, the classification of the cells is given, as well as a specific set of figures of merits to fairly compare the performances of different algorithms. This initiative aims to offer a new test tool to the image processing and pattern matching communities, direct to stimulating new studies in this important field of research.
Acute lymphoblastic leukemia; public image database; image segmentation; image classification
Settore INF/01 - Informatica
Institute of Electrical and Electronics Engineers (IEEE)
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/168333
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