This work describes an automatic method for discrimination in microphotographs between normal and pathological human megakaryocytes and between two kinds of disorders of these cells. A segmentation procedure has been developed, mainly based on mathematical morphology and wavelet transform, to isolate the cells. The features of each megakaryocyte (e.g. area, perimeter and tortuosity of the cell and its nucleus, and shape complexity via elliptic Fourier transform) are used by a regression tree procedure applied twice: the first time to find the set of normal megakaryocytes and the second to distinguish between the pathologies. The output of our classifier has been compared to the interpretation provided by the pathologists and the results show that 98.4% and 97.1% of normal and pathological cells, respectively, have testified an excellent classification. This study proposes a useful aid in supporting the specialist in the classification of megakaryocyte disorders. © 2008 Elsevier B.V. All rights reserved.

Ballarò, B., Florena, A., Franco, V., Tegolo, D., Tripodo, C., Valenti, C. (2008). An automated image analysis methodology for classifying megakaryocytes in chronic myeloproliferative disorders. MEDICAL IMAGE ANALYSIS, 12(6), 703-712 [10.1016/j.media.2008.04.001].

An automated image analysis methodology for classifying megakaryocytes in chronic myeloproliferative disorders

BALLARO', Benedetto;FLORENA, Ada Maria;FRANCO, Vito;TEGOLO, Domenico;TRIPODO, Claudio;VALENTI, Cesare Fabio
2008

Abstract

This work describes an automatic method for discrimination in microphotographs between normal and pathological human megakaryocytes and between two kinds of disorders of these cells. A segmentation procedure has been developed, mainly based on mathematical morphology and wavelet transform, to isolate the cells. The features of each megakaryocyte (e.g. area, perimeter and tortuosity of the cell and its nucleus, and shape complexity via elliptic Fourier transform) are used by a regression tree procedure applied twice: the first time to find the set of normal megakaryocytes and the second to distinguish between the pathologies. The output of our classifier has been compared to the interpretation provided by the pathologists and the results show that 98.4% and 97.1% of normal and pathological cells, respectively, have testified an excellent classification. This study proposes a useful aid in supporting the specialist in the classification of megakaryocyte disorders. © 2008 Elsevier B.V. All rights reserved.
Settore INF/01 - Informatica
http://www.sciencedirect.com/science/article/pii/S1361841508000431
Ballarò, B., Florena, A., Franco, V., Tegolo, D., Tripodo, C., Valenti, C. (2008). An automated image analysis methodology for classifying megakaryocytes in chronic myeloproliferative disorders. MEDICAL IMAGE ANALYSIS, 12(6), 703-712 [10.1016/j.media.2008.04.001].
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S1361841508000431-main.pdf

Solo gestori archvio

Dimensione 1.39 MB
Formato Adobe PDF
1.39 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/203651
Citazioni
  • ???jsp.display-item.citation.pmc??? 12
  • Scopus 30
  • ???jsp.display-item.citation.isi??? 24
social impact