An automated image analysis methodology for classifying megakaryocytes in chronic myeloproliferative disorders.
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.
Ballarò
Benedetto
B
Dipartimento di Matematica e Applicazioni, Università degli Studi di Palermo, Italy. ballaro@math.unipa.it
Florena
Ada Maria
AM
Franco
Vito
V
Tegolo
Domenico
D
Tripodo
Claudio
C
Valenti
Cesare
C
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D002478:Cells, Cultured; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008533:Megakaryocytes / Q000145:classification* / Q000473:pathology*; D009196:Myeloproliferative Disorders / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity