Megakaryocytic features useful for the diagnosis of myeloproliferative disorders can be obtained by a novel unsupervised software analysis.

An unsupervised method for megakaryocyte detection and analysis is proposed, in order to validate supplementary tools which can be of help in supporting the pathologist in the classification of Philadelphia negative chronic myeloproliferative disorders with thrombocytosis. The experiment was conducted on high power magnification photomicrographs taken from hematoxylin-and-eosin 3 micrometer thick sections of formalin fixed, paraffin embedded bone marrow biopsies from patients with reactive thrombocytosis or chronic myeloproliferative disorders. Each megakaryocyte has been isolated in the photos through an image segmentation process, mainly based on mathematical morphology and wavelet analysis. A set of features (e.g. area, perimeter and fractal dimension of the cell and its nucleus, shape complexity via elliptic Fourier transform, and so on) is used to characterize the disorders and discriminate between essential thrombocythemia and idiopathic myelofibrosis. Features related to the general contour of the cell like cytoplasmic area and perimeter are good markers in distinguishing between normal or reactive and pathologic megakaryocytes while nuclear features and global circularity are helpful in the differential diagnosis between ET and prefibrotic IMF. The method proposed should be considered as a fast preprocessing tool for the diagnostic phase and its use can be extended to solve different object recognition problems.