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RESEARCH PRODUCT
An automated image analysis methodology for classifying megakaryocytes in chronic myeloproliferative disorders
Domenico TegoloCesare ValentiAda Maria FlorenaBenedetto BallaròClaudio TripodoVito Francosubject
Decision treeReproducibility of ResultHealth InformaticsMathematical morphologySensitivity and SpecificityWavelet analysiPattern Recognition Automatedsymbols.namesakeWaveletMegakaryocyteMegakaryocyteArtificial IntelligenceImage Interpretation Computer-AssistedmedicineAnimalsHumansRadiology Nuclear Medicine and imagingComputer visionSegmentationMyeloproliferative DisorderCells Cultured1707MathematicsHealth InformaticMyeloproliferative DisordersSettore INF/01 - InformaticaRadiological and Ultrasound TechnologyAnimalbusiness.industryMorphometryReproducibility of ResultsWavelet transformPattern recognitionAutomatic classification; Elliptic Fourier transform; Morphometry; Wavelet analysis; Animals; Cells Cultured; Humans; Image Enhancement; Image Interpretation Computer-Assisted; Megakaryocytes; Myeloproliferative Disorders; Pattern Recognition Automated; Reproducibility of Results; Sensitivity and Specificity; Algorithms; Artificial Intelligence; Computer Graphics and Computer-Aided Design; 1707; Radiology Nuclear Medicine and Imaging; Health Informatics; Radiological and Ultrasound TechnologyImage EnhancementComputer Graphics and Computer-Aided DesignAlgorithmFourier transformmedicine.anatomical_structuresymbolsAutomatic classificationElliptic Fourier transformComputer Vision and Pattern RecognitionArtificial intelligencebusinessMegakaryocytesClassifier (UML)AlgorithmsHumandescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2008-01-01 | Medical Image Analysis |