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In the detection of myeloproliferative, the number of cells in each type ofbone marrow cells (BMC) is an important parameter for the evaluation. In thisstudy, we propose a new counting method, which also consists of three modulesincluding localization, segmentation and classification. The localization ofBMC is achieved from a color transformation enhanced BMC sample image andstepwise averaging method (SAM). In the nucleus segmentation, both SAM andOtsu's method will be applied to obtain a weighted threshold for segmenting thepatch into nucleus and non-nucleus. In the cytoplasm segmentation, a colorweakening transformation, an improved region growing method and the K-Meansalgorithm are used. The connected cells with BMC will be separated by themarker-controlled watershed algorithm. The features will be extracted for theclassification after the segmentation. In this study, the BMC are classifiedusing the SVM, Random Forest, Artificial Neural Networks, Adaboost and BayesianNetworks into five classes including one outlier, namely, neutrophilic splitgranulocyte, neutrophilic stab granulocyte, metarubricyte, mature lymphocytesand the outlier (all other cells not listed). Our experimental results showthat the best average recognition rate is 87.49% for the SVM.

Bone marrow cells detection: A technique for the microscopic image analysis