Bone marrow cells detection: A technique for the microscopic image analysis
This work addresses a domain-specific problem in medical image analysis for hematology, but it is incremental as it combines existing methods for localization, segmentation, and classification.
The study tackled the problem of detecting and classifying bone marrow cells in microscopic images for evaluating myeloproliferative disorders, achieving a best average recognition rate of 87.49% using SVM.
In the detection of myeloproliferative, the number of cells in each type of bone marrow cells (BMC) is an important parameter for the evaluation. In this study, we propose a new counting method, which also consists of three modules including localization, segmentation and classification. The localization of BMC is achieved from a color transformation enhanced BMC sample image and stepwise averaging method (SAM). In the nucleus segmentation, both SAM and Otsu's method will be applied to obtain a weighted threshold for segmenting the patch into nucleus and non-nucleus. In the cytoplasm segmentation, a color weakening transformation, an improved region growing method and the K-Means algorithm are used. The connected cells with BMC will be separated by the marker-controlled watershed algorithm. The features will be extracted for the classification after the segmentation. In this study, the BMC are classified using the SVM, Random Forest, Artificial Neural Networks, Adaboost and Bayesian Networks into five classes including one outlier, namely, neutrophilic split granulocyte, neutrophilic stab granulocyte, metarubricyte, mature lymphocytes and the outlier (all other cells not listed). Our experimental results show that the best average recognition rate is 87.49% for the SVM.