Cell image classification based on ensemble features and random forest

An efficient white blood cell (WBC) image classification method using ensemble features and classification scheme is introduced. After WBC segmentation into the nucleus and cytoplasm, classification into five different categories is necessary for accurate disease diagnosis. First, from several experiments, it was proved that the nucleus alone is adequate for classifying WBCs without using the cytoplasm because the cytoplasm of some WBCs presents a very weak difference against the background and touches neighbouring WBCs and red blood cells. Secondly, it was proved that the random forest is a reasonable classifier for WBC classification compared to other classification methods, when using small training datasets.