Automatic identification of abnormal blood smear images using color and morphology variation of RBCS and central pallor.

The anatomy of red blood cells (RBCs) in blood smear images plays an important role in the detection of several diseases. The automated image-based technique is fast and accurate for the analysis of blood cells morphology that can save time of both pathologists as well as that of patients. In this paper, we propose a novel method which segment and identify varied RBCs in a given blood smear images. In the proposed method, the central pallor and whole cell information are used, after using color processing followed by double thresholding of blood smear images. The shape and size variances of cells are calculated for the identification of abnormalities in peripheral blood smear images. We used cross-validation accuracy weighted probabilistic ensemble (CAWPE). It is a heterogeneous ensembling technique of nearly equivalent classifiers produced on averagely significant better classifiers (regarding errors and probability estimates) as compared to a wide range of potential parent classifiers. The proposed method is tested on 3 sets of images. The sets of images were prepared in a local government hospital by expert pathologists. Each image set has varied photographic conditions. The method was found accurate in term of results, closer to the ground truth. The average accuracy of the proposed method is 97% for the segmentation of single cells and 96% for overlapped cells. The variance (σ2) of accuracy is 3.5 and the deviation (σ) is 1.87.