Automatic classification of cells using morphological shape in peripheral blood images

A novel technique for automatic analysis and classification of cells in peripheral blood images is presented. The purposes of this research are to analyze and classify morphological shapes of mature red-blood cells and white-blood cells in peripheral blood images. We first, identify red-blood cells and white-blood cells in a blood image captured from CCD camera attached to microscope. Feature extraction is the second step. Finally blood cells are classified using back propagation neural network. Fifteen different classification clusters including normal cells are in red blood cell. However, there are five different normal categories in discrimination of white blood cells. In other words, the system can tell whether a given white cell belongs to a one of five normal classes or not. A novel segmentation method is presented for extraction of nucleus and cytoplasm which inherently posses valuable clues in white blood cell classification. Initially, seventy-six dimensions of a feature vector that includes UNL Fourier descriptor, shape, and color are considered in red-blood cell classification. While, thirty-eight dimensions of a feature vector are considered in red blood cell classification. Based on the proposed method, a prototype system has implemented and evaluated with various classification algorithms such as LVQ-3 (Learning Vector Quantization) and K-NN (K-nearest neighbor). The experiment results show that the proposed method out performs on blood cell classification compared with other alternatives.