Using Adapted JSEG Algorithm with Fuzzy C Mean for Segmentation and Counting of White Blood Cell and Nucleus Images

In this paper, an adapted unsupervised segmentation approach is proposed to fully automate the segmentation of white blood cells and their nuclei. Segmentation and counting of white blood cells from microscope images are challenging tasks, especially the segmentation of white blood cell nuclei from the cell wall and cytoplasm because of the need to consider intra-class variations arising from non-uniform illumination, stage of maturity, colour distribution, scale, and overlapped cells with other components of the blood. We propose the use of the JSEG algorithm based on colour-texture distribution, and adapted region growing using the Fuzzy C Mean to segment and count WBCs and their nuclei. First, colours in the image are quantized to represent differentiated regions in the image. Image pixel colours are then replaced by their corresponding colour class labels, thus forming a class-map of the image. A criterion for “good” segmentation using this spatial class-map is applied to local image windows resulting in J-images, which can be segmented using adapted region growing based on the Fuzzy C Mean algorithm. The Fuzzy C Mean is also employed for counting each white blood cell in images. Performance of the proposed method is evaluated on a combined dataset of 10 types of white blood cell with 200 digital images collected from 3 datasets. It achieves an average segmentation accuracy using four indices for WBC segmentation: jaccard distance, rand index, boundary detection error and F-value indices, 0.002, 0.93, 10.11, 0.93, respectively, while for WBC nuclei segmentation, it achieves indices values, 0.015, 0.88, 14.11, 0.90, respectively. The segmentation accuracy of the proposed method is also compared and benchmarked with the other existing techniques for segmentation of white blood cells over the same datasets and the results show that the proposed method is superior to other approaches.