Accurate Blood Cells Segmentation through Intuitionistic Fuzzy Set Threshold

Counting and analysis of blood cells allow the evaluation and diagnosis of a vast number of diseases. In particular, the identification of white blood cells and red blood cells is a topic of great interest to hematologists. Nowadays the observation of blood samples is still performed manually by skilled operators. This task is tedious, lengthy and repetitive, and the results accuracy heavily depends on the operator skills. Differently, the automated analysis by computer is performed quickly, requires only one image of the blood sample and provides precise results. One of the major steps on image analysis is segmentation, that subdivides images into meaningful parts. Thresholding is one of the most used technique and subdivides the image pixels on the basis of their intensity grey levels. There are many crisp techniques for calculating the threshold value of an image. Recently many intuitionistic fuzzy methods have been proposed to determine the optimal threshold value, showing better results for segmentation but not even better computational performance. In this paper we propose an intuitionistic fuzzy set approach for optimal threshold selection based on computations performed on the histogram. This method is then extended in order to perform multiple thresholds and in order to take into account possible local variations on the image. The proposed approach has been tested on peripheral blood images, in order to subdivide the various image components, showing excellent performance both for segmentation both in terms of speed.