Leukocyte image segmentation by visual attention and extreme learning machine

This paper presents a fast and simple framework for leukocyte image segmentation by learning with extreme learning machine (ELM) and sampling via simulating visual system. In sampling stage, visual attention and the effect of microsaccades in fixation are simulated. The high gradient pixels in fixation regions are sampled to group training set. We designed an automatic sampling process for leukocyte image according to the staining knowledge of blood smears. In learning stage, ELM classifier is trained online to simulate visual neuron system and then extracts pixels of object from image. The ELM-based segmentation is fully automatic by the proposed framework, which could find efficient samples actively, train the classification model in real time and almost no parameter adjusted. Experimental results demonstrated the new method could extract entire leukocyte from complex scenes, has equivalent performance compared to the SVM-based method and exceeds the marker-controlled watershed algorithm.