A multi-scale cell segmentation method for detecting hematological disorders

Cell segmentation, conducted on a microscopic image that contains blood cells, plays a crucial role for the detection of various hematological disorders. Existing methods often yield inferior performance in the presence of elongated and irregularly-shaped cells, as well as to those adjacent cells with partial overlapping among themselves. To address these issues, a novel multi-scale cell segmentation (MCS) method is proposed in this paper that involves three scales, denoted by coarse, medium, and fine, for demonstrating the effectiveness and efficacy of the proposed multiscale approach. It has been shown in our work that noise and insignificant cell structures can be effectively suppressed at the coarse scale. Consequently, those elongated and irregularly-shaped cells are more accurately identified. Furthermore, our developed coarse-to-fine scale tracing algorithm, performed to trace each identified cell, improves segmentation accuracy. To solve the problem of cell overlapping, an effective cell identification technique is developed for extracting cell contours for each scale. Extensive experimental results obtained from three benchmark datasets have shown that the proposed MCS outperforms the state-of-the-art methods by a large margin.