Recognizing white blood cells with local image descriptors

Abstract Automatic and reliable classification of images of white blood cells is desirable for inexpensive, quick and accurate health diagnosis worldwide. In contrast to previous approaches which tend to rely on image segmentation and a careful choice of ad hoc (geometric) features, we explore the possibilities of local image descriptors, since they are a simple approach, they require no explicit segmentation, and yet they have been shown to be quite robust against background distraction in a number of visual tasks. Despite its potential, this methodology remains unexplored for this problem. In this work, images are therefore characterized with the well-known visual bag-of-words approach. Three keypoint detectors and five regular sampling strategies are studied and compared. The results indicate that the approach is encouraging, and that both the sparse keypoint detectors and the dense regular sampling strategies can perform reasonably well (mean accuracies of about 80% are obtained), and are competitive to segmentation-based approaches. Two of the main findings are as follows. First, for sparse points, the detector which localizes keypoints on the cell contour (oFAST) performs somehow better than the other two (SIFT and CenSurE). Second, interestingly, and partly contrary to our expectations, the regular sampling strategies including hierarchical spatial information, multi-resolution encoding, or foveal-like sampling, clearly outperform the two simpler uniform-sampling strategies considered. From the broader perspective of expert and intelligent systems, the relevance of the proposed approach is that, since it is very general and problem-agnostic, it makes unnecessary human expertise to be elicited in the form of explicit visual cues; only the labels of the cell type are required from human domain experts.