White blood cell detection is one of the most basic and key steps in the automatic recognition system of white blood cells in microscopic blood images. Its accuracy and stability greatly affect the operating speed and recognition accuracy of the whole system. But there are only a few methods available for cell detection or segmentation due to the complexity of the microscopic images. This paper focuses on this issue. Based on the detailed analysis of the existing two methods--threshold segmentation followed by mathematical morphology (TSMM), and the fuzzy logic method--a new detection algorithm (NDA) based on fuzzy cellular neural networks is proposed. NDA combines the advantages of TSMM and the fuzzy logic method, and overcomes their drawbacks. With NDA, we can detect almost all white blood cells, and the contour of each detected cell is nearly complete. Its adaptability is strong and the running speed is expected to be comparatively high due to the easy hardware implementation of FCN. Experimental results show good performance.
Recently, compressive hyperspectral imaging (CHI) has received increasing interests, which can recover a large range of scenes with a small number of sensors via compressed sensing (CS) theory. However, most of the available CHI methods separate and vectorize hyperspectral cubes into spatial and spectral vectors, which will result in heavy computational and storage burden in the recovery. Moreover, the complexity of real scene makes the sparsifying difficult and thus requires more measurements to achieve accurate recovery. In this paper, these two issues are addressed, and a new CHI approach via sparse tensors and nonlinear CS (NCS) is advanced for accurate maintenance of image structure with limited number of sensors. Based on a multidimensional multiplexing (MDMP) CS scheme, the observed measurements are denoted as tensors and a nonlinear sparse tensor coding is adopted, to develop a new tensor-NCS (T-NCS) algorithm for noniterative recovery of hyperspectral images. Moreover, two recovery schemes are advanced for T-NCS, including example-aided and self-learning CHI approaches. Finally, some experiments are performed on three real hyperspectral data sets to investigate the performance of T-NCS, and the results demonstrate its efficiency and superiority to the counterparts.Index Terms-Compressive hyperspectral imaging (CHI), joint spatial-spectral, multidimensional multiplexing (MDMP), nonlinear compressed sensing (NCS), sparse tensor.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.