Titlebook: Cellular Image Classification; Xiang Xu,Xingkun Wu,Feng Lin Book 2017 Springer International Publishing AG 2017 Biomedical imaging.Optical

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Optical Systems for Cellular Imaging,ation and classification in modern biological and medical engineering. Generally, implementation of physically classification of cells/bio-particles involve real-time recognition of cells upon acquisition of cellular image, followed up with optical cell manipulation such as branching (directly push

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Image Representation with Bag-of-Words,an be applied in video surveillance, remote sensing, web content analysis, biometrics, etc. Many successful models transform low-level descriptors into richer mid-level representations. Extracting mid-level features involves a sequence of interchangeable modules. However, they always consist of two

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Encoding Image Features,o kinds of rotation invariant descriptors to characterize HEp-2 cells that are highly discriminative and descriptive with respect to their staining patterns. We firstly propose a rotation invariant textural feature of pairwise local ternary patterns with spatial rotation invariant (PLTP-SRI). The in

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Defining Feature Space for Image Classification,ve model is built to adaptively characterize the CoDT feature space. We further exploit a more discriminant representation for the HEp-2 cell images based on the adaptive partitioned feature space, and then feed the representation into a linear SVM classifier to identify the staining patterns. Two b

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