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      Portable lensless wide-field microscopy imaging platform based on digital inline holography and multi-frame pixel super-resolution

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          Abstract

          In this paper, an irregular displacement-based lensless wide-field microscopy imaging platform is presented by combining digital in-line holography and computational pixel super-resolution using multi-frame processing. The samples are illuminated by a nearly coherent illumination system, where the hologram shadows are projected into a complementary metal-oxide semiconductor-based imaging sensor. To increase the resolution, a multi-frame pixel resolution approach is employed to produce a single holographic image from multiple frame observations of the scene, with small planar displacements. Displacements are resolved by a hybrid approach: (i) alignment of the LR images by a fast feature-based registration method, and (ii) fine adjustment of the sub-pixel information using a continuous optimization approach designed to find the global optimum solution. Numerical method for phase-retrieval is applied to decode the signal and reconstruct the morphological details of the analyzed sample. The presented approach was evaluated with various biological samples including sperm and platelets, whose dimensions are in the order of a few microns. The obtained results demonstrate a spatial resolution of 1.55 µm on a field-of-view of ≈30 mm 2.

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          A new microscopic principle.

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            Scalable Nearest Neighbor Algorithms for High Dimensional Data.

            For many computer vision and machine learning problems, large training sets are key for good performance. However, the most computationally expensive part of many computer vision and machine learning algorithms consists of finding nearest neighbor matches to high dimensional vectors that represent the training data. We propose new algorithms for approximate nearest neighbor matching and evaluate and compare them with previous algorithms. For matching high dimensional features, we find two algorithms to be the most efficient: the randomized k-d forest and a new algorithm proposed in this paper, the priority search k-means tree. We also propose a new algorithm for matching binary features by searching multiple hierarchical clustering trees and show it outperforms methods typically used in the literature. We show that the optimal nearest neighbor algorithm and its parameters depend on the data set characteristics and describe an automated configuration procedure for finding the best algorithm to search a particular data set. In order to scale to very large data sets that would otherwise not fit in the memory of a single machine, we propose a distributed nearest neighbor matching framework that can be used with any of the algorithms described in the paper. All this research has been released as an open source library called fast library for approximate nearest neighbors (FLANN), which has been incorporated into OpenCV and is now one of the most popular libraries for nearest neighbor matching.
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              Ant colony optimization for continuous domains

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                Author and article information

                Journal
                101610753
                41525
                Light Sci Appl
                Light Sci Appl
                Light, science & applications
                2095-5545
                2047-7538
                8 December 2017
                23 October 2015
                2015
                13 April 2018
                : 4
                : e346
                Affiliations
                [1 ]Demirci Bio-Acoustic-MEMS in Medicine (BAMM) Laboratory, Division of Biomedical Engineering, Division of Renal Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
                [2 ]Demirci Bio-Acoustic-MEMS in Medicine (BAMM) Laboratory, Division of Infectious Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
                [3 ]INCoD - National Brazilian Institute for Digital Convergence/LAPIX, Image Processing and Computer Graphics Lab, Federal University of Santa Catarina, Brazil
                [4 ]Demirci Bio-Acoustic-MEMS in Medicine (BAMM) Laboratory, Stanford University School of Medicine, Canary Center at Stanford for Cancer Early Detection, Palo Alto, CA, USA
                [6 ]4VisionLab, Master in Applied Computing, University of Itajaí Valley, Brazil
                [7 ]CERTI Foundation, Florianopolis, Brazil
                Author notes
                Correspondence: U Demirci. utkan@ 123456stanford.edu
                [5]

                Present Address: Department of Biomedical Engineering Faculty of Engineering, Başkent University, Ankara, Turkey

                Article
                NIHMS925826
                10.1038/lsa.2015.119
                5898403
                29657866
                720e4e16-c439-4ae9-9cb3-9bf928740725

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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                Categories
                Article

                digital in-line holography,image registration,lensless imaging,point-of-care platform,pixel super-resolution

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