Portal del coneixement obert de la UPC http://upcommons.upc.edu/e-prints Aquesta és una còpia de la versió author's final draft d'un article publicat a la revista Journal of optics.

We present a spatio-temporal analysis of cell membrane fluctuations to distinguish healthy patients from patients with sickle cell disease. A video hologram containing either healthy red blood cells (h-RBCs) or sickle cell disease red blood cells (SCD-RBCs) was recorded using a low-cost, compact, 3D printed shearing interferometer. Reconstructions were created for each hologram frame (time steps), forming a spatio-temporal data cube. Features were extracted by computing the standard deviations and the mean of the height fluctuations over time and for every location on the cell membrane, resulting in two-dimensional standard deviation and mean maps, followed by taking the standard deviations of these maps. The optical flow algorithm was used to estimate the apparent motion fields between subsequent frames (reconstructions). The standard deviation of the magnitude of the optical flow vectors across all frames was then computed. In addition, seven morphological cell (spatial) features based on optical path length were extracted from the cells to further improve the classification accuracy. A random forest classifier was trained to perform cell identification to distinguish between SCD-RBCs and h-RBCs. To the best of our knowledge, this is the first report of machine learning assisted cell identification and diagnosis of sickle cell disease based on cell membrane fluctuations and morphology using both spatio-temporal and spatial analysis.

We propose an optical security method for object authentication using photoncounting encryption implemented with phase encoded QR codes. By combining the full phase double-random-phase encryption with photon-counting imaging method and applying an iterative Huffman coding technique, we are able to encrypt and compress an image containing primary information about the object. This data can then be stored inside of an optically phase encoded QR code for robust read out, decryption, and authentication. The optically encoded QR code is verified by examining the speckle signature of the optical masks using statistical analysis. Optical experimental results are presented to demonstrate the performance of the system. In addition, experiments with a commercial Smartphone to read the optically encoded QR code are presented. To the best of our knowledge, this is the first report on integrating photon-counting security with optically phase encoded QR codes.

We investigate a full-phase-based photon-counting double-random-phase encryption (PC-DRPE) method. A PC technique is applied during the encryption process, creating sparse images. The statistical distribution of the PC decrypted data for full-phase encoding and amplitude-phase encoding are derived, and their statistical parameters are used for authentication. The performance of the full-phase PC-DRPE is compared with the amplitude-based PC-DRPE method. The PC decrypted images make it difficult to visually authenticate the input image; however, advanced correlation filters can be used to authenticate the decrypted images given the correct keys. Initial computational simulations show that the full-phase PC-DRPE has the potential to require fewer photons for authentication than the amplitude-based PC-DRPE.

No abstract

We propose a low-cost, compact, and field-portable 3D printed holographic microscope for automated cell identification based on a common path shearing interferometer setup. Once a hologram is captured from the portable setup, a 3D reconstructed height profile of the cell is created. We extract several morphological cell features from the reconstructed 3D height profiles, including mean physical cell thickness, coefficient of variation, optical volume (OV) of the cell, projected area of the cell (PA), ratio of PA to OV, cell thickness kurtosis, cell thickness skewness, and the dry mass of the cell for identification using the random forest (RF) classifier. The 3D printed prototype can serve as a low-cost alternative for the developing world, where access to laboratory facilities for disease diagnosis are limited. Additionally, a cell phone sensor is used to capture the digital holograms. This enables the user to send the acquired holograms over the internet to a computational device located remotely for cellular identification and classification (analysis). The 3D printed system presented in this paper can be used as a low-cost, stable, and field-portable digital holographic microscope as well as an automated cell identification system. To the best of our knowledge, this is the first research paper presenting automatic cell identification using a low-cost 3D printed digital holographic microscopy setup based on common path shearing interferometry.

Conventional two-dimensional (2D) imaging systems that operate in the visible spectrum may perform poorly in environments under low light illumination. In this work, we present the potential of passive three-dimensional (3D) integral imaging (II) to perform 3D imaging of a scene under low light conditions in the visible spectrum and without the need for a photon counting or cooled CCD camera. Using dedicated algorithms, we demonstrate that the reconstructed 3D integral image is naturally optimum in a maximum likelihood sense in low light levels and in the presence of detector noise enabling object visualization in the scene. The conventional 2D imaging fails due to the limited number of photons. Using 3D imaging, we demonstrate the potential for 3D detection of objects behind occlusion in a photon-starved scene. To the best of our knowledge, this is the first report of experimentally using II sensing under low illumination conditions for 3D visualization and 3D object detection in the presence of obscurations with a conventional image sensor.

Abstract-Multidimensional optical imaging systems for information processing and visualization technologies have numerous applications in fields such as manufacturing, medical sciences, entertainment, robotics, surveillance, and defense. Among different three-dimensional (3D) imaging methods, integral imaging is a promising multiperspective sensing and display technique. Compared with other 3D imaging techniques, integral imaging can capture a scene using an incoherent light source and generate real 3D images for observation without any special viewing devices. This review paper describes passive multidimensional imaging systems combined with different integral imaging configurations. One example is the integral imaging based Multidimensional Optical Sensing and Imaging Systems (MOSIS), which can be used for 3D visualization, seeing through obscurations, material inspection, and object recognition from micro scales to long range imaging. This system utilizes many degrees of freedom such as time and space multiplexing, depth information, polarimetric, temporal, photon flux and multispectral information based on integral imaging to record and reconstruct the multidimensionally integrated scene. Image fusion may be used to integrate the multidimensional images obtained by polarimetric sensors, multispectral cameras, and various multiplexing techniques. The multidimensional images contain substantially more information compared with two-dimensional (2D) images or conventional 3D images. In addition, we present recent progress and applications of 3D integral imaging including human gesture recognition in the time domain, depth estimation, mid-wave infrared photon counting, 3D polarimetric imaging for object shape and material identification, dynamic integral imaging implemented with liquid crystal devices, and 3D endoscopy for healthcare applications.