Lensless imaging systems: a technological and clinical review for automated disease identification

Gregory Aschenbrenner; Peter M. Douglass; Timothy O'Connor; Saurabh Goswami; Kashif Usmani; Bahram Javidi

doi:10.1117/12.3013599

7 June 2024 Lensless imaging systems: a technological and clinical review for automated disease identification

Gregory Aschenbrenner, Peter M. Douglass, Timothy O'Connor, Saurabh Goswami, Kashif Usmani, Bahram Javidi

Proceedings Volume 13041, Three-Dimensional Imaging, Visualization, and Display 2024; 130410C (2024) https://doi.org/10.1117/12.3013599
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

We present an overview of previously reported Single Random Phase Encoding (SRPE) and Double Random Phase Encoding (DRPE) optical bio-sensing systems. In contrast to traditional imaging modalities that rely on lenses to capture and magnify subjects, SRPE and DRPE employ phase masks to modulate the light field emanating from an object. This modulation results in a pseudo-random optical signal to be received at the sensor, which is then classified by an appropriate classification algorithm. This lensless paradigm not only reduces the physical bulk and expense associated with optical components but provides wide field of view, and enhanced depth of field in comparison with lens-based imaging system. In biomedical imaging, the application of SPRE and DRPE systems has significant promise in the context of distinguishing between various types of red blood cells (RBCs) for disease diagnosis. Specifically, these imaging systems have demonstrated remarkable efficacy in identifying horse and cow RBCs, as well as differentiating between sickle cell-positive and negative RBCs with high accuracy and robustness to noise. The integration of Convolutional Neural Networks (CNNs), when trained directly on captured opto-biological signature (OBS) images show significant robustness to noise. Training a CNN on the Local Binary Patterns (LBP) of captured OBS images has shown not only improved classification performance but also maintained accuracy under conditions of significant data compression.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Gregory Aschenbrenner, Peter M. Douglass, Timothy O'Connor, Saurabh Goswami, Kashif Usmani, and Bahram Javidi "Lensless imaging systems: a technological and clinical review for automated disease identification", Proc. SPIE 13041, Three-Dimensional Imaging, Visualization, and Display 2024, 130410C (7 June 2024); https://doi.org/10.1117/12.3013599

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