Mohendra Roy

Low-cost telemedicine device performing cell and particle size measurement based on lens-free shadow imaging technology

Recent advances in lens-free shadow imaging technology have enabled a new class of cell imaging platform, which is a suitable candidate for point-of-care facilities. In this paper, we firstly demonstrate a compact and low-cost telemedicine device providing automated cell and particle size measurement based on lens-free shadow imaging technology. Using the generated shadow (or diffraction) patterns, the proposed approach can detect and measure the sizes of more than several hundreds of micro-objects simultaneously within a single digital image frame. In practical experiments, we defined four types of shadow parameters extracted from each micro-object shadow pattern, and found that a specific shadow parameter (peak-to-peak distance, PPD) demonstrated a linear relationship with the actual micro-object sizes. By using this information, a new algorithm suitable for operation on both a personal computer (PC) and a cell phone was also developed, providing automated size detection of poly-styrenemicro-beads and biological cells such as red blood cells, MCF-7, HepG2, and HeLa. Results from the proposed device were compared with those of a conventional optical microscope, demonstrating good agreement between two approaches. In contrast to other existing cell and particle size measurement approaches, such as Coulter counter, flow-cytometer, particle-size analyzer, and optical microscope, this device can provide accurate cell and particle size information with a 2 µm maximum resolution, at almost no cost (less than 100 USD), within a compact instrumentation size (9.3×9.0×9.0 cm(3)), and in a rapid manner (within 1 min). The proposed lens-free automated particle and cell size measurement device, based on shadow imaging technology, can be utilized as a powerful tool for many cell and particle handling procedures, including environmental, pharmaceutical, biological, and clinical applications.

Capillary Flow in PDMS Cylindrical Microfluidic Channel Using 3-D Printed Mold

This letter investigates the capillary filling in polydimethylsiloxane (PDMS) microchannels using 3-D printed molds to produce channels with circular cross sections. The circular cross sections are prevalent in biology and anatomy, yet they cannot readily be mimicked with existing soft-lithography techniques. The molds are printed directly from computer-aided design files, making rapid prototyping of microfluidic devices possible in hours, demonstrating microscale features in PDMS channels. The PDMS channels with variable channel diameters ranging from 200 to 1000 μm in a single device that are obtained from four different 3-D printers are compared in terms of capillary flow. Technology limits, including surface roughness and resolution, are also characterized, and estimated as an equivalent contact angle which is a fit parameter dependent on the 3-D printer.

A review of recent progress in lens-free imaging and sensing.

Recently, lens-free imaging has evolved as an alternative imaging technology. The key advantages of this technology, including simplicity, compactness, low cost, and flexibility of integration with other components, have facilitated the realization of many innovative applications, especially, in the fields of the on-chip lens-free imaging and sensing. In this review, we discuss the development of lens-free imaging, from theory to applications. This article includes the working principle of lens-free digital inline holography (DIH) with coherent and semi coherent light, on-chip lens-free fluorescence imaging and sensing, lens-free on-chip tomography, lens-free on-chip gigapixel nanoscopy, detection of nanoparticles using on-chip microscopy, wide field microscopy, and lens-free shadow image based point-of-care systems. Additionally, this review also discusses the lens-free fluorescent imaging and its dependence on structure and optical design, the advantage of using the compact lens-free driven equilibrium Fourier transform (DEFT) resolved imaging technique for on-chip tomography, the pixel super-resolved algorithm for gigapixel imaging, and the lens-free technology for point-of-care applications. All these low-cost, compact, and fast-processing lens-free imaging and sensing techniques may play a crucial role especially in the fields of environmental, pharmaceutical, biological, and clinical applications of the resource-limited settings.

Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology

Lens-free imaging technology has been extensively used recently for microparticle and biological cell analysis because of its high throughput, low cost, and simple and compact arrangement. However, this technology still lacks a dedicated and automated detection system. In this paper, we describe a custom-developed automated micro-object detection method for a lens-free imaging system. In our previous work (Roy et al.), we developed a lens-free imaging system using low-cost components. This system was used to generate and capture the diffraction patterns of micro-objects and a global threshold was used to locate the diffraction patterns. In this work we used the same setup to develop an improved automated detection and analysis algorithm based on adaptive threshold and clustering of signals. For this purpose images from the lens-free system were then used to understand the features and characteristics of the diffraction patterns of several types of samples. On the basis of this information, we custom-developed an automated algorithm for the lens-free imaging system. Next, all the lens-free images were processed using this custom-developed automated algorithm. The performance of this approach was evaluated by comparing the counting results with standard optical microscope results. We evaluated the counting results for polystyrene microbeads, red blood cells, HepG2, HeLa, and MCF7 cells lines. The comparison shows good agreement between the systems, with a correlation coefficient of 0.91 and linearity slope of 0.877. We also evaluated the automated size profiles of the microparticle samples. This Wi-Fi-enabled lens-free imaging system, along with the dedicated software, possesses great potential for telemedicine applications in resource-limited settings.

Smartphone based automated microparticle analysis system

Cell and microparticle analysis is one of the major task in all pathological labs. The concentration profile, such as red blood cell (RBC), white blood cell (WBC), platelet concentration are the key parameters for early diagnosis of many diseases. However in most laboratories, especially in resource limited settings, this diagnosis are done manually using conventional optical microscope. This manual process is slow and prone to subjective error. In this paper we demonstrate a smartphone based automated cell detection and counting system. The system is based on the lens-free imaging method, which is a compact facility made up of inexpensive components. We evaluated the performance of the system as well as smartphone algorithm by evaluating the concentration of the micro particles of different sizes. This results were then compared with the conventional optical microscope result. The correlation coefficients of this comparisons shows a great agreement between the two modalities. This kind of compact system along with the wireless facility would be a good point of care facility in resource limited settings.

High-throughput and real-time microalgae monitoring platform using lens-free shadow imaging system (LSIS)

Floc size analysis is one of the major processes in bio-flocculant efficiency determination, which is critical for microalgae harvesting. Till now the flocculation analysis has been performed by using photospectrometry. In this conventional method, optical density of a sample is measured in a time interval by collecting the sample from a fixed point of the container, up to ~5 hrs of observation period. This time consuming process may not guarantee the viability of the algae, which limits the efficient harvesting. To address this issue, we introduce a real time flocculation monitoring system based on the lens-free shadow imaging technique (LSIT). This simple, fast, and cost-effective system automatically analyzes the thousands of single microalgae all in parallel and monitors their flocculation by using a custom developed algorithm. We evaluate the performance of this approach by comparing the results with the standard method, showing a good agreement between two modalities.

Lens-free automated cell detection system for telemedicine application

Cell and micro particle analysis is one of the major tasks in many clinical labs culturing cells. The functional profiles of cells such as concentration, size, morphology and viability are the key parameters for early diagnosis of many diseases. However in most labs, especially in the resource limited settings, these processes are done fully manually using conventional optical microscopes, which are slow and prone to subjective errors. Recently the lens-free shadow imaging system has been introduced as an alternative for the conventional optical microscopy. In this paper we demonstrate a dedicated algorithm for lens-free shadow imaging system, which was implemented as a smartphone application. This android application can wirelessly acquire the images from the lens-free system and process them automatically. The results can be obtained locally or sent to a remote expert for further analysis. The feasibility of this system was evaluated by comparing the results with the standard optical microscope.

Underwater multispectral imaging system for environmental monitoring

In this article, we present a novel underwater monitoring system based on the hyperspectral imager. This system consists of the commercial hyperspectral camera and white-colored LED lighting. We evaluated the performance of the proposed hyperspectral imaging system by conducting the small scale experiments to identify the difference of two different oil samples. For the classification between oil samples and seawater, we analyzed the spectrum profiles of oils and seawater. Though that, we can clearly not only distinguish the oil samples from seawater but also distinguish the bunker C oil from lubricating oil. This result shows the feasibility to application of our proposed setup for the underwater oil spill monitoring system.

An automated cell detection algorithm for lensfree shadow imaging platform

Here we propose an automated cell detection and counting algorithm for lens-free shadow imaging platform. This approach is based on the adaptive thresholding algorithm. The spatial variation of the threshold values is determined by the moving window technique. The shadow images of the Red Blood Cells(RBCs) are well detected and specified by the algorithm. The cell counting results from the algorithm for different concentration of blood cells are verified with the established cell counting results.