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Digital holography with deep learning for algae identification and classification
(2024-01-01)
Ruttanasirawit, Chinnaphat
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Plaipichit, Suwan
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Thongsuwan, Setthanun
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Thonglim, Pachara
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Phunpruch, Saranya
Recently, the characterization of marine objects, populations and biophysical interactions have become crucial within the research community. In this study, we leverage digital holographic imaging systems and deep learning networks to classify three distinct types of micro-algae: Chlamydomonas, Scenedesmus armatus, and Scenedesmus_sp-L. We employed reconstructed digital holographic images and deep learning to identify the results from both approaches. The integration of holographic imaging holds promises in replacing expensive characterization systems like AFM, x-ray diffraction, and Raman spectroscopy, offering a more costeffective solution. In our system, we utilize in-line microscopic digital holographic imaging to record and reconstruct images of the algae specimens. An essential advantage of holographic techniques is that they do not require intact samples of the specimens for effective object identification. To further enhance the process, we combined deep learning algorithms with holographic imaging, capitalizing on the advanced computers. This combination enables highly effective characterizing and classification of different types of algae. These innovative approaches pave the way for exciting advancement in marine research and monitoring.
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Gender classification using convolutional neural networks based on fingerprint analysis with in-line digital holography
(2024-01-01)
Thonglim, Pachara
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Thongsuwan, Setthanun
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Buranasiri, Prathan
Gender classification has found applications in various fields, including criminology, biometrics, and surveillance. Historically, different methods for gender identification have been employed, such as analyzing hand shape, gait, iris, and facial features. Fingerprints, being unique to each individual, are formed based on the control of multiple genes on chromosomes. After the 24th embryonic week, a person's fingerprint pattern remains unchanged throughout their life. Numerous studies have explored the use of fingerprints for various purposes, such as investigating mental characteristics, characteristics of hereditary diseases, and cancer screening. This paper focuses on studying fingerprints for the identification and classification of human gender through fingerprint analysis using in-line digital holography. The deep learning model constructed for this study includes two convolutional layers, pooling layers, and dense layers. It was trained on a biometric fingerprint database containing 6,000 images, achieving an impressive 99% accuracy. The model was then utilized to classify human gender based on fingerprint analysis, and its accuracy was tested using fingerprint images obtained through Inline Digital Holography (IDH) technique, achieving an 83% accuracy. The performance of the proposed system demonstrates that fingerprints contain vital features for effectively discriminating a person's gender.
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Classification of Fingerprints to Study the Relationship Between Fingerprint Types and Disease Lesions Using the Mach-Zehnder Interferometer Technique - Phase I: Classification of Fingerprints
(2024-01-01)
Thonglim, Pachara
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Usman, Abdullahi
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Bhatranand, Apichai
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Buranasiri, Prathan
The investigation of fingerprints was performed using the Mach-Zehnder interferometer technique to classify fingerprint types. Fingerprint type images have been applied to predict the probability of disease in humans for the next phase.
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Classification of Fingerprints to Study the Relationship Between Fingerprint Types and Disease Lesions Using the Mach-Zehnder Interferometer Technique - Phase I: Classification of Fingerprints
(2024-01-01)
Thonglim, Pachara
;
Usman, Abdullahi
;
Bhatranand, Apichai
;
Buranasiri, Prathan
The investigation of fingerprints was performed using the Mach-Zehnder interferometer technique to classify fingerprint types. Fingerprint type images have been applied to predict the probability of disease in humans for the next phase.
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Polarization phase shifting for thickness measurement of NiO material transport layer using Sagnac interferometer
(2023-01-01)
Usman, Abdullahi
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Thonglim, Pachara
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Bhatranand, Apichai
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Pimanpang, Samuk
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Buranasiri, Prathan
The optical interferometric technique with polarization phase shifting has been realized as one of the most important techniques for optical non-contact measurements. However, the measurement of thin film thickness using field emission scanning electron microscopy (FE-SEM) or scanning electron microscopy (SEM) has been inconvenient due to the high cost of maintenance. This research aims to measure the thickness of the Hole Transport Material Nickel (II) oxide (NiO) layer deposited on a glass substrate (NiO/FTO layer) using phase shifting in a Sagnac interferometer. In the experimental setup, the signal is split into the FTO reference arm and the NiO/FTO sample arm using a nonpolarizing beam splitter. The split signals are then detected through a balanced photodetector. By analyzing the signal intensities at polarization settings ranging from 0° to 90°, the phase shift and thickness of the NiO layer can be determined. In this study, a NiO thickness value of 281.64 nm was successfully achieved. To evaluate the accuracy of the proposed measurement method, the percentage error between the proposed technique and the conventional SEM method was computed. The percentage error was found to be 0.23%. These results indicate that the proposed setup holds promise as a cost-effective alternative to SEM for measuring thin film thickness.
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The Moina Gender Investigation using In-Line Digital Holographic Microscopy
(2023-01-01)
Voochaiyaphum, Bunyarit
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Thongsuwan, Setthanun
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Ruttanasirawit, Chinnapat
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Plaipichit, Suwan
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Thonglim, Pachara
The investigation of Moina was performed using in-line DHM. The reconstructed images of Moinas have been applied for investigation their surface area. Then the surface area has been used for gender investigation.
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Optimized uptake time of18F-THK5351 PET/CT in normal thai brain
(2019-07-01)
Thonglim, Pachara
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Chotipanich, Chanisa
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Buranasiri, Prathan
This study investigated18F-THK5351 positron emission tomography-computed tomography (PET/ CT) images to determine the optimized imaging time of radiopharmaceutical PET/CT in normal Thai population. Seventeen volunteers without any neurological or psychiatric illnesses, who showed no abnormalities upon neurological examination and the standardized uptake value ratio, were included. All subjects were diagnosed using18F-THK5351 PET/CT and 3.0 Tesla magnetic resonance imaging (MRI). THK5351 PET/CT was operated on the co-registered MRI to draw a region of interest (ROI). A digital imaging and communications in a medical file were converted to a .img file. Next, the image file was rendered from discrete time to continuous time for plotting graphs. The ROI positions and rendered file were then used to plot a graph showing the relationship between the radiopharmaceutical uptake quantity and the interval time to determine the optimized uptake time of THK5351 PET/CT for the brains of normal Thai population, which was consequently 40–60 min.
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Surface of quantitative uptake value of radiopharmaceutical PET/CT in normal Thai population
(2019-01-01)
Thonglim, Pachara
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Chotipanich, Chanisa
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Iamsa-Art, Chanidapa
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Jantarato, Attapon
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Buranasiri, Prathan
The objective of this study was to the created surface of quantitative uptake value with radioactive tracer PET/CT in normal Thai brain. The surface was generated from the matrix of quantitative uptake value by MATLAB software. Data of PET/CT image was modified to High dynamic range imaging file format by MRI convert and merge together with ana75-2.mat, in this step surfacedata.mat was obtained. The surface data was taken to create a surface of quantitative uptake value to observing the distribution of radiopharmaceuticals in the region of interest.
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The optimized time of 18F-THK5351 PET/CT in normal Thai population
(2018-01-01)
Thonglim, Pachara
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Sungkarat, Witaya
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Chotipanich, Chanisa
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Somphonsane, Ratchanok
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Phoemphoonthanyakit, Suwaphit
In this paper, we investigated 18F-THK5351 PET/CT image of normal Thai population for the optimized time of radioactive tracer PET/CT. Twenty-five volunteers without neurological or psychiatric illnesses and all of them have no abnormalities detected on neurologic examination. All subject were underdiagnosed on 18F-THK5351 PET/CT and 3.0 Tesla MR imaging. THK5351 PET/CT were operated on the co-registered MRI comprised for drawing an ROI. The DICOM file was converted to.img file,.hdr file and then merge with each other for obtaining an fmridata.mat. Position of ROI and fmridata.mat were used to plot graph showing the relationship between quantitative uptake with the frame of image. The optimized time of 18F-THK5351 PET/CT in normal Thai population is 50 to 70 minutes.
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