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RISS 인기검색어
- 검색키워드
- 저자 : Jaehong Aum (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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Live Acquisition of Internal Fingerprint With Automated Detection of Subsurface Layers Using OCT
Jaehong Aum,Ji-Hyun Kim,Jichai Jeong IEEE 2016 Photonics Technology Letters Vol.28 No.2
<P>Recently, optical coherence tomography (OCT) was introduced as a novel fingerprint scanning technique. This approach is resistant to fake fingerprint attacks, and is robust against poor fingertip conditions, i.e., wet or stained fingers. The method proceeds by scanning a fingertip in three dimensions and capturing a fingerprint from the subsurface layer. Although OCT has the potential to be widely used as a new standard in fingerprint scanning, it is hindered by its low scanning speed and the lack of computing power available for reproducing raw OCT data into images in real time; for instance, this process can take minutes to obtain barely one fingerprint image. In this letter, we introduce a novel spectral-domain OCT-based 3-D fingerprint scanner that is capable of obtaining an internal fingerprint image within 2 s. In order to obtain internal fingerprint images from raw OCT data in real time, we used graphics processing unit for massive parallel computation, along with an automated method for extracting the internal fingerprint from a 3-D scan of a fingertip. In addition, the robustness of the OCT fingerprint scanner was established by comparing fingerprint images-of wet, stained, and damaged fingertips-that were obtained by the OCT system with those from a commercially available optical fingerprint scanner.</P>
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Aum, Jaehong,Kim, Ji-hyun,Jeong, Jichai The Optical Society 2015 Applied optics Vol.54 No.13
<P>Non-local means (NLM) filter is one of the state-of-the-art denoising filters. It exploits the presence of similar features in an image and averages those similar features to remove noise. However, a conventional NLM filter shows somewhat inferior performance of noise reduction around edges, suffering from low efficiency of collecting similar features to be averaged. In order to overcome this phenomenon, we propose a NLM filter with double Gaussian anisotropic kernels as a substitute for the conventional homogeneous kernel to effectively remove noise from OCT images corrupted by speckle noise. The proposed filter was evaluated by comparing with various denoising filters such as conventional NLM filter, median filter, bilateral filter, and Wiener filter. The fingertip OCT images, which were processed with the different denoising filters, indicated that the proposed NLM filter provides superior denoising performance, among the filters in terms of the contrast-to-noise ratio (CNR), the equivalent number of looks (ENL), and the speckle suppression index (SSI). A human retina OCT image was also used to compare and show the performances of noise reduction among different filters. In addition, the denoising performance with the proposed NLM filter was also investigated in the synthetic images for fair comparison among the filters by calculating the peak signal-to-noise ratio (PSNR). The proposed NLM filter outperformed the conventional NLM filter as well as the other filters. (C) 2015 Optical Society of America</P>
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Jaehong Aum,Ji-hyun Kim,Sunghee Dong,Jichai Jeong 한국광학회 2018 Current Optics and Photonics Vol.2 No.5
We have developed a high-performance signal-processing and image-rendering heterogeneous computation system for optical coherence tomography (OCT) on mobile processor. In this paper, we reveal it by demonstrating real-time OCT image processing using a Snapdragon 800 mobile processor, with the introduction of a heterogeneous image visualization architecture (HIVA) to accelerate the signal-processing and image-visualization procedures. HIVA has been designed to maximize the computational performances of a mobile processor by using a native language compiler, which targets mobile processor, to directly access mobile-processor computing resources and the open computing language (OpenCL) for heterogeneous computation. The developed mobile image processing platform requires only 25 ms to produce an OCT image from 512 × 1024 OCT data. This is 617 times faster than the naïve approach without HIVA, which requires more than 15 s. The developed platform can produce 40 OCT images per second, to facilitate real-time mobile OCT image visualization. We believe this study would facilitate the development of portable diagnostic image visualization with medical imaging modality, which requires computationally expensive procedures, using a mobile processor.
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