Contrast-enhanced dual mode imaging: photoacoustic imaging plus more

박성조,정운상,이승현,이동현,김철홍 대한의용생체공학회 2017 Biomedical Engineering Letters (BMEL) Vol.7 No.2

Conventional biomedical imaging modalities inwide clinical use, such as ultrasound imaging, X-raycomputed tomography, magnetic resonance imaging, andpositron emission tomography, can provide morphological,anatomical, and functional information about biologicaltissues. However, single mode imaging in conventionalmedicine provides only limited information for definitivediagnoses. Thus, combinational diagnosis using multipleimaging modalities has become increasingly important. Recently, photoacoustic imaging (PAI) has gained significantattention, and several PAI prototypes have been usedin clinical trials. At the same time, PAI has been tested incombination with conventional imaging modalities. For allthese imaging modalities, various contrast-enhancingagents have been developed for various purposes. In thisreview article, we will focus on recent progress in developingdual mode contrast agents for PAI in combinationwith other conventional imaging modalities.

The Demand for Quantitative Techniques in Biomedical Image Informatics

장하영,김혜련,강미선,김명희,장병탁 대한의용생체공학회 2014 Biomedical Engineering Letters (BMEL) Vol.4 No.4

With recent technological advances, biomedical imageinformatics has become a quickly rising field. It focuses onthe use of computational techniques to process and analyzebiomedical images. Its goal is to obtain useful knowledgefrom complicated and heterogeneous images from differentmodalities for biomedical research. Although the advancementof imaging technologies has provided a data explosion, mostbiomedical images are only used by the researchers whocreate them. The lack of a canonical procedure, from dataacquisition to data analysis, contributes to this issue. As thenumber of biomedical images increases, the demand forquantitative techniques rises. In order to increase awarenessof the needs and importance of quantitative techniques forbiomedical image informatics, this paper reviews severalaspects including biomedical imaging, image repositories,and image processing. We explore the state of the arttechnology available in quantitative techniques for biomedicalimage informatics. The essential techniques for quantification,such as imaging devices, biomedical image management,and image processing, are further summarized.

A Proposed Approach for Biomedical Image Denoising Using PCA_NLM

Mohit Bansal,Munesh Devi,Neha Jain,Chinu Kukreja 보안공학연구지원센터 2014 International Journal of Bio-Science and Bio-Techn Vol.6 No.6

The main problem faced during biomedical image diagnosis is the noise introduced due to the consequence of the coherent nature of the image. The noise interfered may be Gaussian noise, speckle noise or Poisson noise, during transmission. The capturing devices itself has a salt & pepper noise. These noises corrupt the image and often lead to incorrect diagnosis. These noises make it more difficult for the observer to discriminate fine detail of the images in diagnostic examinations. Thus, denoising these noises from a noisy image has become the most important step in medical image processing. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. Denoising techniques are aimed at removing noise or distortion from images while retaining the original quality of the image. In this work, we propose PCA_NLM approach which computes neighborhood similarities after PCA projection. Our algorithm is based on the assumption that image contains an extensive amount of self-similarity. The accuracy and computational cost of the PCA algorithm is improved by computing neighborhood similarities, i.e., averaging weights, after a PCA projection to a lower dimensional subspace. We evaluate and compare the performance of proposed technique with different existing methods by using six quality measures PSNR, SNR, MSE, NAE, Correlation Coefficient and SSIM. Comparative analysis shows our approach give the best performance results in terms of improved quality measures as well as visual interpretation.

Heterogeneous Computation on Mobile Processor for Real-time Signal Processing and Visualization of Optical Coherence Tomography Images

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.

Seeing the Previously Unseen: Biomedical Optics and Optical Metrology

H. Yoo(유홍기) Korean Society for Precision Engineering 2021 한국정밀공학회 학술발표대회 논문집 Vol.2021 No.11월

Recent advances in lasers, electronics, fiber optics, optical components, and signal and image processing techniques have introduced optical instrumentations for use in a variety of biomedical and industrial applications. In particular, confocal microscopy offers several advantages over conventional wide field optical microscopy, including the ability to control depth of field, the capability to collect serial optical sections from thick specimens, and the acquisition of three-dimensional images. Since confocal microscopy can provide extremely high-quality and three-dimensional images, it is widely used in various fields such as biological imaging and industrial measurement. In this talk, we discuss real-time three-dimensional imaging based on advanced confocal detection methods. In addition, multimodal multiphoton microscopy for precise tissue characterization is presented. Optical coherence tomography (OCT) acquires high-resolution tomographic images of biological systems. Especially, miniaturized fiber-based endoscopic OCT probe has been successfully translated into clinical diagnostics, including cardiology. While gray-scale OCT provides only the microstructural information of biological samples, multimodal endoscopic imaging techniques can provide comprehensive information, such as biochemical composition and molecular information, on top of the microstructure. We will cover the technical advancements of various fiber-based micro-endoscopy technologies and its biomedical applications.

모바일 장치기반의 바이오 객체 이미지 매칭 시스템 설계 및 구현

박찬일,문승진 한국인터넷정보학회 2019 인터넷정보학회논문지 Vol.20 No.6

Object-based image matching algorithms have been widely used in the image processing and computer vision fields. A variety of applications based on image matching algorithms have been recently developed for object recognition, 3D modeling, video tracking, and biomedical informatics. One prominent example of image matching features is the Scale Invariant Feature Transform (SIFT) scheme. However many applications using the SIFT algorithm have implemented based on stand-alone basis, not client-server architecture. In this paper, We initially implemented based on client-server structure by using SIFT algorithms to identify and match objects in biomedical images to provide useful information to the user based on the recently released Mobile platform. The major methodological contribution of this work is leveraging the convenient user interface and ubiquitous Internet connection on Mobile device for interactive delineation, segmentation, representation, matching and retrieval of biomedical images. With these technologies, our paper showcased examples of performing reliable image matching from different views of an object in the applications of semantic image search for biomedical informatics. 객체기반 이미지 매칭 알고리즘 기술은 이미지 프로세싱 및 컴퓨터 비전 분야에서 광범위하게 사용되어 왔다. 이러한 이미지 매칭 알고리즘 기반의 수 많은 응용 프로그램은 객체인식, 3D 모델링, 비디오 추적 및 바이오 정보학 분야에서 개발되어 왔다. 이미지 매칭 알고리즘의 좋은 예는 Scale invariant Feature Transform(SIFT) 이다. 하지만 SIFT 알고리즘 기술을 이용한 많은 응용 프로그램은 클라이언트-서버 구조가 아닌 하나의 시스템으로 운영되어 왔다. 본 논문은 모바일 플랫폼 기반에서 SIFT 알고리즘 기술을 이용하여 클라이언트-서버 구조로 이미지 매칭 시스템을 구현하였다. 제안된 시스템은 바이오 이미지 객체를 매칭하고 식별하여 사용자에게 유용한 정보를 제공한다. 또한 본 논문의 주요 방법론적 기여는 모바일 장치에 유비쿼터스 인터넷 연결을 활용하여 편리한 사용자 인터페이스와 객체간의 상호작용적인 묘사, 분할, 표현, 매칭 및 바이오 이미지를 검색한다. 본 논문은 이러한 기술과 함께 바이오 정보학에 대한 의미론적 이미지 검색을 수행하며 응용 프로그램에서 객체 이미지의 다른 점을 추출하여 신뢰할 수 있는 이미지 매칭을 수행하는 예를 제시해주었다.

Geographic atrophy segmentation in SD-OCT images using synthesized fundus autofluorescence imaging

Wu, Menglin,Cai, Xinxin,Chen, Qiang,Ji, Zexuan,Niu, Sijie,Leng, Theodore,Rubin, Daniel L.,Park, Hyunjin ELSEVIER SCIENTIFIC PUBLISHERS IRELAND LTD 2019 COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE Vol.182 No.-

<P><B>Abstract</B></P> <P><B>Background and objective</B></P> <P>Accurate assessment of geographic atrophy (GA) is critical for diagnosis and therapy of non-exudative age-related macular degeneration (AMD). Herein, we propose a novel GA segmentation framework for spectral-domain optical coherence tomography (SD-OCT) images that employs synthesized fundus autofluorescence (FAF) images.</P> <P><B>Methods</B></P> <P>An en-face OCT image is created via the restricted sub-volume projection of three-dimensional OCT data. A GA region-aware conditional generative adversarial network is employed to generate a plausible FAF image from the en-face OCT image. The network balances the consistency between the entire synthesize FAF image and the lesion. We use a fully convolutional deep network architecture to segment the GA region using the multimodal images, where the features of the en-face OCT and synthesized FAF images are fused on the front-end of the network.</P> <P><B>Results</B></P> <P>Experimental results for 56 SD-OCT scans with GA indicate that our synthesis algorithm can generate high-quality synthesized FAF images and that the proposed segmentation network achieves a dice similarity coefficient, an overlap ratio, and an absolute area difference of 87.2%, 77.9%, and 11.0%, respectively.</P> <P><B>Conclusion</B></P> <P>We report an automatic GA segmentation method utilizing synthesized FAF images.</P> <P><B>Significance</B></P> <P>Our method is effective for multimodal segmentation of the GA region and can improve AMD treatment.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Novel geographic atrophy (GA) segmentation for spectral-domain optical coherence tomography. </LI> <LI> Our approach uses synthesized fundus autofluorescence images to aid the segmentation. </LI> <LI> Our method can improve the segmentation performance of the GA. </LI> </UL> </P>

Bioengineered Short Carbon Nanotubes as Tumor-Targeted Carriers for Biomedical Imaging

박선호,김태엽,조단비,정진석,조가영,박윤정,강은성,김용호,김장호,김경훈,현훈 한국고분자학회 2019 Macromolecular Research Vol.27 No.8

Cancer is one of the leading causes of death in human beings. Therefore, it is important to detect specific target tumors earlier enough in the formative stages of cancer without causing negative side effects and damages to the body. In this study, we proposed bio-engineered short mussel adhesive proteins (MAPs)-carbon nanotubes (CNTs) as specific tumor-targeted carriers for biomedical imaging. Short CNT near-infrared fluorophore (NIRF) hybrid carriers that were developed accumulated in the tumors with exceptional clearance and specific targeting in less time, and these results indicated that bioengineered short CNT-based carriers have great potential for real time tumor imaging.

Efficient Eye Location for Biomedical Imaging using Two-level Classifier Scheme

Mi Young Nam,Xi Wang,Phill Kyu Rhee 대한전기학회 2008 International Journal of Control, Automation, and Vol.6 No.6

We present a novel method for eye location by means of a two-level classifier scheme. Locating the eye by machine-inspection of an image or video is an important problem for Computer Vision and is of particular value to applications in biomedical imaging. Our method aims to overcome the significant challenge of an eye-location that is able to maintain high accuracy by disregarding highly variable changes in' the environment. A first level of computational analysis processes this image context. This is followed by object detection by means of a two-class discrimination classifier (second algorithmic level). We have tested our eye location system using FERET and BioID database. We compare the performance of two-level classifier with that of non-level classifier, and found it's better performance.

A Computationally Efficient Retina Detection and Enhancement Image Processing Pipeline for Smartphone-Captured Fundus Images

Elloumi, Yaroub,Akil, Mohamed,Kehtarnavaz, Nasser Korea Multimedia Society 2018 The journal of multimedia information system Vol.5 No.2

Due to the handheld holding of smartphones and the presence of light leakage and non-balanced contrast, the detection of the retina area in smartphone-captured fundus images is more challenging than retinography-captured fundus images. This paper presents a computationally efficient image processing pipeline in order to detect and enhance the retina area in smartphone-captured fundus images. The developed pipeline consists of five image processing components, namely point spread function parameter estimation, deconvolution, contrast balancing, circular Hough transform, and retina area extraction. The results obtained indicate a typical fundus image captured by a smartphone through a D-EYE lens is processed in 1 second.