研究者詳細 - 神谷　亨

論文 - 神谷　亨

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実数値GAに基づく指骨CR画像の位置合わせ

川越、村上、陸、タン、金、青木

第38回医療情報学連合大会抄録集 388 - 391 2018年11月

CT temporal subtraction method for detection of sclerotic bone metastasis in the thoracolumbar spine 査読有り国際誌

Ueno M., Aoki T., Murakami S., Kim H., Terasawa T., Fujisaki A., Hayashida Y., Korogi Y.

European Journal of Radiology 107 54 - 59 2018年10月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 2018 Elsevier B.V. Purpose: To assess the effectiveness of a CT temporal subtraction (TS) method on radiologists’ performance in sclerotic metastasis detection in the thoracolumbar spine. Materials and methods: 20 pairs (current and previous CTs) of standard-dose CT and their TS images in patients with sclerotic bone metastasis and 20 pairs (current and previous CTs) of those in patients without bone metastasis were used for an observer performance study. A total of 135 lesions were identified as the reference standard of actionable lesions (sclerotic metastasis newly appeared or increased in size or in attenuation). 4 attending radiologists and 4 radiology residents participated in this observer study. Ratings and locations of “lesions” determined by the observers were utilized for assessing the statistical significance of differences between radiologists’ performances without and with the CT-TS images in JAFROC analysis. The statistical significance of differences in the reviewing time was determined by a two-tailed paired t-test. Results: The average figure-of-merit (FOM) values for all but one radiologist increased to a statistically significant degree, from 0.856 without the CT-TS images to 0.884 with the images (P =.037). The average sensitivity for detecting the actionable lesions was improved from 60.7 % to 72.5% at a false-positive rate of 0.15 per case by use of the CT-TS images. The average reading time with CT-TS images was significantly shorter than that without (150.6 s vs. 166.5 s, P =.004). Conclusion: The use of CT-TS would improve the observer performance for the detection of the sclerotic bone metastasis in the thoracolumbar spine.

DOI： 10.1016/j.ejrad.2018.07.017

Scopus

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Statistical shape model generation using K-means clustering 査読有り国際誌

Wu J., Li G., Lu H., Kim H.

ACM International Conference Proceeding Series 207 - 211 2018年09月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2018 Association for Computing Machinery. Statistical shape models (SSMs) is a robust and efficient method in medical image segmentation. In this paper, a novel landmark corresponding method based on k-means clustering and demons registration is proposed to train a 3-D statistical shape model with higher quality. The k-means clustering method is performed on the original geometric surface to obtain a simplified surface as standard set of landmarks to find correspondent landmarks on each mapped spherical surface obtained from demon registration in the training set. Twenty cases of left lung and right lung regions in thoracic MDCT images are used in the experiment to build two SSMs. Performance evaluation results show that SSMs generated by the proposed method achieve better generalization ability and specificity while maintaining the same compactness and accuracy of segmentation as those reported by state-of-the-art methods.

DOI： 10.1145/3277453.3277467

Scopus

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R-FCNとGrowCutを用いたボールペンの検出

毛利、陸、金

産業応用工学会全国大会2018講演論文集 85 - 86 2018年09月

Object Detection on Video Images Based on GrowCut Algorithm 査読有り国際誌

Mouri, Lu, Tan, Kim

ICT-Robot 2018年09月

Activity representation by SURF-based templates 査読有り国際誌

Ahad M., Tan J., Kim H., Ishikawa S.

Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization 6 ( 5 ) 573 - 583 2018年09月

記述言語：英語掲載種別：研究論文（学術雑誌）

© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. This paper presents a method to recognise actions, which are overlapping and multi-dimensionalities. A spatio-temporal representation is illustrated on local interest points to compute global features. Motion history image (MHI) is computed and motion overwriting the motion overwriting problem of the MHI. The main contribution of this paper is that it demonstrates a higher discriminative ability of various complex actions when compared to the other MHI-based approaches. It selects local interest feature points to capture motion information using Speeded-Up Robust Features (SURF). These key interest points are exploited to compute gradient-based optical flow into four channels. RANSAC is exploited to remove outliers. It incorporates frame-subtracted accumulated image so that we can mask out points that are not required. Afterwards, feature vectors are computed based on moments. Actions are recognised by employing a nearest neighbour classifier and leave-one-out cross-validation partitioning scheme. The proposed method provides satisfactory recognition rates over several other approaches for some challenging actions in outdoor scenes.

DOI： 10.1080/21681163.2017.1298472

Scopus

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FDCNet: filtering deep convolutional network for marine organism classification 査読有り国際誌

Lu H., Li Y., Uemura T., Ge Z., Xu X., He L., Serikawa S., Kim H.

Multimedia Tools and Applications 77 ( 17 ) 21847 - 21860 2018年09月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 2017, Springer Science+Business Media New York. Convolutional networks are currently the most popular computer vision methods for a wide variety of applications in multimedia research fields. Most recent methods have focused on solving problems with natural images and usually use a training database, such as Imagenet or Openimage, to detect the characteristics of the objects. However, in practical applications, training samples are difficult to acquire. In this study, we develop a powerful approach that can accurately learn marine organisms. The proposed filtering deep convolutional network (FDCNet) classifies deep-sea objects better than state-of-the-art classification methods, such as AlexNet, GoogLeNet, ResNet50, and ResNet101. The classification accuracy of the proposed FDCNet method is 1.8%, 2.9%, 2.0%, and 1.0% better than AlexNet, GooLeNet, ResNet50, and ResNet101, respectively. In addition, we have built the first marine organism database, Kyutech10K, with seven categories (i.e., shrimp, squid, crab, shark, sea urchin, manganese, and sand).

DOI： 10.1007/s11042-017-4585-1

Scopus

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BrainNets: Human Emotion Recognition Using an Internet of Brian Things Platform 査読有り国際誌

Lu H., Kim H., Li Y., Zhang Y.

2018 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018 1313 - 1316 2018年08月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2018 IEEE. Human wearable helmet is a useful tool for monitoring the status of miners in the mining industry. However, there is little research regarding human emotion recognition in an extreme environment. In this paper, an emotional state evoked paradigm is designed to identify the brain area where the emotion feature is most evident. Next, the correct electrode position is determined for the collection of the negative emotion by the electroencephalograph (EEG) based on the international 10-20 system of electrode placement. And then, a fusion algorithm of the anxiety level is proposed to evaluate the person's mental state using the θ, α, and β rhythms of an EEG. Experiments demonstrate that the position Fp2 is the best electrode position for obtaining the anxiety level parameter. The most visible EEG changes appear within the first two seconds following stimulation. The amplitudes of the θ rhythm increase most significantly in the negative emotional state.

DOI： 10.1109/IWCMC.2018.8450382

Scopus

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Automatic detection of cell regions in microscope images based on BFED algorithm 査読有り国際誌

Nakamichi K., Lu H., Kim H., Yoneda K., Tanaka F.

ACM International Conference Proceeding Series 38 - 42 2018年08月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2018 Association for Computing Machinery. Circulating tumor cells (CTC) attract attention as a biomarker that can evaluate cancer metastasis and therapeutic effects. The CTC exists in the blood of cancer patients, so pathologists analyze blood by using a fluorescence microscope. However, manual analysis by pathologists is hard-work since the number of CTC to substances contained in the blood is very few and the cell regions are often unclear depending on shooting environments. In addition, there are few studies on automatic identification of CTC. In this paper, we develop an automatic detection method of cell regions in microscope images based on bacterial foraging-based edge detection (BFED) algorithm to analyze CTC. In the first step, we detect the initial cell regions by BFED algorithm. Second, we identify whether the region is a single cell or multiple cells come in connect with other cell(s) by SVM. Third, when a cell is connected with other one, we separate the connecting cells by branch and bound algorithm and obtain the final cell regions. We applied our proposed method to 1680 microscopy images (6 cases). The experimental results demonstrate that the proposed method has a true positive rate of 93.9% and a false positive 1.29 /case.

DOI： 10.1145/3278229.3278232

Scopus

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Environment recognition for electric wheelchair based on YOLOv2 査読有り国際誌

Sakai Y., Lu H., Tan J., Kim H.

ACM International Conference Proceeding Series 112 - 117 2018年08月

担当区分：責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）

© 2018 Association for Computing Machinery. At present, the aging population is growing in Japan. Along with that, the need for the utilization of welfare equipment is increasing. Electric wheelchair, a convenient transportation tool, is popularized rapidly. However, many accidents have occurred by using electric wheelchair, and the dangers for driving are pointed out. Therefore, it needs to improve accident factors, reduce accidents and improve the convenience of electric wheelchair by automation. Environmental recognition is the key technology for developing autonomous electric wheelchair. Environmental recognition includes self-position estimation, recognition of sidewalks, crosswalks, traffic lights, and moving object prediction, etc. In order to solve these problems, this paper describes a system for the detection of sidewalks, crosswalks and traffic lights. We develop the object recognition methods using a modified YOLOv2 that is one of object detection algorithms applying convolutional neural networks (CNN). We detect the object through YOLOv2 and perform processing such as unnecessary bounding box deletion and interpolation. The experimental results demonstrate that the area under the curve (AUC) of the detection rate is 0.620.

DOI： 10.1145/3278229.3278231

Scopus

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Preface 招待有り査読有り国際誌

Kim H.

ACM International Conference Proceeding Series 2018年08月

Motor anomaly detection for unmanned aerial vehicles using reinforcement learning 査読有り国際誌

Lu H., Li Y., Mu S., Wang D., Kim H., Serikawa S.

IEEE Internet of Things Journal 5 ( 4 ) 2315 - 2322 2018年08月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 2017 IEEE. Unmanned aerial vehicles (UAVs) are used in many fields including weather observation, farming, infrastructure inspection, and monitoring of disaster areas. However, the currently available UAVs are prone to crashing. The goal of this paper is the development of an anomaly detection system to prevent the motor of the drone from operating at abnormal temperatures. In this anomaly detection system, the temperature of the motor is recorded using DS18B20 sensors. Then, using reinforcement learning, the motor is judged to be operating abnormally by a Raspberry Pi processing unit. A specially built user interface allows the activity of the Raspberry Pi to be tracked on a Tablet for observation purposes. The proposed system provides the ability to land a drone when the motor temperature exceeds an automatically generated threshold. The experimental results confirm that the proposed system can safely control the drone using information obtained from temperature sensors attached to the motor.

DOI： 10.1109/JIOT.2017.2737479

Scopus

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Automatic road detection system for an airland amphibious car 査読有り

Li, Lu, Nakayama, Kim, Serikawa

Future Generation Computer Systems 85 51 - 59 2018年08月

Automatic road detection system for an air–land amphibious car drone 査読有り国際誌

Li Y., Lu H., Nakayama Y., Kim H., Serikawa S.

Future Generation Computer Systems 85 51 - 59 2018年08月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 2018 Elsevier B.V. In recent years, unmanned aerial vehicle (UAV) technologies have rapidly developed. Drones, which are one type of UAV, are used in many industrial fields, such as photography, delivery and agriculture. However, a commercial drone can fly for only approximately 20 min on one charge. Furthermore, drones are prohibited from flying in some areas, and cannot be operated in bad weather. Due to the development of drone technologies, we must reduce energy consumption and achieve long-range movement. To overcome these limitations, we develop a new air–land amphibious car drone that can fly and requires less power consumption in land mode; this extends the range of mobility of the drone. Moreover, land mode can be used to pass through restricted areas or bad weather conditions by sliding. Furthermore, we develop a Convolutional Neural Network (CNN)-based algorithm for detecting the road in a captured scene. To more accurately segment the road region based on images from the equipped camera of the drone, we propose atrous spatial pyramid pooling (ASPP) ResNet blocks, instead of Resblocks, which were proposed by DeepLab. The experimental results demonstrate that the proposed method improves the pixel accuracy (PA) to 85.6% and achieves a mean Intersection over Union (mIoU) of 55.8%.

DOI： 10.1016/j.future.2018.02.036

Scopus

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ResNetを用いた指骨CR画像からの骨粗しょう症の自動識別

畠野、村上、植村、陸、タン、金、青木

第37回日本医用画像工学会大会予稿集 238 - 244 2018年07月

3次元Residual networks(ResNets)を用いた大腸CADにおける偽陽性陰影の識別

植村、陸、金、橘、弘中、Nappi、吉田

第37回日本医用画像工学会大会予稿集 436 - 442 2018年07月

CNNによる胸部CT画像からの経時的差分画像上の異常陰影の検出

長尾、植村、三宅、陸、タン、金、村上、青木、平野、木戸

第37回日本医用画像工学会大会予稿集 404 - 410 2018年07月

Image Registration for Detection of Sclerotic Bone Metastasis in CT Images 査読有り国際誌

Kim, Lu, Tan, Murakami, Ueno, Terasawa, TAoki

40th International Conference of the IEEE Engineering in Medicine and Biology Society 2018年07月

3D deelp residual convolutional networks for computer-aided detection of polyos in CT colonography 査読有り

Uemura, Lu, Kim, Tachibana, Hironaka, Nappi, Yoshida

International Journal of Computer Assited Radiology and Surgery 94 - 95 2018年06月

Low illumination underwater light field images reconstruction using deep convolutional neural networks 査読有り国際誌

Lu H., Li Y., Uemura T., Kim H., Serikawa S.

Future Generation Computer Systems 82 142 - 148 2018年05月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 2018 Elsevier B.V. Underwater optical images are usually influenced by low lighting, high turbidity scattering and wavelength absorption. To solve these issues, a great deal of work has been performed to improve the quality of underwater images. Most of them use the high-intensity LEDs for lighting to obtain the high contrast images. However, in high turbidity water, high-intensity LEDs cause strong scattering and absorption. In this paper, we propose a light field imaging approach for solving underwater imaging problems in a low-intensity light environment. As a solution, we tackle the problem of de-scattering from light field images by using deep convolutional neural networks with depth estimation. Furthermore, a spectral characteristic-based color correction method is used for recovering the color reduction. Experimental results show the effectiveness of the proposed method by challenging real-world underwater imaging.

DOI： 10.1016/j.future.2018.01.001

Scopus

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