研究者詳細 - 神谷　亨

論文 - 神谷　亨

分割表示751 件中 341 - 360 件目／全件表示 >>

Environment recognition for navigation of autonomous wheelchair from a video image 査読有り国際誌

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

International Conference on Control, Automation and Systems 2017-October 1439 - 1443 2017年12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）

© 2017 Institute of Control, Robotics and Systems - ICROS. The Japanese population has rapidly aged and the number of aged persons who have lower physical ability has increased recently. Thus the development of medical and healthcare devices is expected. Wheelchair requires care support in most cases. Therefore the development of autonomous wheelchair is meaningful since we can expect to improve convenience and to reduce burden of caregivers. The autonomous wheelchair requires several techniques. Our research is to develop a navigation system based on image processing techniques. However, we assume that the system instructs an appropriate direction to head towards the destination when a wheelchair user comes to a crossing. Incidentally, deep learning, a kind of artificial neural network, has attracted attention in the field of machine learning in recent years. This paper proposes methodology for supporting autonomous driving by use of a classifier trained on a video images with deep learning. Also, we apply visual odometry to generate training data.

DOI： 10.23919/ICCAS.2017.8204217

Scopus

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Extraction of GGO regions from chest CT images using deep learning 査読有り国際誌

Hirayama K., Miyake N., Lu H., Tan J., Kim H., Tachibana R., Hirano Y., Kido S.

International Conference on Control, Automation and Systems 2017-October 351 - 355 2017年12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）

© 2017 Institute of Control, Robotics and Systems - ICROS. Lung cancer is the leading cause of death which accounts for the number of deaths in cancer in the world. Early detection and early treatment are regarded as an important. Especially, the ground glass opacity (GGO) is a shadow called pre-cancerous lesion, but it is a shadow which is difficult to detect by a radiologist because of haze and complicated shape. Therefore, in recent years, a computer aided diagnosis (CAD) system has been developed for the purpose of improving the detection accuracy for early detection and reducing the burden to radiologists. In this paper, we extract the GGO using Deep Convolutional Neural Network (DCNN) based on emphasized images. Before detect a GGO region, we apply preprocessing such as isotropic voxel to the original images, and extraction of the lung area. Next, we remove the vessel and bronchial region by 3D line filter based on Hessian matrix, and extract the initial candidate regions using density gradient, volume and sphericity. Subsequently, we segment the candidate regions, extraction of features, and reducing false positive shadows. Finally we create emphasize images and identify with DCNN using those images. As a result of applying the proposed method to 31 cases on Lung Image Database Consortium (LIDC), we obtained a true positive rate (TP) of 86.05 [%] and false positive number (FP) of 4.81 [/case].

DOI： 10.23919/ICCAS.2017.8204464

Scopus

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Automatic identification of circulating tumor cells in fluorescence microscopy images based on AdaBoost 査読有り国際誌

Tsuji K., Lu H., Tan J.K., Kim H., Yoneda K., Tanaka F.

International Conference on Control, Automation and Systems 2017-October 1449 - 1454 2017年12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）

Circulating tumor cells (CTCs) is a useful biomarker for cancer metastasis. The blood from a cancer patient is analyzed by a fluorescence microscope. Each case takes a large number of images, which usually have a lot of cell regions. Thus, analyzing the images is hard work for pathologists, and misdiagnosis may happen. In this paper, we develop an automatic CTCs identification method for fluorescence microscopy images. The proposed method consists of three steps. First, we extract cell regions in images using filtering methods. Second, we compute features of each CTC candidate regions. Finally, we identify the CTCs using AdaBoost algorithm. And we analyze the features to know which ones are effective for characterizing CTCs and normal cells. We apply the proposed method to 5040 microscopy images, and evaluate the effectiveness of our method by using leave-one-out cross validation. We achieve a true positive rate of 97.30 [%] and a false positive rate of 12.82 [%].

DOI： 10.23919/ICCAS.2017.8204219

Scopus

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Automatic liver segmentation from multiphase CT images by using level set method 査読有り国際誌

Saito K., Lu H., Tan J.K., Kim H., Yamamoto A., Kido S., Tanabe M.

International Conference on Control, Automation and Systems 2017-October 1590 - 1592 2017年12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）

Segmentation of liver from Multi-phase CT images is one of the essential technology for computer aided diagnosis. Contrast medium gives multi-phase CT images different intensity feature which enables to detect tumor. It is a challenging problem to segment liver region from multi-phase CT images. There are many approaches for solving this problem, however, these methods depend on other phases or registration. In order to solve this problem, we propose anatomy feature-based method which is mostly independent for each phase in this paper. This method uses level set method for final segmentation. The accuracy of segmentation result by level set methods relay on initial contour, so we preprocess initial region of liver by anatomical feature. Then we introduced contour constrain by using ribs information to improve segmentaion accuracy. Our segmentation was evaluated on 5 multi-phase CT images which have 4 phases. Experimental results show that the proposed method is good accuracy for each phase.

DOI： 10.23919/ICCAS.2017.8204240

Scopus

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Underwater Optical Image Processing: a Comprehensive Review 査読有り国際誌

Lu H., Li Y., Zhang Y., Chen M., Serikawa S., Kim H.

Mobile Networks and Applications 22 ( 6 ) 1204 - 1211 2017年12月

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. Underwater cameras are widely used to observe the sea floor. They are usually included in autonomous underwater vehicles (AUVs), unmanned underwater vehicles (UUVs), and in situ ocean sensor networks. Despite being an important sensor for monitoring underwater scenes, there exist many issues with recent underwater camera sensors. Because of light’s transportation characteristics in water and the biological activity at the sea floor, the acquired underwater images often suffer from scatters and large amounts of noise. Over the last five years, many methods have been proposed to overcome traditional underwater imaging problems. This paper aims to review the state-of-the-art techniques in underwater image processing by highlighting the contributions and challenges presented in over 40 papers. We present an overview of various underwater image-processing approaches, such as underwater image de-scattering, underwater image color restoration, and underwater image quality assessments. Finally, we summarize the future trends and challenges in designing and processing underwater imaging sensors.

DOI： 10.1007/s11036-017-0863-4

Scopus

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Weighted Local Laplacian Filtering for Underwater Image Enhancement 査読有り

Lu, Li, Serikawa, Kim

Proceeding of The 2nd International Symposium on Artificial Intelligence and Robotics 36 - 47 2017年11月

Non-Rigid Image Registration Techniques and Its Application to Medical Imaging 招待有り査読有り

Kim

International Symposium on Artificial Intelligence and Robotics 2017年11月

AKEZE局所特徴量に基づく歯科MR画像からの嚥下動作の解析法

末谷、陸、タン、金、田中、鬼頭、森本

第35回計測自動制御学会九州支部学術講演会論文集 25 - 26 2017年11月

DeepEye: A dedicated Camera for Deep-sea Tripod Observation Systems in the South China Sea 査読有り

Lu, Li, Uemura, Tadoh, Kihara, Serikawa, Kim

Proceeding of The 2nd International Symposium on Artificial Intelligence and Robotics 198 - 201 2017年11月

A Diffeomorphic Demons Approch to Statistical Shape Modeling 査読有り

Li, Wu, Xiao, Lu, Kim, Ogubona

Proceeding of The 2nd International Symposium on Artificial Intelligence and Robotics 175 - 182 2017年11月

経時的差分像技術を用いた胸部CT画像上のGGO候補領域の検出

近藤、三宅、陸、タン、金、村上、寺澤、青木、平野、木戸

日本医療情報学会講演会 884 - 887 2017年11月

Automatic segmentation of cell candidate regions in microscopy images based on an optimization algorithm 査読有り

Tsuji K., Tan J., Kim H., Yoneda K., Tanaka F.

International Conference on Control, Automation and Systems 720 - 723 2017年10月

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）

© 2016 Institute of Control, Robotics and Systems - ICROS.Circulating tumor cells (CTCs) is an informative biomarker which assists pathologists in early diagnosis and evaluating therapeutic effects of patients with malignant tumors. The blood from a cancer patient is analyzed by a microscope and a large number of pictures including many cells are generated for each case. Thus, analyzing them is time-consuming work for pathologists, and misdiagnosis may happen since the diagnosis of CTCs tends to depend on the individual skill of pathologist. In this paper, we propose a method which detects cell candidate regions in microscopy images automatically to make quantitative analysis possible by computer. Our proposed method consists of three steps. In the first step, we extract initial cell candidate regions in microscopy images based on the saliency map. In the second step, we choose non-single cell regions from the initial candidates based on the SVM algorithm. In the third step, we separate connected regions into single cell regions based on the branch and bound algorithm. We demonstrated the effectiveness of our proposed method using 540 microscopy images and we achieved a true positive rate of 99.04[%] and a false positive rate of 3.95[%].

DOI： 10.1109/ICCAS.2016.7832397

Scopus

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Extraction of GGO candidate regions from the LIDC database using deep learning 査読有り

Hirayama K., Tan J., Kim H.

International Conference on Control, Automation and Systems 724 - 727 2017年10月

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）

© 2016 Institute of Control, Robotics and Systems - ICROS.In recent years, development of the computer-aided diagnosis (CAD) systems for the purpose of reducing the false positive on visual screening and improving accuracy of lesion detection has been advanced. Lung cancer is the leading cause of cancer death in the world. Among them, GGO (Ground Glass Opacity) that exhibited early in the before cancer lesion and carcinoma in situ shows a pale concentration, have been concerned about the possibility of undetected on the screening. In this paper, we propose an automatic extraction method of GGO candidate regions from the chest CT image. Our proposed image processing algorithms is consist of four main steps; 1) segmentation of volume of interest from the chest CT image and removing the blood vessel regions, bronchus regions based on 3D line filter, 2) first detection of GGO regions based on density and gradient which is selected the initial GGO candidate regions, 3) identification of the final GGO candidate regions based on DCNN (Deep Convolutional Neural Network) algorithms. Finally, we calculates the statistical features for reducing the false-positive (FP) shadow by the rule-based method, performs identification of the final GGO candidate regions by SVM (Support Vector Machine). Our proposed method performed on to the 31 cases of the LIDC (Lung Image Database Consortium) database, and final identification performance of TP: 93.02[%], FP: 128.52[/case] are obtained respectively.

DOI： 10.1109/ICCAS.2016.7832398

Scopus

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Extraction of Spinal Candidate Region from A Dental MR Imaging 査読有り

Suetani, Lu, Tan, Kim, Tanaka, Kitou, Morimoto

Proceedings of the 17th International Conference on Control, Automation and Systems 1601 - 1604 2017年10月

Image Registration of Vertebral Region from CT Images Based on Salient Region Feature 査読有り

Sato, Lu, Tan, Kim, Murakami, Ueno, Terasawa, Aoki

Proceedings of the 17th International Conference on Control, Automation and Systems 1597 - 1600 2017年10月

Classification of Osteoporosis from Phalanges CR Images Based on DCNN 査読有り

Hatano, Murakami, Lu, Tan, Kim, Aoki

Proceedings of the 17th International Conference on Control, Automation and Systems 1593 - 1596 2017年10月

Automatic Liver Segmentation from Multiphase CT images by Using Level Set Method 査読有り

Saito, Lu, Tan, Kim, Yamamoto, Kido, Tanabe

Proceedings of the 17th International Conference on Control, Automation and Systems 1590 - 1592 2017年10月

Image Analysis of Cleft Lip from Moire Image Based on Symmetry Analysis 査読有り

Yamada, Lu, Tan, Kim, Kimura, Okawachi, Nozoe, Nakamura

Proceedings of the 17th International Conference on Control, Automation and Systems 1586 - 1589 2017年10月

Detection of Lung Nodules on Temporal Subtraction Images Using 3D Sparse Coding 査読有り

Tanaka, Miyake, Lu, Tan, Kim, Murakami, Aoki, Hirano, Kido

Proceedings of the 17th International Conference on Control, Automation and Systems 1455 - 1457 2017年10月

Automatic Identification of Circulating Tumor Cells in Fluorescence Microscopy Images Based on AdaBoost 査読有り

Tsuji, Lu, Tan, Kim, Yoneda, Tanaka

Proceedings of the 17th International Conference on Control, Automation and Systems 1449 - 1454 2017年10月

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