Faculty Profiles - TANJO YUI

Papers - TANJO YUI

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Segmentation of Spinal Canal Region in CT Images using 3D Region Growing Technique

Fu G., Lu H., Tan J., Kim H., Zhu X., Lu J.

2018 International Conference on Information and Communication Technology Robotics 1 - 4 2018.11

Object Detection on Video Images Based on R-FCN and GrowCut Algorithm

Mouri K., Lu H., Tan J.K., Kim H.

2018 International Conference on Information and Communication Technology Robotics 1 - 4 2018.11

Feature Transform Optimization for Pedestrian Classification Reviewed

Nakashima Y., Tan J.

2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2018 804 - 809 2018.10

Detection of circulating tumor cells in fluorescence microscopy Image based on ANN classifier Reviewed

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

2018.10

Registration of phalange region from CR Images based on genetic algorithm Reviewed

Kawagoe, K., Murakami S., Lu H., Tan J.K., Kim H., Aoki T.

Proceedings of the 18th International Conference on Control, Automation and Systems 1464 - 1467 2018.10

Feature Transform Optimization for Pedestrian Classification Reviewed

Proc. of SICE Annual Conference 2018 804 - 809 2018.09

Activity representation by SURF-based templates Reviewed

Ahad M.A.R., Tan J.K., Kim H., Ishikawa S.

Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization 6 ( 5 ) 573 - 583 2018.09

Automatic identification of Bone Erosions in rheumatoid Arthritis from Hand Radiographs Based on Deep Convolution Neural Network Reviewed

Seiichi Murakami, Kazuhiro Hatano, Joo Kooi Tan, Hyoungseop Kim, Takatoshi Aoki

Multimedia Tools and Applications 77 ( 9 ) 10921 - 10937 2018.09

Environment recognition for electric wheelchair based on YOLOv2 Reviewed

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

ACM International Conference Proceeding Series 112 - 117 2018.08

Environment recognition for electric wheelchair based on YOLOv2 Reviewed

Y. Sakai, H.,Lu, Joo Kooi Tan, Hyoungseop Kim

The 3rd International Conference on Biomedical Signal and Image Processing, ICBIP 2018 112 - 117 2018.08

Optimizing Feature Transform Structure for Object Classification Reviewed

Nakashima Yuuki, Tan Joo Kooi

20 ( １ ) １ - １３ 2018.05

Automatic identification of bone erosions in rheumatoid arthritis from hand radiographs based on deep convolutional neural network Reviewed

Murakami S., Hatano K., Tan J., Kim H., Aoki T.

Multimedia Tools and Applications 77 ( 9 ) 10921 - 10937 2018.05

Language：English Publishing type：Research paper (scientific journal)

Although radiographic assessment of joint damage is essential in characterizing disease progression and prognosis in patients with rheumatoid arthritis (RA), it is often difficult even for trained radiologists to find radiographic changes on hand and foot radiographs because lesion changes are often subtle. This paper proposes a novel quantitative method for automatically detecting bone erosion on hand radiographs to assist radiologists. First, the proposed method performs with the crude segmentation of phalanges regions from hand radiograph and extracts the detailed phalanges regions by the multiscale gradient vector flow (MSGVF) Snakes method. Subsequently, the region of interest (ROI; 40 × 40 pixels) is automatically set on the contour line of the segmented phalanges by the MSVGF algorithm. Finally, these selected ROIs are identified by the presence or absence of bone erosion using a deep convolutional neural network classifier. This proposed method is applied to the hand radiographs of 30 cases with RA. The true-positive rate and the false-positive rate of the proposed method are 80.5% and 0.84%, respectively. The number of false-positive ROIs is 3.3 per case. We believe that the proposed method is useful for supporting radiologists in imaging diagnosis of RA.

DOI： 10.1007/s11042-017-5449-4

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85056120147&origin=inward

Detection of a specific moving object from head-mounted camera images Reviewed

Ishitobi K., Tan J.K., Kim H., Ishikawa S.

SII 2017 - 2017 IEEE/SICE International Symposium on System Integration 2018-January 817 - 822 2018.02

Development of a Support System for Human Communication Based on the Visualization of Words Reviewed

Kazuya Motoyama, Tan Joo Kooi, Ishikawa Seiji

The I23rd International Symposium on Artificial Life and Robotics 2018 329 - 332 2018.01

Bone Erosions Detection on Hand CR Images Based on DCNN Reviewed

Seiichi Murakami, Kazuhiro Hatano, Huimin Lu, Joo Kooi Tan, Hyoungseop Kim, Takatoshi Aoki

The I23rd International Symposium on Artificial Life and Robotics 2018/The Third International Sysposium onBioComplexity(ISBC) 357 - 360 2018.01

A unified action recognition framework Reviewed

Amine Ilidrissi, Tan Joo Kooi, Kim Hyoungseop,Ishikawa Seiji

The I23rd International Symposium on Artificial Life and Robotics 2018 57 - 62 2018.01

Automatic Identification of Osteoporosis from Phalanges Computed Radiography Images Based on Deep Convolutional Neural Network

HATANO Kazuhiro, MURAKAMI Seiichi, UEMURA Tomoki, LU Huimin, TAN Joo Kooi, KIM Hyoungseop, AOKI Takatoshi

Medical Imaging Technology 36 ( 2 ) 90 - 95 2018.01

Describing a Self-occlusive Motion by a Reverse Descriptive Motion History Image

TAN Joo Kooi, OKAE Sayaka, YAMASHITA Youtaro, ONO Yuta, ISHIKAWA Seiji

バイオメディカル・ファジィ・システム学会大会講演論文集 ( バイオメディカル・ファジィ・システム学会 ) 31 ( 0 ) 25 - 26 2018.01

Authorship：Lead author Language：Japanese Publishing type：Research paper (conference, symposium, etc.)

This paper proposes a new method of describing a human motion based on Motion History Image (MHI) representation. MHI representation is a well-known way of describing a motion by a single image, but it cannot deal with a self-occlusive motion in a proper way. This paper focuses on the motion in depth direction and proposes Reverse Descriptive MHI (RD-MHI). The RD-MHI and the original MHI are both employed for motion recognition, since the former well describes the motion toward an observing camera, whereas the latter away from the camera. Experimental results support the combination in human motion recognition.

DOI： 10.24466/pacbfsa.31.0_25

CiNii Article

Other Link： https://ci.nii.ac.jp/naid/130007979978

A human care system by a mobile robot employing cooperative objects recognition Reviewed

Takaki S., Tana J.K., Ishikawa S.

IEEE Region 10 Annual International Conference, Proceedings/TENCON 2017-December 1148 - 1152 2017.12

Automatic identification of circulating tumor cells in fluorescence microscopy images based on AdaBoost Reviewed

2017-October 1449 - 1454 2017.12

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Papers - TANJO YUI