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A Divided Scheduling Method based on Structural Characteristics in Wireless
Yoshino, Yuriko,Hashimoto, Masafumi,Wakamiya, Naoki Korea Multimedia Society 2016 The journal of multimedia information system Vol.3 No.4
Wireless mesh networks (WMNs) are used for metropolitan area network that requires high network throughput for handling many users. TDMA-based access is a common solution for this problem and several scheduling methods have been proposed. However, existing heuristic methods have room for improvement at network throughput although they are low complexity. In this paper, we propose a novel divided scheduling method based on structural characteristics in order to improve network throughput in WMNs. It separately schedules neighbor links of gateways and that of the other links by different scheduling algorithms. Simulation-based evaluations show that our proposal improves up to 14% of network throughput compared with conventional scheduling algorithm script.
Development of Image Viewer for Analyzing of Temporal Subtraction from Chest CT Images
Masashi Kondo,Yuriko Yoshino,Hyoungseop Kim,Joo Kooi Tan,Seiji Ishikawa,Seiichi Murakami,Takatoshi Aoki,Rie Tachibana,Yasushi Hirano,Shoji Kido 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10
Recently, CT (Computed Tomography) scanner is used for detecting the abnormalities because CT scanner gradually becomes high resolution and high speed. However, with the improvement of the resolution of CT images, the number of CT images becomes huge. Therefore, radiologists have to analyze huge number of images and they sometimes misdiagnoses are happened. Hence, to deal with this problem the CAD (Computer Aided Diagnosis) system is developed. One of the CAD systems called temporal subtraction technique is useful to detect abnormalities in medical field. There is no viewer system which displays abnormal region using temporal subtraction technique. In this paper, we develop a novel temporal subtraction technique to help the radiologists to reduce diagnostic time and improve the diagnostic accuracy.
Detection of Abnormal Shadows on Temporal Subtraction Images Based on Multi-phase CNN
Mitsuaki NAGAO,Noriaki MIYAKE,Yuriko YOSHINO,Huimin LU,Hyoungseop KIM,Seiichi MURAKAMI,Takatoshi AOKI,Shoji KIDO 제어로봇시스템학회 2018 제어로봇시스템학회 국제학술대회 논문집 Vol.2018 No.10
Recently, visual screening based on CT images become useful tools in the medical fields. However, due to the large number of images and the complexity of the image processing algorithms, image processing technique for the high screening quality is still required. To overcome this problem, some computer aided diagnosis (CAD) algorithms are proposed. Cancer is a leading cause of death both in Japan and worldwide. Detection of cancer region in CT images is the most important task to early detection and early treatment. We have designed and developed a framework combining machine learning based on multi-phase convolutional neural networks (CNN) and temporal subtraction techniques based on non-rigid image registration algorithm. Our main classification method can be built into three main steps; i) preprocessing for image segmentation, ii) image matching for registration, and iii) classification of abnormal regions based on machine learning algorithms. We performed our proposed technique to 25 thoracic MDCT sets and obtained true positive rates of 93.55%, false positive rates of 10.93 /case.
Detection of Abnormal Candidate Regions on Temporal Subtraction Images Based on DCNN
Mitsuaki NAGAO,Noriaki MIYAKE,Yuriko YOSHINO,Huimin LU,Joo Kooi TAN,Hyoungseop KIM,Seiichi MURAKAMI,Takatoshi AOKI,Yasushi HIRANO,Shoji KIDO 제어로봇시스템학회 2017 제어로봇시스템학회 국제학술대회 논문집 Vol.2017 No.10
Cancer is a leading cause of death both in Japan and worldwide. Detection of cancer region in CT images is the most important task to early detection. Recently, visual screening based on CT images become useful tools for cancer detection. However, due to the large number of images and the complexity of the image processing algorithms, image processing technique is still required a high screening quality. To overcome this problem, some computer aided diagnosis (CAD) algorithms are proposed. In this paper, we have designed and developed a framework combining machine learning based on deep convolutional neural networks (DCNN) and temporal subtraction techniques based on non-rigid image registration algorithm. Our main classification method can be built into three main steps; i) pre-processing for image segmentation, ii) image matching for registration, and iii) classification of abnormal regions based on machine learning algorithms. We performed our proposed technique to 25 thoracic MDCT sets and obtained true positive rates of 92.31 [%], false positive rates of 6.32 [/case] were obtained.