研究者詳細 - 神谷　亨

論文 - 神谷　亨

分割表示751 件中 181 - 200 件目／全件表示 >>

Incorporating Ghost Module into RCAN for Super-Resolution of Satellite Images 査読有り国際誌

Ikeda H., Li G., Kamiya T.

International Conference on Control, Automation and Systems 2021-October 2059 - 2063 2021年01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）

With the explosion of amount of low cost satellites, satellite images have been widely used for many non-military applications, such as agriculture, landscape, and recognition of environment. Improving the image resolution to mine useful information becomes one of the immediate problems. Therefore, it is expected to improve the recognition accuracy by increasing the resolution of satellite images. Recently, deep learning technique has been proposed to increase the resolution of images. However it requires a large number of learning parameters, which results in huge computational cost. To overcome this problem, we develop a new deep learning model based on ghost module to reduce the parameters while maintaining the quality of results. We utilized Google Earth Pro satellite imagery for the network training and testing. Comparing to the classical convolutional neural network module based methods, the number of parameters used in our model was reduced 49.31 % but keeping the same level of Peak Signal-to-Noise Ratio (24.1578) and Structural Similarity (0.7174).

DOI： 10.23919/ICCAS52745.2021.9649982

Scopus

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Extraction of Cervical Lymph Nodes Based on Three-Dimensional Image Registration 査読有り国際誌

Shime N., Kamiya T., Ishida T.

International Conference on Control, Automation and Systems 2021-October 1792 - 1795 2021年01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）

One of the difficulties of cancer is the metastasis of cancer through the lymph nodes. Therefore, early detection and treatment of cancerous is important. Visual screening is one of useful tool for diagnosing of the cancer. However, the number of images obtained at a time is large, and the burden on the reading physician is increasing. Furthermore, reading of image is based on the subjective judgment of the physician, which may lead to different diagnostic results. To solve these problems, computer aided diagnosis (CAD) systems have been attracting attention in recent years. One of the CAD systems is the temporal subtraction image technology. In this paper, we propose an image alignment method for generating temporal subtraction images from images taken before and after contrast agent was administered to the cervical lymph nodes of the same subject. In addition, we propose an image analysis method that suppresses overextraction of lymph node candidate regions on the temporal subtraction image based on the features of lymph nodes. We applied the proposed method to the CT images of three sets and compared the temporal subtraction images with the final lymph node extraction images, and confirmed that the proposed method can suppress the overextraction of lymph node regions.

DOI： 10.23919/ICCAS52745.2021.9649797

Scopus

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Environment Recognition from A Spherical Camera Image Based on DeepLab v3+ 査読有り国際誌

Nishida Y., Li Y., Kamiya T.

International Conference on Control, Automation and Systems 2021-October 2043 - 2046 2021年01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）

The number of users of electric wheelchairs has been increasing in recent years because it is easy to operate the electric wheelchair and do not require physical strength. However, the traffic accidents are also increasing because of the large number of wheelchairs. The development of autonomous electric wheelchairs is expected to reduce the risk of accidents and improve the convenience of electric wheelchairs. Environmental recognition is essential for the development of autonomous electric wheelchairs. In this paper, we propose a method for recognizing roads, sidewalks, buildings, electric wheelchair drivers, poles, electric wheelchairs, vegetation, curbs, sky, pedestrians, lanes, cars, steps, and bicycles. For recognizing those objects, we use a panoramic image acquired from a spherical camera. As the machine techniques, we use DeepLab v3+, a semantic segmentation algorithm based on Convolutional Neural Network (CNN). In the proposed method, a new CNN model is constructed by adding deformable convolution, SE-block, and MobileNet v2 to DeepLab v3+ into the original DeepLab v3+. In the experiment, IoU 38.8% and Dice of 46.7% were obtained.

DOI： 10.23919/ICCAS52745.2021.9649846

Scopus

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Determination of Abnormality of IGBT Images Using VGG16 査読有り国際誌

Ogawa T., Watanabe A., Omura I., Kamiya T.

International Conference on Control, Automation and Systems 2021-October 2055 - 2058 2021年01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）

A power device is a semiconductor device for power control used for power conversion such as converting direct current to alternating current and alternating current to direct current. It is widely used such as refrigerators, air conditioners which is implemented electronic components that are closely related to our daily lives. Therefore, high reliability and safety are required, and power cycle tests are conducted for the purpose of evaluating them. In the conventional test, there is a problem that it is difficult to perform analysis because sparks are generated during the test and the device is severely damaged after the test. To solve this problem, a new technology has been developed that adds ultrasonic that enable internal observation during the test. However, there are remains a problem that the method for analyzing the ultrasonic image obtained in the new technology has not been established. Also, few abnormal images are obtained in the test. In this paper, we propose a method for detection of abnormal devices based on CNN. Especially, we implement a Cycle-GAN to extend the abnormal data and classify the known image based on improved VGG16. As an experimental result, classification accuracy of Precision = 97.06%, Recall = 93.58%, F-measure = 95.17% were obtained.

DOI： 10.23919/ICCAS52745.2021.9650029

Scopus

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Detection of the root resorption from panoramic X-ray images using deep metric learning 査読有り国際誌

Tamura K., Kamiya T., Oda M., Tanaka T., Morimoto Y.

International Conference on Control, Automation and Systems 2021-October 1800 - 1803 2021年01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）

Root resorption is a pathological process characterized by the loss of tooth roots because of inflammation induced by bacterial infection, trauma, physical or chemical irritation. As a result, the development of periodontal disease, increased susceptibility to infection and crooked teeth. In the worst case, it can lead to tooth extraction. Root resorption is often caused by pressure during orthodontic treatment. The presence of root resorption should be checked regularly during orthodontic treatment, as it often occurs. It is necessary to check for root resorption periodically during orthodontic treatment. However, it is difficult to detect the root resorption using a panoramic radiograph. As a result, root resorption is often latent and goes undetected. In this paper, we propose an image analysis method based on deep learning technique for detecting the root resorption on panoramic radiograph. We incorporate the EfficientNet for feature extraction in deep learning to the center loss and triplet loss as the loss function for metric learning. Our proposed method performed to 337 images which is obtained by panoramic radiograph. Accuracy of 71 %, true positive rate of 77%, false positive rate of 30% were obtained.

DOI： 10.23919/ICCAS52745.2021.9649745

Scopus

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Classification of Respiratory Sounds by Generated Image and Improved CRNN 査読有り国際誌

Asatani N., Kamiya T., Mabu S., Kido S.

International Conference on Control, Automation and Systems 2021-October 1804 - 1808 2021年01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）

The death toll from respiratory illness reached nearly 8 million in 2019. Auscultation is used to diagnose for respiratory illness. Highly accurate diagnosis is required to reduce the number of deaths. However, unlike diagnostic imaging, auscultation of respiratory sounds could not visualize the diagnostic results. In addition, since there is a problem that the experience of a doctor affects the diagnosis results, it is required to develop a diagnostic system for quantitative analysis. In recent years, the development of a diagnostic system using the ICBHI 2017 Challenge Respiratory Sound Database has been carried out in the field of respiratory sound analysis. However, the proposed system still has accuracy problems. Therefore, in this study, we improve the proposed method by classifying the improved CRNN (Convolutional Recurrent Neural Network) by inputting multiple respiratory sound images. As a result, Sensitivity: 0.64, Specificity: 0.83, Average Score: 0.74, Harmonic Score: 0.72 were obtained, and excellent results were achieved compared with other methods.

DOI： 10.23919/ICCAS52745.2021.9649906

Scopus

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Automatic Segmentation of Finger Bone Regions from CR Images Using Improved DeepLabv3+ 査読有り国際誌

Ono H., Murakami S., Kamiya T., Aoki T.

International Conference on Control, Automation and Systems 2021-October 1788 - 1791 2021年01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）

The number of hospitalized patients and the number of people requiring nursing care are serious social problems in Japan due to the increasing elderly population. The major causes of bedridden patients are bone and joint disorders caused by rheumatoid arthritis and osteoporosis. Early detection and treatment of these bone diseases are important because they significantly interfere with the quality of life (QOL) as the symptoms progress. Visual screening based on CR is used as a diagnosing tool for bone diseases. However, imaging diagnosis is subjective and lacks objectivity, and there is a possibility that lesions may be overlooked. In addition, it is difficult to find out subtle changes from images, increasing the workload for doctors. To solve these problems, there is a need to develop a computer aided diagnosis (CAD) system that can quantitatively diagnose bone diseases. We propose a method for automatic extraction of phalange regions for the CAD system to diagnose these diseases. The proposed method can extract the phalanges with high accuracy by using the improved DeepLabv3+. In this paper, we apply the proposed method to 101 cases of CR images and mIoU of 0.949 was obtained.

DOI： 10.23919/ICCAS52745.2021.9649864

Scopus

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Automatic Identification of CTC in Fluorescence Microscope Images Using Segmentation Algorithm of Cell Nucleus 査読有り国際誌

Hashimoto K., Kamiya T., Yoneda K., Tanaka F.

International Conference on Control, Automation and Systems 2021-October 2051 - 2054 2021年01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）

Today, cancer is the number one cause of death in Japan, and cancer accounts for 27.3% of all deaths number. The development of cancer by repeating metastasis, hence it is important to operate early detection and early treatment. The diagnosis of cancer includes various treatments, but it is difficult to judge whether cancer is metastatic or not. Then, analysis of Circulating Tumor Cells (CTCs) in blood has been attracting attention as a new biomarker. However, because the ratio of CTCs in a billion blood cells is only a few, and there is a concern that the burden on doctors will increase. We propose a method for automatic identification of CTCs from fluorescence microscopy images to enable quantitative analysis by computer for the diagnosis of CTCs in blood. First, after detecting the cell candidate regions mainly by filtering, we set the region of interest in the cell candidate regions and reconstruct the region of interest by cutting out the cell nucleus region. In this paper, we applied the proposed method to 5,040 images of 6 samples and conducted experiments on the identification of CTCs. As a result, the number of detections was 148(TPR = 100%) and the number of over-detected non-CTCs was 988.

DOI： 10.23919/ICCAS52745.2021.9650022

Scopus

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A Method for Evaluating of Asymmetry on Cleft Lip Using Symmetry Plane 査読有り国際誌

Sawada S., Kamiya T., Kimura-Nomoto N., Okawachi T., Nozoe E., Nakamura N.

International Conference on Control, Automation and Systems 2021-October 1796 - 1799 2021年01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）

Cleft lip is one of the most common morphological abnormalities that occur when the fetal lips are torn. Cleft lip is treated with multiple surgical procedures. However, there is a problem that the evaluation of surgical results depends on the subjectivity of doctors and the degree of symmetry is difficult to evaluate quantitatively. Therefore, as a previous study, a method for detecting and evaluating the plane of symmetry of the face with an asymmetric region was proposed, however, there remained a problem with the accuracy of separation of the asymmetry between patients and healthy subjects. In this paper, we propose an asymmetry evaluation method with high separation accuracy using the plane of symmetry of the face. The plane of symmetry used as the evaluation standard is obtained by adjusting the plane of symmetry of the entire face to the plane of symmetry of the surgical region. The asymmetry is evaluated by the evaluation index using the depth information with the plane of symmetry as the reference. By comparing the proposed method with the conventional method, the degree of separation of asymmetry between patients and healthy subjects was improved, and more useful results were obtained as an evaluation index.

DOI： 10.23919/ICCAS52745.2021.9649934

Scopus

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A Classification Method for Magnetic Particle Testing Image Using U-Net 査読有り

Moritsuka S., Kamiya T.

International Conference on Control, Automation and Systems 2021-October 2047 - 2050 2021年01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）

Magnetic Particle Testing (MPT) is a method for determining the presence or absence of a defect by magnetizing the object to be inspected and sprinkling magnetic powder, which is absorbed by the defective part such as a crack and appears as a magnetic powder pattern, which is then evaluated by a specialist. By using the MTP, inspection can be performed without breaking the object to be inspected. However, there are some problems such as the possibility of overlooking defects. In this paper, to solve the problems we develop a classification method of defect images by deep learning for the automation of MPT. The proposed method is based on the structure of U-Net, which has excellent segmentation capability in image processing, and performs segmentation using an improved model that adds convolutional layers to U-Net. Then, an algorithm that combines the result with the last part of the encoder of U-Net is used to discriminate the presence or absence of defects. Using this method, defects were classified from the images obtained during MPT. The results showed that Accuracy of 85.8%, TPR of 65.2%, and FPR of 13.8%.

DOI： 10.23919/ICCAS52745.2021.9650052

Scopus

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Automatic Classification of Respiratory Sounds Based on LSTM with Affinity Loss

Minami, Kamiya, Mabu, Kido

8th International Symposium on Applied Engineering and Sciences (SAES2020) 2020年12月

A Detection Method for Liver Cancer Region Based on Faster R-CNN and Level-set Method

Furuzuki, Kamiya

8th International Symposium on Applied Engineering and Sciences (SAES2020) 2020年12月

多元計算解剖学における高度知能化診断招待有り査読有り

平野、神谷、木戸

Medical Imaging Technology 38 ( 5 ) 217 - 221 2020年11月

深層学習による胸部CT画像からの結節状陰影の検出

玉井、神谷、青木、木戸

第33回バイオメディカル・ファジィ・システム学会年次大会講演論文集 ( 第33回バイオメディカル・ファジィ・システム学会年次大会講演論文集 ) 26 - 29 2020年10月

YOLOｖ３を用いた全天球カメラ映像からの障害物認識

甲斐、陸、神谷

第33回バイオメディカル・ファジィ・システム学会年次大会講演論文集 ( 第33回バイオメディカル・ファジィ・システム学会年次大会講演論文集 ) 84 - 88 2020年10月

SVMによる胸部CT画像からのドライバー遺伝子情報変異の検出

吉福、寺澤、神谷、青木

第33回バイオメディカル・ファジィ・システム学会年次大会講演論文集 ( 第33回バイオメディカル・ファジィ・システム学会年次大会講演論文集 ) 80 - 83 2020年10月

２つの解像度スペクトルとTF-CRNNによる呼吸音の分類

浅谷、神谷、間普、木戸

第33回バイオメディカル・ファジィ・システム学会年次大会講演論文集 ( 第33回バイオメディカル・ファジィ・システム学会年次大会講演論文集 ) 64 - 67 2020年10月

An image registration method for spine region in CT images considering sagittal plane 査読有り国際誌

Yamashita Y., Kamiya T., Aoki T.

International Conference on Control, Automation and Systems 2020-October 407 - 410 2020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（国際会議プロシーディングス）

© 2020 Institute of Control, Robotics, and Systems - ICROS. In recent years, the computer-aided diagnosis (CAD) systems for supporting to the physician is attracting attention in medical research field. One of them is temporal subtraction technique. It is a technique to generate images emphasizing temporal changes in lesions by performing a differential operation between current and previous image of the same subject. However, there is a problem of mistakenly selecting current and previous slice of a spine region with similar geometry as the same slice because many of the spine regions in axial plane have similar geometries. In this paper, we propose an image registration method for the detection of bone metastases from the spine region by creating a temporal subtraction images from CT images. Especially, we develop an image registration system for spine region considering sagittal plane to accurately select the same slice for the current image and the previous image. In the experiment, we applied the proposed method to CT images of 27 cases with bone metastases and the results were compared with the markings of the lesions.

DOI： 10.23919/ICCAS50221.2020.9268246

Scopus

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Automatic Classification of Respiratory Sounds Considering Time Series Information Based on VGG16 with LSTM 査読有り国際誌

Asatani N., Kamiya T., Mabu S., Kido S.

International Conference on Control, Automation and Systems 2020-October 423 - 426 2020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（国際会議プロシーディングス）

© 2020 Institute of Control, Robotics, and Systems - ICROS. According to the 2016 World Health Organization (WHO) survey, respiratory diseases are serious diseases that account for four of the top ten causes of death in the world, accounting for more than 8 million deaths worldwide. Currently, the diagnosis of respiratory disease is made by auscultation, but in order to make an accurate diagnosis, a number of abnormal patterns of respiratory sounds need to be memorized, and the results of the diagnosis are dependent on the proficiency of the physician. Therefore, a computer aided diagnosis (CAD) system is needed to quantitatively classify the respiratory sounds and output the results as a "second opinion". In this paper, a short-time Fourier transformed spectrogram, a Constant-Q transformed logarithmic frequency spectrogram, and a continuous wavelet transformed scalogram are simultaneously input to VGG16 which is one of the network models of CNN(Convolutional Neural Network) and classified by LSTM (Long short-term memory). The proposed method is applied to 26 respiratory sounds, and the 0.90 of accuracy, sensitivity of 0.97, and specificity of 0.90 is obtained.

DOI： 10.23919/ICCAS50221.2020.9268428

Scopus

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Identification of normal and abnormal from ultrasound images of power devices using VGG16 査読有り国際誌

Ogawa T., Lu H., Watanabe A., Omura I., Kamiya T.

International Conference on Control, Automation and Systems 2020-October 415 - 418 2020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（国際会議プロシーディングス）

© 2020 Institute of Control, Robotics, and Systems - ICROS. Power devices are semiconductor devices that handle high voltages and large currents, which are used in electric vehicles, televisions, and trains. Therefore, high reliability and safety are required, and to ensure this, power cycle tests are performed to analyze the breakdown process. Conventional tests are often difficult to analyze due to the influence of sparks generated during the test. Therefore, new tests are being developed by adding ultrasound to conventional methods. The new technology is capable of continuously recording structural changes inside the device during testing, which is expected to make testing much easier than conventional testing. However, the new technology still has some challenges. The main problems are the lack of a method for analyzing large amounts of image data and the extraction of small changes in image features that are difficult to distinguish with the human eye, and the establishment of such a system is required. In this paper, we use deep learning for image classification of the obtained ultrasound images. We propose a new network model with the addition of Batch normalization and Global average pooling to VGG16, which is a pre-trained model. In the experiment, accuracy=98.29%, TPR=98.96% and FPR=7.43% classification accuracy was obtained.

DOI： 10.23919/ICCAS50221.2020.9268275

Scopus

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