Dr. Naoki Kamiya Profile

Dr. Naoki Kamiya

Associate Professor at Aichi Prefectural Univ

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Publications (8)

Proceedings Article | 26 March 2023 Paper

Initial study of fiber estimation in macro-images of paper based on patch-based classification using two-stage EfficientNet

Yu Yoshizato, Yexin Zhou, Yoichi Ohyanagi, Akiko Iwata, Koji Shibazaki, Naoki Kamiya

Proceedings Volume 12592, 125921J (2023) https://doi.org/10.1117/12.2666711

KEYWORDS: Image classification, Digital cameras, Image analysis, Cultural studies, Cross validation

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Proceedings Article | 1 May 2022 Paper

Automated segmentation of oblique abdominal muscle based on body cavity segmentation in torso CT images using U-Net

N. Kamiya, X. Zhou, H. Kato, T. Hara, H. Fujita

Proceedings Volume 12177, 121771V (2022) https://doi.org/10.1117/12.2624316

KEYWORDS: Image segmentation, Computed tomography, Medicine, Tomography, Tissues, Intestine, Information technology, Image processing, Bone, Visualization

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Proceedings Article | 20 April 2021 Paper

Relationship between number of annotations and accuracy in segmentation of the erector spinae muscle using Bayesian U-Net in torso CT images

Yuichi Wakamatsu, Naoki Kamiya, Xiangrong Zhou, Takeshi Hara, Hiroshi Fujita

Proceedings Volume 11792, 1179207 (2021) https://doi.org/10.1117/12.2590780

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Proceedings Article | 27 March 2019 Paper

Investigation of the effect of image resolution on automatic classification of mammary gland density in mammography images using deep learning

Ami Oshima, Norimitsu Shinohara, Naoki Kamiya

Proceedings Volume 11050, 1105018 (2019) https://doi.org/10.1117/12.2521255

KEYWORDS: Mammary gland, Image resolution, Image classification, Mammography, Breast, Breast cancer, Visualization, Convolution, Ultrasonography, Edge detection

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Proceedings Article | 27 March 2019 Paper

Initial study on the classification of amyotrophic diseases using texture analysis and deep learning in whole-body CT images

N. Kamiya, A. Oshima, E. Asano, X. Zhou, M. Yamada, H. Kato, C. Muramatsu, T. Hara, T. Miyoshi, M. Matsuo, H. Fujita

Proceedings Volume 11050, 110500X (2019) https://doi.org/10.1117/12.2518199

KEYWORDS: Computed tomography, Statistical analysis, Image classification, Inspection, Radiology, Visualization, Computer aided diagnosis and therapy, Analytical research, Information science, Information technology

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Proceedings Article | 3 March 2017 Paper

Automated analysis of whole skeletal muscle for muscular atrophy detection of ALS in whole-body CT images: preliminary study

Naoki Kamiya, Kosuke Ieda, Xiangrong Zhou, Megumi Yamada, Hiroki Kato, Chisako Muramatsu, Takeshi Hara, Toshiharu Miyoshi, Takashi Inuzuka, Masayuki Matsuo, Hiroshi Fujita

Proceedings Volume 10134, 1013442 (2017) https://doi.org/10.1117/12.2251584

KEYWORDS: Computed tomography, Computer aided diagnosis and therapy, Solid modeling, Diagnostics, Inspection, Image segmentation, Visualization, Computer aided design, Abdomen, Lung, 3D modeling, Medicine, Radiology

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Amyotrophic lateral sclerosis (ALS) causes functional disorders such as difficulty in breathing and swallowing through the atrophy of voluntary muscles. ALS in its early stages is difficult to diagnose because of the difficulty in differentiating it from other muscular diseases. In addition, image inspection methods for aggressive diagnosis for ALS have not yet been established. The purpose of this study is to develop an automatic analysis system of the whole skeletal muscle to support the early differential diagnosis of ALS using whole-body CT images. In this study, the muscular atrophy parts including ALS patients are automatically identified by recognizing and segmenting whole skeletal muscle in the preliminary steps. First, the skeleton is identified by its gray value information. Second, the initial area of the body cavity is recognized by the deformation of the thoracic cavity based on the anatomical segmented skeleton. Third, the abdominal cavity boundary is recognized using ABM for precisely recognizing the body cavity. The body cavity is precisely recognized by non-rigid registration method based on the reference points of the abdominal cavity boundary. Fourth, the whole skeletal muscle is recognized by excluding the skeleton, the body cavity, and the subcutaneous fat. Additionally, the areas of muscular atrophy including ALS patients are automatically identified by comparison of the muscle mass. The experiments were carried out for ten cases with abnormality in the skeletal muscle. Global recognition and segmentation of the whole skeletal muscle were well realized in eight cases. Moreover, the areas of muscular atrophy including ALS patients were well identified in the lower limbs. As a result, this study indicated the basic technology to detect the muscle atrophy including ALS. In the future, it will be necessary to consider methods to differentiate other kinds of muscular atrophy as well as the clinical application of this detection method for early ALS detection and examine a large number of cases with stage and disease type.

Proceedings Article | 24 March 2016 Paper

Automated recognition of the iliac muscle and modeling of muscle fiber direction in torso CT images

N. Kamiya, X. Zhou, K. Azuma, C. Muramatsu, T. Hara, H. Fujita

Proceedings Volume 9785, 97853K (2016) https://doi.org/10.1117/12.2214613

KEYWORDS: Computed tomography, Databases, Data modeling, Visualization, Image segmentation, CAD systems, Laser induced plasma spectroscopy, Feature extraction, Intestine, Medicine, Solid modeling, Quantitative analysis

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Proceedings Article | 9 March 2010 Paper

A model based method for recognizing psoas major muscles in torso CT images

Naoki Kamiya, Xiangrong Zhou, Huayue Chen, Takeshi Hara, Ryujiro Yokoyama, Masayuki Kanematsu, Hiroaki Hoshi, Hiroshi Fujita

Proceedings Volume 7624, 76241X (2010) https://doi.org/10.1117/12.843758

KEYWORDS: X-ray computed tomography, Medicine, Radiology, X-ray imaging, X-rays, Computer aided diagnosis and therapy, Process modeling, Databases, Visualization, Data modeling

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Showing 5 of 8 publications

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