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

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자료유형: 학술대회자료

저자정보: Mitsuaki NAGAO (Kyushu Institute of Technology) Noriaki MIYAKE (Kyushu Institute of Technology) Yuriko YOSHINO (Kyushu Institute of Technology) Huimin LU (Kyushu Institute of Technology) Hyoungseop KIM (Kyushu Institute of Technology) Seiichi MURAKAMI (University of Occupational and Environmental Health) Takatoshi AOKI (University of Occupational and Environmental Health) Shoji KIDO (Yamaguchi University)

저널정보: 제어로봇시스템학회 제어로봇시스템학회 국제학술대회 논문집 ICCAS 2018

발행연도: 2018.10

수록면: 1,333 - 1,337 (5page)

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이 논문의 연구 히스토리 (2)

2018

Detection of Abnormal Shadows on Temporal Subtraction Images Based on Multi-phase CNN

Mitsuaki NAGAO , No , iaki MIYAKE 외 7명 제어로봇시스템학회 국제학술대회 논문집 2018.10 학술대회자료

2017

Detection of Abnormal Candidate Regions on Temporal Subtraction Images Based on DCNN

Mitsuaki NAGAO , No , iaki MIYAKE 외 9명 제어로봇시스템학회 국제학술대회 논문집 2017.10 학술대회자료

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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.

#Computer Aided Diagnosis #Temporal Subtraction Technique #Deep Leaning

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