Computer-aided liver cirrhosis diagnosis via automatic liver segmentation and machine learning algorithm

Ting Yu Su, Wei Tse Yang, Tsu Chi Cheng, Yi Fei He, Ching-Jui Yang, Yu-Hua Dean Fang

研究成果: Conference contribution

摘要

In this paper, a new computer-aided diagnosis system is proposed to automatically diagnose liver cirrhosis based on fourphases CT images, which included non-contrast phase, arterial phase, delay phase and portal venous phase. It is developed for the purpose of discriminating the cirrhosis into mild or severe level by automatic liver segmentation method and classification method using machine learning algorithm. First, the gradient-inverse map of CT images are calculated to derive the relative-smooth features in local area. Then we compared the centroid and area of each binary labeled groups through each slice to quantitatively extract the volume of interest (VOI) of liver automatically. In classification step, some first-order features and texture features are calculated to describe the intensity representation of liver parenchyma. Some parameters are also used to quantify the distribution of intensity in VOI. By the way, we also quantified the shape of VOI and derived some structural features. Finally, the trained support vector machine (SVM) and Neural Network (NN) classifier is applied to classify the subjects into clinical stages of the liver cirrhosis.

原文English
主出版物標題International Forum on Medical Imaging in Asia 2019
編輯Hiroshi Fujita, Jong Hyo Kim, Feng Lin
發行者SPIE
ISBN(電子)9781510627758
DOIs
出版狀態Published - 2019 一月 1
事件International Forum on Medical Imaging in Asia 2019 - Singapore, Singapore
持續時間: 2019 一月 72019 一月 9

出版系列

名字Proceedings of SPIE - The International Society for Optical Engineering
11050
ISSN(列印)0277-786X
ISSN(電子)1996-756X

Conference

ConferenceInternational Forum on Medical Imaging in Asia 2019
國家Singapore
城市Singapore
期間19-01-0719-01-09

指紋

machine learning
liver
Liver
Learning algorithms
learning
Learning systems
Learning Algorithm
Machine Learning
Segmentation
CT Image
Computer aided diagnosis
Computer-aided Diagnosis
Texture Feature
Centroid
classifiers
Slice
centroids
Support vector machines
Support Vector Machine
Quantify

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

引用此文

Su, T. Y., Yang, W. T., Cheng, T. C., He, Y. F., Yang, C-J., & Fang, Y-H. D. (2019). Computer-aided liver cirrhosis diagnosis via automatic liver segmentation and machine learning algorithm. 於 H. Fujita, J. H. Kim, & F. Lin (編輯), International Forum on Medical Imaging in Asia 2019 [1105011] (Proceedings of SPIE - The International Society for Optical Engineering; 卷 11050). SPIE. https://doi.org/10.1117/12.2521631
Su, Ting Yu ; Yang, Wei Tse ; Cheng, Tsu Chi ; He, Yi Fei ; Yang, Ching-Jui ; Fang, Yu-Hua Dean. / Computer-aided liver cirrhosis diagnosis via automatic liver segmentation and machine learning algorithm. International Forum on Medical Imaging in Asia 2019. 編輯 / Hiroshi Fujita ; Jong Hyo Kim ; Feng Lin. SPIE, 2019. (Proceedings of SPIE - The International Society for Optical Engineering).
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title = "Computer-aided liver cirrhosis diagnosis via automatic liver segmentation and machine learning algorithm",
abstract = "In this paper, a new computer-aided diagnosis system is proposed to automatically diagnose liver cirrhosis based on fourphases CT images, which included non-contrast phase, arterial phase, delay phase and portal venous phase. It is developed for the purpose of discriminating the cirrhosis into mild or severe level by automatic liver segmentation method and classification method using machine learning algorithm. First, the gradient-inverse map of CT images are calculated to derive the relative-smooth features in local area. Then we compared the centroid and area of each binary labeled groups through each slice to quantitatively extract the volume of interest (VOI) of liver automatically. In classification step, some first-order features and texture features are calculated to describe the intensity representation of liver parenchyma. Some parameters are also used to quantify the distribution of intensity in VOI. By the way, we also quantified the shape of VOI and derived some structural features. Finally, the trained support vector machine (SVM) and Neural Network (NN) classifier is applied to classify the subjects into clinical stages of the liver cirrhosis.",
author = "Su, {Ting Yu} and Yang, {Wei Tse} and Cheng, {Tsu Chi} and He, {Yi Fei} and Ching-Jui Yang and Fang, {Yu-Hua Dean}",
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Su, TY, Yang, WT, Cheng, TC, He, YF, Yang, C-J & Fang, Y-HD 2019, Computer-aided liver cirrhosis diagnosis via automatic liver segmentation and machine learning algorithm. 於 H Fujita, JH Kim & F Lin (編輯), International Forum on Medical Imaging in Asia 2019., 1105011, Proceedings of SPIE - The International Society for Optical Engineering, 卷 11050, SPIE, International Forum on Medical Imaging in Asia 2019, Singapore, Singapore, 19-01-07. https://doi.org/10.1117/12.2521631

Computer-aided liver cirrhosis diagnosis via automatic liver segmentation and machine learning algorithm. / Su, Ting Yu; Yang, Wei Tse; Cheng, Tsu Chi; He, Yi Fei; Yang, Ching-Jui; Fang, Yu-Hua Dean.

International Forum on Medical Imaging in Asia 2019. 編輯 / Hiroshi Fujita; Jong Hyo Kim; Feng Lin. SPIE, 2019. 1105011 (Proceedings of SPIE - The International Society for Optical Engineering; 卷 11050).

研究成果: Conference contribution

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AU - Yang, Ching-Jui

AU - Fang, Yu-Hua Dean

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N2 - In this paper, a new computer-aided diagnosis system is proposed to automatically diagnose liver cirrhosis based on fourphases CT images, which included non-contrast phase, arterial phase, delay phase and portal venous phase. It is developed for the purpose of discriminating the cirrhosis into mild or severe level by automatic liver segmentation method and classification method using machine learning algorithm. First, the gradient-inverse map of CT images are calculated to derive the relative-smooth features in local area. Then we compared the centroid and area of each binary labeled groups through each slice to quantitatively extract the volume of interest (VOI) of liver automatically. In classification step, some first-order features and texture features are calculated to describe the intensity representation of liver parenchyma. Some parameters are also used to quantify the distribution of intensity in VOI. By the way, we also quantified the shape of VOI and derived some structural features. Finally, the trained support vector machine (SVM) and Neural Network (NN) classifier is applied to classify the subjects into clinical stages of the liver cirrhosis.

AB - In this paper, a new computer-aided diagnosis system is proposed to automatically diagnose liver cirrhosis based on fourphases CT images, which included non-contrast phase, arterial phase, delay phase and portal venous phase. It is developed for the purpose of discriminating the cirrhosis into mild or severe level by automatic liver segmentation method and classification method using machine learning algorithm. First, the gradient-inverse map of CT images are calculated to derive the relative-smooth features in local area. Then we compared the centroid and area of each binary labeled groups through each slice to quantitatively extract the volume of interest (VOI) of liver automatically. In classification step, some first-order features and texture features are calculated to describe the intensity representation of liver parenchyma. Some parameters are also used to quantify the distribution of intensity in VOI. By the way, we also quantified the shape of VOI and derived some structural features. Finally, the trained support vector machine (SVM) and Neural Network (NN) classifier is applied to classify the subjects into clinical stages of the liver cirrhosis.

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Su TY, Yang WT, Cheng TC, He YF, Yang C-J, Fang Y-HD. Computer-aided liver cirrhosis diagnosis via automatic liver segmentation and machine learning algorithm. 於 Fujita H, Kim JH, Lin F, 編輯, International Forum on Medical Imaging in Asia 2019. SPIE. 2019. 1105011. (Proceedings of SPIE - The International Society for Optical Engineering). https://doi.org/10.1117/12.2521631