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.
| Original language | English |
|---|---|
| Title of host publication | International Forum on Medical Imaging in Asia 2019 |
| Editors | Hiroshi Fujita, Jong Hyo Kim, Feng Lin |
| Publisher | SPIE |
| ISBN (Electronic) | 9781510627758 |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
| Event | International Forum on Medical Imaging in Asia 2019 - Singapore, Singapore Duration: 2019 Jan 7 → 2019 Jan 9 |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Volume | 11050 |
| ISSN (Print) | 0277-786X |
| ISSN (Electronic) | 1996-756X |
Conference
| Conference | International Forum on Medical Imaging in Asia 2019 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 19-01-07 → 19-01-09 |
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All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering
Cite this
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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. ed. / Hiroshi Fujita; Jong Hyo Kim; Feng Lin. SPIE, 2019. 1105011 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11050).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Computer-aided liver cirrhosis diagnosis via automatic liver segmentation and machine learning algorithm
AU - Su, Ting Yu
AU - Yang, Wei Tse
AU - Cheng, Tsu Chi
AU - He, Yi Fei
AU - Yang, Ching-Jui
AU - Fang, Yu-Hua Dean
PY - 2019/1/1
Y1 - 2019/1/1
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.
UR - http://www.scopus.com/inward/record.url?scp=85063862455&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063862455&partnerID=8YFLogxK
U2 - 10.1117/12.2521631
DO - 10.1117/12.2521631
M3 - Conference contribution
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - International Forum on Medical Imaging in Asia 2019
A2 - Fujita, Hiroshi
A2 - Kim, Jong Hyo
A2 - Lin, Feng
PB - SPIE
ER -