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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationInternational Forum on Medical Imaging in Asia 2019
EditorsHiroshi Fujita, Jong Hyo Kim, Feng Lin
PublisherSPIE
ISBN (Electronic)9781510627758
DOIs
Publication statusPublished - 2019 Jan 1
EventInternational Forum on Medical Imaging in Asia 2019 - Singapore, Singapore
Duration: 2019 Jan 72019 Jan 9

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11050
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceInternational Forum on Medical Imaging in Asia 2019
CountrySingapore
CitySingapore
Period19-01-0719-01-09

Fingerprint

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

Cite this

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. In H. Fujita, J. H. Kim, & F. Lin (Eds.), International Forum on Medical Imaging in Asia 2019 [1105011] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 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. editor / Hiroshi Fujita ; Jong Hyo Kim ; Feng Lin. SPIE, 2019. (Proceedings of SPIE - The International Society for Optical Engineering).
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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.",
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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. in H Fujita, JH Kim & F Lin (eds), International Forum on Medical Imaging in Asia 2019., 1105011, Proceedings of SPIE - The International Society for Optical Engineering, vol. 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. 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 proceedingConference contribution

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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. In Fujita H, Kim JH, Lin F, editors, 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