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 Juei Yang, Yu Hua Fang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


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
ISBN (Electronic)9781510627758
StatePublished - 2019
Externally publishedYes
EventInternational Forum on Medical Imaging in Asia 2019 - Singapore, Singapore
Duration: 07 01 201909 01 2019

Publication series

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


ConferenceInternational Forum on Medical Imaging in Asia 2019

Bibliographical note

Publisher Copyright:
© 2019 SPIE.


  • Liver segmentation
  • arterial phase
  • delay phase and portal venous phase of CT images
  • non-contrast phase
  • support vector machine algorithm


Dive into the research topics of 'Computer-aided liver cirrhosis diagnosis via automatic liver segmentation and machine learning algorithm'. Together they form a unique fingerprint.

Cite this