A Liver Segmentation Algorithm with Interactive Error Correction for Abdominal CT Images: A Preliminary Study

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

Abstract

An automatic method for segmenting the liver from the portal venous phase of abdominal CT images using the K-Means clustering method is described in this paper. We have incorporated an interactive technique for correcting the errors in the liver segmentation results using power law transformation. The proposed method was validated on abdominal CT volumes of fifteen patients obtained from Kasturba Medical College, Manipal. The average values of the various standard evaluation metrics obtained are as follows: Dice coefficient = 0.9361, Jaccard index = 0.8805, volumetric overlap error = 0.1195, absolute volume difference = 4.048%, average symmetric surface distance = 1.7282 mm and maximum symmetric surface distance = 38.039 mm. The quantitative and qualitative results obtained in our preliminary work show that the K-Means clustering technique along with power law transformation is effective in producing good liver segmentation outputs. As future work, we will attempt to automate the power law transformation technique.

Original languageEnglish
Title of host publicationComputational Intelligence in Data Science - 4th IFIP TC 12 International Conference, ICCIDS 2021, Revised Selected Papers
EditorsVallidevi Krishnamurthy, Suresh Jaganathan, Kanchana Rajaram, Saraswathi Shunmuganathan
PublisherSpringer Science and Business Media Deutschland GmbH
Pages132-140
Number of pages9
ISBN (Print)9783030925994
DOIs
Publication statusPublished - 2021
Event4th IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2021 - Virtual, Online
Duration: 18-03-202120-03-2021

Publication series

NameIFIP Advances in Information and Communication Technology
Volume611 IFIPAICT
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X

Conference

Conference4th IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2021
CityVirtual, Online
Period18-03-2120-03-21

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management

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