A 3-D liver segmentation method with parallel computing for selective internal radiation therapy

Mohammed Goryawala, Magno R. Guillen, Mercedes Cabrerizo, Armando Barreto, Seza Gulec, Tushar C. Barot, Rekha R. Suthar, Ruchir N. Bhatt, Anthony McGoron, Malek Adjouadi

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

This study describes a new 3-D liver segmentation method in support of the selective internal radiation treatment as a treatment for liver tumors. This 3-D segmentation is based on coupling a modified k-means segmentation method with a special localized contouring algorithm. In the segmentation process, five separate regions are identified on the computerized tomography image frames. The merit of the proposed method lays in its potential to provide fast and accurate liver segmentation and 3-D rendering as well as in delineating tumor region(s), all with minimal user interaction. Leveraging of multicore platforms is shown to speed up the processing of medical images considerably, making this method more suitable in clinical settings. Experiments were performed to assess the effect of parallelization using up to 442 slices. Empirical results, using a single workstation, show a reduction in processing time from 4.5 h to almost 1 h for a 78% gain. Most important is the accuracy achieved in estimating the volumes of the liver and tumor region(s), yielding an average error of less than 2% in volume estimation over volumes generated on the basis of the current manually guided segmentation processes. Results were assessed using the analysis of variance statistical analysis.

Original languageEnglish (US)
Article number06041030
Pages (from-to)62-69
Number of pages8
JournalIEEE Transactions on Information Technology in Biomedicine
Volume16
Issue number1
DOIs
StatePublished - Jan 2012
Externally publishedYes

Keywords

  • 3-Dreconstruction
  • Image segmentation
  • K-means algorithm
  • Liver segmentation
  • MATLAB
  • Parallel computing

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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