Tumor Subtype-Specific Parameter Optimization in a Hybrid Active Surface Model for Hepatic Tumor Segmentation of 3D Liver Ultrasonograms

  • Myungeun Lee
  • Jong Hyo Kim
  • Moon Ho Park
  • Ye-Hoon Kim
  • Yeong Kyeong Seong
  • Junghoe Kim
  • Baek Hwan Cho
  • Sinsang Yu
  • Kyoung-Gu Woo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8198)

Abstract

Segmentation of hepatic tumors is a clinically demanding task for improving reliability in diagnosis and treatment procedures, and yet remains a challenging problem due to their highly noisy, low contrast, and blurry imaging nature. However, once correctly segmented, the shape and volume information of a tumor may provide useful information for radiological decision making. In this study, we propose an active surface model. The model combines edge, region, and contour smoothness energies. We extracted qualitative appearance features from three hepatic tumor subtypes and use them to adjust the weights of the energy terms in order to determine an optimized set of parameters for each tumor subtype. The performance of the developed method was evaluated with a dataset of 60 cases including 18 hepatic simple cysts, 18 hemangiomas, and 24 hepatocellular carcinomas, as determined by the radiologist’s visual assessment. Evaluation of the results showed that our proposed method produced tumor boundaries that were equal to or better than acceptable in 87% of cases.

Keywords

Tumor segmentation active surface model liver ultrasound 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Myungeun Lee
    • 1
  • Jong Hyo Kim
    • 2
    • 3
  • Moon Ho Park
    • 4
  • Ye-Hoon Kim
    • 4
  • Yeong Kyeong Seong
    • 4
  • Junghoe Kim
    • 4
  • Baek Hwan Cho
    • 4
  • Sinsang Yu
    • 4
  • Kyoung-Gu Woo
    • 4
  1. 1.Medical Research CenterSeoul National UniversitySeoulKorea
  2. 2.Department of RadiologySeoul National University College of MedicineSeoulKorea
  3. 3.Department of Transdisciplinary Studies, Graduate School of Convergence Science and TechnologySeoul National UniversitySuwonKorea
  4. 4.Data Analytics Group, Future IT Laboratories, Samsung Advanced Institute of TechnologySamsung ElectronicsYongin-SiKorea

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