3D prostate segmentation of ultrasound images combining longitudinal image registration and machine learning

Xiaofeng Yang, Baowei Fei

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

35 Scopus citations

Abstract

We developed a three-dimensional (3D) segmentation method for transrectal ultrasound (TRUS) images, which is based on longitudinal image registration and machine learning. Using longitudinal images of each individual patient, we register previously acquired images to the new images of the same subject. Three orthogonal Gabor filter banks were used to extract texture features from each registered image. Patient-specific Gabor features from the registered images are used to train kernel support vector machines (KSVMs) and then to segment the newly acquired prostate image. The segmentation method was tested in TRUS data from five patients. The average surface distance between our and manual segmentation is 1.18 ± 0.31 mm, indicating that our automatic segmentation method based on longitudinal image registration is feasible for segmenting the prostate in TRUS images.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2012
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
DOIs
StatePublished - May 1 2012
Externally publishedYes
EventMedical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling - San Diego, CA, United States
Duration: Feb 5 2012Feb 7 2012

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume8316
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling
CountryUnited States
CitySan Diego, CA
Period2/5/122/7/12

Keywords

  • Transrectal ultrasound (TRUS)
  • image registration
  • image segmentation
  • machine learning
  • prostate cancer
  • support vector machine (SVM)

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Yang, X., & Fei, B. (2012). 3D prostate segmentation of ultrasound images combining longitudinal image registration and machine learning. In Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling [83162O] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 8316). https://doi.org/10.1117/12.912188