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

Xiaofeng Yang; Baowei Fei

doi:10.1117/12.912188

17 February 2012 3D prostate segmentation of ultrasound images combining longitudinal image registration and machine learning

Xiaofeng Yang, Baowei Fei

Author Affiliations +

Proceedings Volume 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling; 83162O (2012) https://doi.org/10.1117/12.912188
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

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.

Citation Download Citation

Xiaofeng Yang and Baowei Fei "3D prostate segmentation of ultrasound images combining longitudinal image registration and machine learning", Proc. SPIE 8316, Medical Imaging 2012: Image-Guided Procedures, Robotic Interventions, and Modeling, 83162O (17 February 2012); https://doi.org/10.1117/12.912188

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