A new prostate segmentation approach using multispectral magnetic resonance imaging and a statistical pattern classifier

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

    8 Citations (Scopus)
    13 Downloads (Pure)

    Abstract

    Prostate segmentation is essential for calculating prostate volume, creating patient-specific prostate anatomical models and image fusion. Automatic segmentation methods are preferable because manual segmentation is timeconsuming and highly subjective. Most of the currently available segmentation methods use a priori knowledge of the prostate shape. However, there is a large variation in prostate shape between patients. Our approach uses multispectral magnetic resonance imaging (MRI) data, containing T1, T2 and proton density (PD) weighted images and the distance from the voxel to the centroid of the prostate, together with statistical pattern classifiers. We investigated the performance of a parametric and a non-parametric classification approach by applying a Baysian-quadratic and a k-nearest-neighbor classifier respectively. An annotated data set is made by manual labeling of the image. Using this data set, the classifiers are trained and evaluated. sThe following results are obtained after three experiments. Firstly, using feature selection we showed that the average segmentation error rates are lowest when combining all three images and the distance with the k-nearest-neighbor classifier. Secondly, the confusion matrix showed that the k-nearest-neighbor classifier has the sensitivity. Finally, the prostate is segmented using both classifier. The segmentation boundaries approach the prostate boundaries for most slices. However, in some slices the segmentation result contained errors near the borders of the prostate. The current results showed that segmenting the prostate using multispectral MRI data combined with a statistical classifier is a promising method.
    Original languageEnglish
    Title of host publicationMedical Imaging 2012
    Subtitle of host publicationImage Processing
    EditorsDavid R. Haynor, Sébastien Ourselin
    Place of PublicationBellingham, WA
    PublisherSPIE
    Number of pages9
    ISBN (Print)9780819489630
    DOIs
    Publication statusPublished - 13 Feb 2012
    EventSPIE Medical Imaging 2012 - Town & Country Resort and Convention Center, San Diego, United States
    Duration: 4 Feb 20129 Feb 2012

    Publication series

    NameProceedings of SPIE
    PublisherSPIE
    Volume8314
    ISSN (Print)1605-7422

    Conference

    ConferenceSPIE Medical Imaging 2012
    CountryUnited States
    CitySan Diego
    Period4/02/129/02/12

    Keywords

    • METIS-286333
    • Magnetic resonance imaging
    • Bayes-quadratic classifier
    • IR-80234
    • EWI-21780
    • Prostate cancer
    • Prostate segmentation
    • statistical pattern classification
    • k-nearest neighbor classifier
    • multispectral MRI

    Fingerprint Dive into the research topics of 'A new prostate segmentation approach using multispectral magnetic resonance imaging and a statistical pattern classifier'. Together they form a unique fingerprint.

    Cite this