کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
525787 869025 2013 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Simultaneous segmentation of prostatic zones using Active Appearance Models with multiple coupled levelsets
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Simultaneous segmentation of prostatic zones using Active Appearance Models with multiple coupled levelsets
چکیده انگلیسی


• Landmark free Active Appearance Model can segment multiple objects simultaneously.
• Prostate, central gland, peripheral zone segmented with dice = .81, .79, .68.
• Hierarchical system uses existing segmentations to drive other segmentations.

In this work we present an improvement to the popular Active Appearance Model (AAM) algorithm, that we call the Multiple-Levelset AAM (MLA). The MLA can simultaneously segment multiple objects, and makes use of multiple levelsets, rather than anatomical landmarks, to define the shapes. AAMs traditionally define the shape of each object using a set of anatomical landmarks. However, landmarks can be difficult to identify, and AAMs traditionally only allow for segmentation of a single object of interest. The MLA, which is a landmark independent AAM, allows for levelsets of multiple objects to be determined and allows for them to be coupled with image intensities. This gives the MLA the flexibility to simulataneously segmentation multiple objects of interest in a new image.In this work we apply the MLA to segment the prostate capsule, the prostate peripheral zone (PZ), and the prostate central gland (CG), from a set of 40 endorectal, T2-weighted MRI images. The MLA system we employ in this work leverages a hierarchical segmentation framework, so constructed as to exploit domain specific attributes, by utilizing a given prostate segmentation to help drive the segmentations of the CG and PZ, which are embedded within the prostate. Our coupled MLA scheme yielded mean Dice accuracy values of .81, .79 and .68 for the prostate, CG, and PZ, respectively using a leave-one-out cross validation scheme over 40 patient studies. When only considering the midgland of the prostate, the mean DSC values were .89, .84, and .76 for the prostate, CG, and PZ respectively.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Vision and Image Understanding - Volume 117, Issue 9, September 2013, Pages 1051–1060
نویسندگان
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