A Multi-phase, Probabilistic Approach To
Image Segmentation In MRI And CT Studies
Author(s)
J. L. Foo & E. Winer
Abstract
This paper presents an approach for segmentation of digital medical images
using a multi-phase probabilistic approach. The first phase of the approach is
enhancement of the image data using an unsharp mask sharpening algorithm.
This vastly improved the clarity of the images prior to segmentation. The
segmentation of an object within the image is then achieved through two more
phases. The first phase is a thresholding process, where each pixel is scored
based on a similarity criterion for a chosen seed point. Pixels with a score
satisfying a required minimum are selected into the segmented region. A second
phase is then instituted for pixels not initially selected. This phase involves a
probability selection, based on a Monte Carlo simulation. A probability for each
pixel is formulated based on the pixel’s intensity and location. This probability
is them compared against a random probability to determine if the pixel is
included in the segmented region. To facilitate the processing of multiple image
slices, an automated relocation algorithm was developed to move determined
seed points from image to image as the shape, size, or location of the object (i.e.
organ) changes. The concepts and techniques developed were tested on three
separate medical studies with one being shown in this paper. The results showed
that the image enhancement process outlined features and details within the data
that were not previously apparent. The segmentation process extracted the
desired object more completely when compared to other segmentation
techniques.
Keywords: image segmentation, probabilistic, region growing, thresholding,
Monte Carlo, medical imaging, DICOM.
Keywords
image segmentation, probabilistic, region growing, thresholding,
Monte Carlo, medical imaging, DICOM.
Related Book
Other papers in this volume
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