The Level Set Method and Its Applications in Medical Image Analysis.
Pang, Jincheng.
2015
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Abstract: Medical
images are collected via a broad assortment of modalities with the objective of studying
structures ranging from cells to tissues, to entire organs for applications as diverse
as elucidating disease mechanisms, understanding the effectiveness of treatment options,
as well as drug discovery. In these applications, clinical relevant information needs to
be extract from large a... read moremounts of data for statistical analysis. However, approaches
based on human-only interaction with the data suffer from a number of problems including
the quantity of effort and time required to process the large data sets as well as the
the related problem of inconsistency of the analysis due to human error. The use of well
designed computer-based methods for processing these data addresses both of these
issues. First, with the developments of hardware and computational techniques, more
sophisticated and powerful algorithms can be applied to larger data sets. Second,
results based on algorithms are always reproducible given the specification of
parameters defining the operation of these methods. Although humans are still needed for
works such as algorithm selection and parameter tuning, repetitive manual effort can be
significantly reduced. In this thesis, we present algorithms using level set methods for
two representative applications of medical image analysis: analysis of magnetic
resonance (MR) images of the human knee to quantify osteoarthritis and analysis of phase
contrast microscopy (PCM) neuron images for Ivermectin effect evaluation. For the MR
knee images, we propose a new coupled prior shape model which incorporates prior shape
information and also the relative position information for multiple objects. In
addition, our new shape model implicitly puts on more constraints in areas with less
shape differences while conventional shape models always impose constraints uniformly on
the whole shape domain. Moreover, an edge based force incorporating directional
information is also introduced. Segmentation results on the real MR knee images
demonstrate the feasibility of our new coupled prior shape model and also the
directional edge-based force. Based on the combination of our coupled prior shape model
and the directional edge-based force, we can reduce the manual interaction time
significantly for bone marrow lesions segmentation without sacrificing accuracy. For the
PCM neuron images, we introduce a parametric image model using the level set framework
to represent images to be restored and segmented. Moreover, we formulate an optimization
problem which merges image restoration and segmentation and give its solutions. Based on
the above segmentation method, we propose a pipeline which is automatic and the first to
segment somas and trace dendrites simultaneously for the PCM neuron images. Results for
both the synthetic and real images validate and demonstrate the advantages of our
approach.
Thesis (Ph.D.)--Tufts University, 2015.
Submitted to the Dept. of Electrical Engineering.
Advisor: Eric Miller.
Keywords: Electrical engineering, Biomedical engineering, and Medical imaging and radiology.read less - ID:
- 0p096k55r
- Component ID:
- tufts:21499
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- DCA Citation Guide EndNote
