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 am... read moreounts 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
