3 research outputs found
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Segmentation and quantitative evaluation of brain MRI data with a multi-phase three-dimensional implicit deformable model
Segmentation of three-dimensional anatomical brain images into tissue classes has applications in both clinical and research settings. This paper presents the implementation and quantitative evaluation of a four-phase three-dimensional active contour implemented with a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to speed up numerical computation and avoid the need for a priori information. This random initialization ensures robustness of the method to variation of user expertise, biased a priori information and errors in input information that could be influenced by variations in image quality. Experimentation on three MRI brain data sets showed that an optimal partitioning successfully labeled regions that accurately identified white matter, gray matter and cerebrospinal fluid in the ventricles. Quantitative evaluation of the segmentation was performed with comparison to manually labeled data and computed false positive and false negative assignments of voxels for the three organs. We report high accuracy for the two comparison cases. These results demonstrate the efficiency and flexibility of this segmentation framework to perform the challenging task of automatically extracting brain tissue volume contours
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Brain MRI Segmentation with Multiphase Minimal Partitioning: A Comparative Study
This paper presents the implementation and quantitative evaluation
of a multiphase three-dimensional deformable model in a level set
framework for automated segmentation of brain MRIs. The
segmentation algorithm performs an optimal partitioning of
three-dimensional data based on homogeneity measures that
naturally evolves to the extraction of different tissue types in
the brain. Random seed initialization was used to minimize the
sensitivity of the method to initial conditions while avoiding the
need for a priori information. This random initialization
ensures robustness of the method with respect to the
initialization and the minimization set up. Postprocessing
corrections with morphological operators were applied to refine
the details of the global segmentation method. A clinical study
was performed on a database of 10 adult brain MRI volumes to
compare the level set segmentation to three other methods:
“idealized” intensity thresholding, fuzzy connectedness, and an
expectation maximization classification using hidden Markov random
fields. Quantitative evaluation of segmentation accuracy was
performed with comparison to manual segmentation computing true
positive and false positive volume fractions. A statistical
comparison of the segmentation methods was performed through a
Wilcoxon analysis of these error rates and results showed very
high quality and stability of the multiphase three-dimensional
level set method
Improving cancer subtype diagnosis and grading using clinical decision support system based on computer-aided tissue image analysis
This research focuses towards the development of a clinical decision support system (CDSS) based on cellular and tissue image analysis and classification system that improves consistency and facilitates the clinical decision making process. In a typical cancer examination, pathologists make diagnosis by manually reading morphological features in patient biopsy images, in which cancer biomarkers are highlighted by using different staining techniques. This process is subjected to pathologist's training and experience, especially when the same cancer has several subtypes (i.e. benign tumor subtype vs. malignant subtype) and the same cancer tissue biopsy contains heterogeneous morphologies in different locations. The variability in pathologist's manual reading may result in varying cancer diagnosis and treatment.
This Ph.D. research aims to reduce the subjectivity and variation existing in traditional histo-pathological reading of patient tissue biopsy slides through Computer-Aided Diagnosis (CAD). Using the CAD, quantitative molecular profiling of cancer biomarkers of stained biopsy images are obtained by extracting and analyzing texture and cellular structure features. In addition, cancer sub-type classification and a semi-automatic grade scoring (i.e. clinical decision making) for improved consistency over a large number of cancer subtype images can be performed. The CAD tools do have their own limitations and in certain cases the clinicians, however, prefer systems which are flexible and take into account their individuality when necessary by providing some control rather than fully automated system. Therefore, to be able to introduce CDSS in health care, we need to understand users' perspectives and preferences on the new information technology. This forms as the basis for this research where we target to present the quantitative information acquired through the image analysis, annotate the images and provide suitable visualization which can facilitate the process of decision making in a clinical setting.PhDCommittee Chair: Dr. May D. Wang; Committee Member: Dr. Andrew N. Young; Committee Member: Dr. Anthony J. Yezzi; Committee Member: Dr. Edward J. Coyle; Committee Member: Dr. Paul Benkese