The use of atlases has been shown to be a robust method for segmentation of medical images. In this paper we explore
different methods of selection of atlases for the segmentation of the quadriceps muscles in magnetic resonance (MR)
images, although the results are pertinent for a wide range of applications. The experiments were performed using 103
images from the Osteoarthritis Initiative (OAI). The images were randomly split into a training set consisting of 50
images and a testing set of 53 images. Three different atlas selection methods were systematically compared. First, a set
of readers was assigned the task of selecting atlases from a training population of images, which were selected to be
representative subgroups of the total population. Second, the same readers were instructed to select atlases from a subset
of the training data which was stratified based on population modes. Finally, every image in the training set was
employed as an atlas, with no input from the readers, and the atlas which had the best initial registration, judged by an
appropriate registration metric, was used in the final segmentation procedure. The segmentation results were quantified
using the Zijdenbos similarity index (ZSI). The results show that over all readers the agreement of the segmentation
algorithm decreased from 0.76 to 0.74 when using population modes to assist in atlas selection. The use of every image
in the training set as an atlas outperformed both manual atlas selection methods, achieving a ZSI of 0.82.
Follicular Lymphoma (FL) accounts for 20-25% of non-Hodgkin lymphomas in the United States. The first step in
follicular lymphoma grading is the identification of follicles. The goal of this paper is to develop a technique to segment
follicular regions in H&E stained images. The method is based on a robust active contour model, which is initialized by a
seed point selected inside the follicle manually by the user. The novel aspect of this method is the introduction of a
matched filter for the flattening of background in the L channel of the Lab color space. The performance of the algorithm
was tested by comparing it against the manual segmentations of trained readers using the Zijbendos similarity index. The
mean accuracy of the final segmentation compared to the manual ground truth was 0.71 with a standard deviation of
0.12.
In this paper a novel framework is proposed for the classification of cervical tumors as susceptible or resistant to radiation therapy. The classification is based on both small- and large-scale temporal changes in the tumors' magnetic resonance imaging (MRI) response. The dataset consists of 11 patients who underwent radiation therapy for advanced cervical cancer. Each patient had dynamic contrast-enhanced (DCE)-MRI studies before treatment and early into treatment, approximately 2 weeks apart. For each study, a T1-weighted scan was performed before injection of contrast agent and again 75 seconds after injection. Using the two studies and the two series from each study, a set of tumor region of interest (ROI) features were calculated. These features were then exhaustively searched for the most separable set of three features based on a treatment outcome of local control or local recurrence. The dimensionality of the three-feature set was then reduced to two dimensions using principal components analysis (PCA). Finally, the classification performance was tested using three different classification procedures: support vector machines (SVM), linear discriminant analysis (LDA), and k-nearest neighbor (KNN). The most discriminatory features were those of volume, standard deviation, skewness, kurtosis, and fractal dimension. Combinations of these features resulted in 100%
classification accuracy using each of the three classifiers.
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