596 research outputs found
A Two-stage Classification Method for High-dimensional Data and Point Clouds
High-dimensional data classification is a fundamental task in machine
learning and imaging science. In this paper, we propose a two-stage multiphase
semi-supervised classification method for classifying high-dimensional data and
unstructured point clouds. To begin with, a fuzzy classification method such as
the standard support vector machine is used to generate a warm initialization.
We then apply a two-stage approach named SaT (smoothing and thresholding) to
improve the classification. In the first stage, an unconstraint convex
variational model is implemented to purify and smooth the initialization,
followed by the second stage which is to project the smoothed partition
obtained at stage one to a binary partition. These two stages can be repeated,
with the latest result as a new initialization, to keep improving the
classification quality. We show that the convex model of the smoothing stage
has a unique solution and can be solved by a specifically designed primal-dual
algorithm whose convergence is guaranteed. We test our method and compare it
with the state-of-the-art methods on several benchmark data sets. The
experimental results demonstrate clearly that our method is superior in both
the classification accuracy and computation speed for high-dimensional data and
point clouds.Comment: 21 pages, 4 figure
A review of algorithms for medical image segmentation and their applications to the female pelvic cavity
This paper aims to make a review on the current segmentation algorithms used for medical images. Algorithms are classified according to their principal methodologies, namely the ones based on thresholds, the ones based on clustering techniques and the ones based on deformable models. The last type is focused on due to the intensive investigations into the deformable models that have been done in the last few decades. Typical algorithms of each type are discussed and the main ideas, application fields, advantages and disadvantages of each type are summarised. Experiments that apply these algorithms to segment the organs and tissues of the female pelvic cavity are presented to further illustrate their distinct characteristics. In the end, the main guidelines that should be considered for designing the segmentation algorithms of the pelvic cavity are proposed
An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor Diagnosis
Brain tumor detection and its analysis are essential in medical diagnosis. The proposed
work focuses on segmenting abnormality of axial brain MR DICOM slices, as this format holds
the advantage of conserving extensive metadata. The axial slices presume the left and right part
of the brain is symmetric by a Line of Symmetry (LOS). A semi-automated system is designed to
mine normal and abnormal structures from each brain MR slice in a DICOM study. In this work,
Fuzzy clustering (FC) is applied to the DICOM slices to extract various clusters for di erent k. Then,
the best-segmented image that has high inter-class rigidity is obtained using the silhouette fitness
function. The clustered boundaries of the tissue classes further enhanced by morphological operations.
The FC technique is hybridized with the standard image post-processing techniques such as marker
controlled watershed segmentation (MCW), region growing (RG), and distance regularized level sets
(DRLS). This procedure is implemented on renowned BRATS challenge dataset of di erent modalities
and a clinical dataset containing axial T2 weighted MR images of a patient. The sequential analysis of
the slices is performed using the metadata information present in the DICOM header. The validation
of the segmentation procedures against the ground truth images authorizes that the segmented objects
of DRLS through FC enhanced brain images attain maximum scores of Jaccard and Dice similarity
coe cients. The average Jaccard and dice scores for segmenting tumor part for ten patient studies of
the BRATS dataset are 0.79 and 0.88, also for the clinical study 0.78 and 0.86, respectively. Finally, 3D
visualization and tumor volume estimation are done using accessible DICOM information.Ministerio de Desarrollo de Recursos Humanos, India SPARC/2018-2019/P145/SLUniversidad Politécnica de Tomsk, Rusia RRSG/19/500
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