17,245 research outputs found
Elimination of Glass Artifacts and Object Segmentation
Many images nowadays are captured from behind the glasses and may have
certain stains discrepancy because of glass and must be processed to make
differentiation between the glass and objects behind it. This research paper
proposes an algorithm to remove the damaged or corrupted part of the image and
make it consistent with other part of the image and to segment objects behind
the glass. The damaged part is removed using total variation inpainting method
and segmentation is done using kmeans clustering, anisotropic diffusion and
watershed transformation. The final output is obtained by interpolation. This
algorithm can be useful to applications in which some part of the images are
corrupted due to data transmission or needs to segment objects from an image
for further processing
A local Gaussian filter and adaptive morphology as tools for completing partially discontinuous curves
This paper presents a method for extraction and analysis of curve--type
structures which consist of disconnected components. Such structures are found
in electron--microscopy (EM) images of metal nanograins, which are widely used
in the field of nanosensor technology.
The topography of metal nanograins in compound nanomaterials is crucial to
nanosensor characteristics. The method of completing such templates consists of
three steps. In the first step, a local Gaussian filter is used with different
weights for each neighborhood. In the second step, an adaptive morphology
operation is applied to detect the endpoints of curve segments and connect
them. In the last step, pruning is employed to extract a curve which optimally
fits the template
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Automated Fourier space region-recognition filtering for off-axis digital holographic microscopy
Automated label-free quantitative imaging of biological samples can greatly
benefit high throughput diseases diagnosis. Digital holographic microscopy
(DHM) is a powerful quantitative label-free imaging tool that retrieves
structural details of cellular samples non-invasively. In off-axis DHM, a
proper spatial filtering window in Fourier space is crucial to the quality of
reconstructed phase image. Here we describe a region-recognition approach that
combines shape recognition with an iterative thresholding to extracts the
optimal shape of frequency components. The region recognition technique offers
fully automated adaptive filtering that can operate with a variety of samples
and imaging conditions. When imaging through optically scattering biological
hydrogel matrix, the technique surpasses previous histogram thresholding
techniques without requiring any manual intervention. Finally, we automate the
extraction of the statistical difference of optical height between malaria
parasite infected and uninfected red blood cells. The method described here
pave way to greater autonomy in automated DHM imaging for imaging live cell in
thick cell cultures
A graph-based mathematical morphology reader
This survey paper aims at providing a "literary" anthology of mathematical
morphology on graphs. It describes in the English language many ideas stemming
from a large number of different papers, hence providing a unified view of an
active and diverse field of research
Adaptive Weighted Morphology Detection Algorithm of Plane Object in Docking Guidance System
In this paper, we presented an image segmentation algorithm based on adaptive weighted mathematical morphology edge detectors. The performance of the proposed algorithm has been demonstrated on the Lena image. The input of the proposed algorithm is a grey level image. The image was first processed by the mathematical morphological closing and dilation residue edge detector to enhance the edge features and sketch out the contour of the image, respectively. Then the adaptive weight SE operation was applied to the edge-extracted image to fuse edge gaps and hill up holds. Experimental results show it can not only primely extract detail edge, but also superbly preserve integer effect comparative to classical edge detection algorithm
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