8,693 research outputs found
Deeply-Supervised CNN for Prostate Segmentation
Prostate segmentation from Magnetic Resonance (MR) images plays an important
role in image guided interven- tion. However, the lack of clear boundary
specifically at the apex and base, and huge variation of shape and texture
between the images from different patients make the task very challenging. To
overcome these problems, in this paper, we propose a deeply supervised
convolutional neural network (CNN) utilizing the convolutional information to
accurately segment the prostate from MR images. The proposed model can
effectively detect the prostate region with additional deeply supervised layers
compared with other approaches. Since some information will be abandoned after
convolution, it is necessary to pass the features extracted from early stages
to later stages. The experimental results show that significant segmentation
accuracy improvement has been achieved by our proposed method compared to other
reported approaches.Comment: Due to a crucial sign error in equation
Morphological operators for very low bit rate video coding
This paper deals with the use of some morphological tools for video coding at very low bit rates. Rather than describing a complete coding algorithm, the purpose of this paper is to focus on morphological connected operators and segmentation tools that have proved to be attractive for compression.Peer ReviewedPostprint (published version
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Statistical Region Based Segmentation of Ultrasound Images
Segmentation of ultrasound images is a challenging problem due to speckle, which
corrupts the image and can result in weak or missing image boundaries, poor signal to
noise ratio, and diminished contrast resolution. Speckle is a random interference pattern
that is characterized by an asymmetric distribution as well as significant spatial correla-
tion. These attributes of speckle are challenging to model in a segmentation approach, so
many previous ultrasound segmentation methods simplify the problem by assuming that
the speckle is white and/or Gaussian distributed. Unlike these methods, in this paper
we present an ultrasound-specific segmentation approach that addresses both the spatial
correlation of the data as well as its intensity distribution. We first decorrelate the image
and then apply a region-based active contour whose motion is derived from an appropri-
ate parametric distribution for maximum likelihood image segmentation. We consider
zero-mean complex Gaussian, Rayleigh, and Fisher-Tippett flows, which are designed
to model fully formed speckle in the in-phase/quadrature (IQ), envelope detected, and
display (log compressed) images, respectively. We present experimental results demon-
strating the effectiveness of our method, and compare the results to other parametric
and non-parametric active contours
Segmentation of Radiographs of Hands with Joint Damage Using Customized Active Appearance Models
This paper is part of a project that investigates the possibilities of automating the assessment of joint damagein hand radiographs. Our goal is to design a robust segmentationalgorithm for the hand skeleton. The algorithm is\ud
based on active appearance models (AAM) [1], which have been used for hand segmentation before [2]. The results will be used in the future for radiographic assessment of rheumatoid arthritis and the early detection of joint damage. New in this work with respect to [2] is the use of multiple object warps for each individual bone in a single AAM. This method prevents modelling and reconstruction defects caused when warping overlapping objects. This makes the algorithm more robust in cases where joint damage is present. The current implementation of the model includes the metacarpals, the phalanges, and the carpal region. For a first experimental evaluation a collection of 50 hand radiographs has been gathered. The image data set was split into a training set (40) and a test set (10) in order to evaluate the algorithm’s performance. First results show that in 8 images from the test set the bone contours are detected correctly within 1.3 mm (1 STD) at 15 pixels/cm resolution. In two images not all contours are detected correctly. Possibly this is caused by extreme deviations in these images that have not yet been incorporated in the model due to a limited training set. More training examples are needed to optimize the AAM and improve the quality and reliability of the results
Image Segmentation Using Weak Shape Priors
The problem of image segmentation is known to become particularly challenging
in the case of partial occlusion of the object(s) of interest, background
clutter, and the presence of strong noise. To overcome this problem, the
present paper introduces a novel approach segmentation through the use of
"weak" shape priors. Specifically, in the proposed method, an segmenting active
contour is constrained to converge to a configuration at which its geometric
parameters attain their empirical probability densities closely matching the
corresponding model densities that are learned based on training samples. It is
shown through numerical experiments that the proposed shape modeling can be
regarded as "weak" in the sense that it minimally influences the segmentation,
which is allowed to be dominated by data-related forces. On the other hand, the
priors provide sufficient constraints to regularize the convergence of
segmentation, while requiring substantially smaller training sets to yield less
biased results as compared to the case of PCA-based regularization methods. The
main advantages of the proposed technique over some existing alternatives is
demonstrated in a series of experiments.Comment: 27 pages, 8 figure
Medical Image Segmentation by Water Flow
We present a new image segmentation technique based on the paradigm of water flow and apply it to medical images. The force field analogy is used to implement the major water flow attributes like water pressure, surface tension and adhesion so that the model achieves topological adaptability and geometrical flexibility. A new snake-like force functional combining edge- and region-based forces is introduced to produce capability for both range and accuracy. The method has been assessed qualitatively and quantitatively, and shows decent detection performance as well as ability to handle noise
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