35 research outputs found
Unbiased Shape Compactness for Segmentation
We propose to constrain segmentation functionals with a dimensionless,
unbiased and position-independent shape compactness prior, which we solve
efficiently with an alternating direction method of multipliers (ADMM).
Involving a squared sum of pairwise potentials, our prior results in a
challenging high-order optimization problem, which involves dense (fully
connected) graphs. We split the problem into a sequence of easier sub-problems,
each performed efficiently at each iteration: (i) a sparse-matrix inversion
based on Woodbury identity, (ii) a closed-form solution of a cubic equation and
(iii) a graph-cut update of a sub-modular pairwise sub-problem with a sparse
graph. We deploy our prior in an energy minimization, in conjunction with a
supervised classifier term based on CNNs and standard regularization
constraints. We demonstrate the usefulness of our energy in several medical
applications. In particular, we report comprehensive evaluations of our fully
automated algorithm over 40 subjects, showing a competitive performance for the
challenging task of abdominal aorta segmentation in MRI.Comment: Accepted at MICCAI 201
Template-Cut: A Pattern-Based Segmentation Paradigm
We present a scale-invariant, template-based segmentation paradigm that sets
up a graph and performs a graph cut to separate an object from the background.
Typically graph-based schemes distribute the nodes of the graph uniformly and
equidistantly on the image, and use a regularizer to bias the cut towards a
particular shape. The strategy of uniform and equidistant nodes does not allow
the cut to prefer more complex structures, especially when areas of the object
are indistinguishable from the background. We propose a solution by introducing
the concept of a "template shape" of the target object in which the nodes are
sampled non-uniformly and non-equidistantly on the image. We evaluate it on
2D-images where the object's textures and backgrounds are similar, and large
areas of the object have the same gray level appearance as the background. We
also evaluate it in 3D on 60 brain tumor datasets for neurosurgical planning
purposes.Comment: 8 pages, 6 figures, 3 tables, 6 equations, 51 reference
"IMAGE DIGEST III: A NEAREST NEIGHBOUR DIFFERENTIAL BASED IMAGE DIGEST GENERATION ALGORITHM "
In this paper we present a methodology to generate a digest for an image based on the grayness value differentials that exist between neighboring pixels. Neighboring pixels are those that lie to the immediate left, immediate right, immediate above and immediate below of a given pixel plus the four pixels that lie in between. This algorithm works on the monochrome images of VGA resolution. Color images are converted to their monochrome equivalents. Images of resolution higher than VGA are converted to images of VGA resolution. The given image is divided into equal sized segments or regions. The pixels of the given image are sampled in such a way that each segment contributes equally to the sampled set for the image. This algorithm uses a histogram based statistical approach towards digest generation. Counters are maintained at the segment level, which keep the raw counts of the differentials for the sampled pixels. The counter values are composed to form the digest for the segment. Computing the digest at the segment level helps to preserve the locality information for the image. The digest for the entire image is a composition of the individual digests generated for each segment or region. The method also provides for the calculation of a lite version of the digest that saves digest space by ignoring the region or locality information
Efficient MRF Energy Propagation for Video Segmentation via Bilateral Filters
Segmentation of an object from a video is a challenging task in multimedia
applications. Depending on the application, automatic or interactive methods
are desired; however, regardless of the application type, efficient computation
of video object segmentation is crucial for time-critical applications;
specifically, mobile and interactive applications require near real-time
efficiencies. In this paper, we address the problem of video segmentation from
the perspective of efficiency. We initially redefine the problem of video
object segmentation as the propagation of MRF energies along the temporal
domain. For this purpose, a novel and efficient method is proposed to propagate
MRF energies throughout the frames via bilateral filters without using any
global texture, color or shape model. Recently presented bi-exponential filter
is utilized for efficiency, whereas a novel technique is also developed to
dynamically solve graph-cuts for varying, non-lattice graphs in general linear
filtering scenario. These improvements are experimented for both automatic and
interactive video segmentation scenarios. Moreover, in addition to the
efficiency, segmentation quality is also tested both quantitatively and
qualitatively. Indeed, for some challenging examples, significant time
efficiency is observed without loss of segmentation quality.Comment: Multimedia, IEEE Transactions on (Volume:16, Issue: 5, Aug. 2014
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Efficient segmentation based on Eikonal and diffusion equations
Segmentation of regions of interest in an image has important applications in medical image analysis, particularly in computer aided diagnosis. Segmentation can enable further quantitative analysis of anatomical structures. We present efficient image segmentation schemes based on the solution of distinct partial differential equations (PDEs). For each known image region, a PDE is solved, the solution of which locally represents the weighted distance from a region known to have a certain segmentation label. To achieve this goal, we propose the use of two separate PDEs, the Eikonal equation and a diffusion equation. In each method, the segmentation labels are obtained by a competition criterion between the solutions to the PDEs corresponding to each region. We discuss how each method applies the concept of information propagation from the labelled image regions to the unknown image regions. Experimental results are presented on magnetic resonance, computed tomography, and ultrasound images and for both two-region and multi-region segmentation problems. These results demonstrate the high level of efficiency as well as the accuracy of the proposed methods
Efficient Mining of Heterogeneous Star-Structured Data
Many of the real world clustering problems arising in data mining applications are heterogeneous in nature. Heterogeneous co-clustering involves simultaneous clustering of objects of two or more data types. While pairwise co-clustering of two data types has been well studied in the literature, research on high-order heterogeneous co-clustering is still limited. In this paper, we propose a graph theoretical framework for addressing star- structured co-clustering problems in which a central data type is connected to all the other data types. Partitioning this graph leads to co-clustering of all the data types under the constraints of the star-structure. Although, graph partitioning approach has been adopted before to address star-structured heterogeneous complex problems, the main contribution of this work lies in an e cient algorithm that we propose for partitioning the star-structured graph. Computationally, our algorithm is very quick as it requires a simple solution to a sparse system of overdetermined linear equations. Theoretical analysis and extensive exper- iments performed on toy and real datasets demonstrate the quality, e ciency and stability of the proposed algorithm
Square-Cut: A Segmentation Algorithm on the Basis of a Rectangle Shape
We present a rectangle-based segmentation algorithm that sets up a graph and
performs a graph cut to separate an object from the background. However,
graph-based algorithms distribute the graph's nodes uniformly and equidistantly
on the image. Then, a smoothness term is added to force the cut to prefer a
particular shape. This strategy does not allow the cut to prefer a certain
structure, especially when areas of the object are indistinguishable from the
background. We solve this problem by referring to a rectangle shape of the
object when sampling the graph nodes, i.e., the nodes are distributed
nonuniformly and non-equidistantly on the image. This strategy can be useful,
when areas of the object are indistinguishable from the background. For
evaluation, we focus on vertebrae images from Magnetic Resonance Imaging (MRI)
datasets to support the time consuming manual slice-by-slice segmentation
performed by physicians. The ground truth of the vertebrae boundaries were
manually extracted by two clinical experts (neurological surgeons) with several
years of experience in spine surgery and afterwards compared with the automatic
segmentation results of the proposed scheme yielding an average Dice Similarity
Coefficient (DSC) of 90.97\pm62.2%.Comment: 13 pages, 17 figures, 2 tables, 3 equations, 42 reference