10,442 research outputs found
Incorporating prior knowledge in medical image segmentation: a survey
Medical image segmentation, the task of partitioning an image into meaningful
parts, is an important step toward automating medical image analysis and is at
the crux of a variety of medical imaging applications, such as computer aided
diagnosis, therapy planning and delivery, and computer aided interventions.
However, the existence of noise, low contrast and objects' complexity in
medical images are critical obstacles that stand in the way of achieving an
ideal segmentation system. Incorporating prior knowledge into image
segmentation algorithms has proven useful for obtaining more accurate and
plausible results. This paper surveys the different types of prior knowledge
that have been utilized in different segmentation frameworks. We focus our
survey on optimization-based methods that incorporate prior information into
their frameworks. We review and compare these methods in terms of the types of
prior employed, the domain of formulation (continuous vs. discrete), and the
optimization techniques (global vs. local). We also created an interactive
online database of existing works and categorized them based on the type of
prior knowledge they use. Our website is interactive so that researchers can
contribute to keep the database up to date. We conclude the survey by
discussing different aspects of designing an energy functional for image
segmentation, open problems, and future perspectives.Comment: Survey paper, 30 page
Bayesian Semantic Instance Segmentation in Open Set World
This paper addresses the semantic instance segmentation task in the open-set
conditions, where input images can contain known and unknown object classes.
The training process of existing semantic instance segmentation methods
requires annotation masks for all object instances, which is expensive to
acquire or even infeasible in some realistic scenarios, where the number of
categories may increase boundlessly. In this paper, we present a novel open-set
semantic instance segmentation approach capable of segmenting all known and
unknown object classes in images, based on the output of an object detector
trained on known object classes. We formulate the problem using a Bayesian
framework, where the posterior distribution is approximated with a simulated
annealing optimization equipped with an efficient image partition sampler. We
show empirically that our method is competitive with state-of-the-art
supervised methods on known classes, but also performs well on unknown classes
when compared with unsupervised methods.Comment: Accepted to ECCV 201
A Continuous Max-Flow Approach to Multi-Labeling Problems under Arbitrary Region Regularization
The incorporation of region regularization into max-flow segmentation has
traditionally focused on ordering and part-whole relationships. A side effect
of the development of such models is that it constrained regularization only to
those cases, rather than allowing for arbitrary region regularization. Directed
Acyclic Graphical Max-Flow (DAGMF) segmentation overcomes these limitations by
allowing for the algorithm designer to specify an arbitrary directed acyclic
graph to structure a max-flow segmentation. This allows for individual 'parts'
to be a member of multiple distinct 'wholes.'Comment: 10 pages, 2 figures, 3 algorithms - v2: Fixed typos / grammatical
errors and mathematical errors in the primal/dual formulation. Extended
methods for weighted DAGs rather than DAGs with edge multiplicit
Just-Enough Interaction Approach to Knee MRI Segmentation: Data from the Osteoarthritis Initiative
State-of-the-art automated segmentation algorithms are not 100\% accurate
especially when segmenting difficult to interpret datasets like those with
severe osteoarthritis (OA). We present a novel interactive method called
just-enough interaction (JEI), which adds a fast correction step to the
automated layered optimal graph segmentation of multiple objects and surfaces
(LOGISMOS). After LOGISMOS segmentation in knee MRI, the JEI user interaction
does not modify boundary surfaces of the bones and cartilages directly. Local
costs of underlying graph nodes are modified instead and the graph is
re-optimized, providing globally optimal corrected results. Significant
performance improvement () was observed when comparing
JEI-corrected results to the automated. The algorithm was extended from 3D JEI
to longitudinal multi-3D (4D) JEI allowing simultaneous visualization and
interaction of multiple-time points of the same patient.Comment: Proceedings of the 3rd International Workshop on Interactive Medical
Image Computing (IMIC), Held in Conjunction with MICCAI, 201
Adaptable Precomputation for Random Walker Image Segmentation and Registration
The random walker (RW) algorithm is used for both image segmentation and
registration, and possesses several useful properties that make it popular in
medical imaging, such as being globally optimizable, allowing user interaction,
and providing uncertainty information. The RW algorithm defines a weighted
graph over an image and uses the graph's Laplacian matrix to regularize its
solutions. This regularization reduces to solving a large system of equations,
which may be excessively time consuming in some applications, such as when
interacting with a human user. Techniques have been developed that precompute
eigenvectors of a Laplacian offline, after image acquisition but before any
analysis, in order speed up the RW algorithm online, when segmentation or
registration is being performed. However, precomputation requires certain
algorithm parameters be fixed offline, limiting their flexibility. In this
paper, we develop techniques to update the precomputed data online when RW
parameters are altered. Specifically, we dynamically determine the number of
eigenvectors needed for a desired accuracy based on user input, and derive
update equations for the eigenvectors when the edge weights or topology of the
image graph are changed. We present results demonstrating that our techniques
make RW with precomputation much more robust to offline settings while only
sacrificing minimal accuracy.Comment: 9 pages, 8 figure
Co-Sparse Textural Similarity for Image Segmentation
We propose an algorithm for segmenting natural images based on texture and
color information, which leverages the co-sparse analysis model for image
segmentation within a convex multilabel optimization framework. As a key
ingredient of this method, we introduce a novel textural similarity measure,
which builds upon the co-sparse representation of image patches. We propose a
Bayesian approach to merge textural similarity with information about color and
location. Combined with recently developed convex multilabel optimization
methods this leads to an efficient algorithm for both supervised and
unsupervised segmentation, which is easily parallelized on graphics hardware.
The approach provides competitive results in unsupervised segmentation and
outperforms state-of-the-art interactive segmentation methods on the Graz
Benchmark
Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models
Segmentation is a fundamental task for extracting semantically meaningful
regions from an image. The goal of segmentation algorithms is to accurately
assign object labels to each image location. However, image-noise, shortcomings
of algorithms, and image ambiguities cause uncertainty in label assignment.
Estimating the uncertainty in label assignment is important in multiple
application domains, such as segmenting tumors from medical images for
radiation treatment planning. One way to estimate these uncertainties is
through the computation of posteriors of Bayesian models, which is
computationally prohibitive for many practical applications. On the other hand,
most computationally efficient methods fail to estimate label uncertainty. We
therefore propose in this paper the Active Mean Fields (AMF) approach, a
technique based on Bayesian modeling that uses a mean-field approximation to
efficiently compute a segmentation and its corresponding uncertainty. Based on
a variational formulation, the resulting convex model combines any
label-likelihood measure with a prior on the length of the segmentation
boundary. A specific implementation of that model is the Chan-Vese segmentation
model (CV), in which the binary segmentation task is defined by a Gaussian
likelihood and a prior regularizing the length of the segmentation boundary.
Furthermore, the Euler-Lagrange equations derived from the AMF model are
equivalent to those of the popular Rudin-Osher-Fatemi (ROF) model for image
denoising. Solutions to the AMF model can thus be implemented by directly
utilizing highly-efficient ROF solvers on log-likelihood ratio fields. We
qualitatively assess the approach on synthetic data as well as on real natural
and medical images. For a quantitative evaluation, we apply our approach to the
icgbench dataset
The Candidate Multi-Cut for Cell Segmentation
Two successful approaches for the segmentation of biomedical images are (1)
the selection of segment candidates from a merge-tree, and (2) the clustering
of small superpixels by solving a Multi-Cut problem. In this paper, we
introduce a model that unifies both approaches. Our model, the Candidate
Multi-Cut (CMC), allows joint selection and clustering of segment candidates
from a merge-tree. This way, we overcome the respective limitations of the
individual methods: (1) the space of possible segmentations is not constrained
to candidates of a merge-tree, and (2) the decision for clustering can be made
on candidates larger than superpixels, using features over larger contexts. We
solve the optimization problem of selecting and clustering of candidates using
an integer linear program. On datasets of 2D light microscopy of cell
populations and 3D electron microscopy of neurons, we show that our method
generalizes well and generates more accurate segmentations than merge-tree or
Multi-Cut methods alone
A Multi-Agents Architecture to Learn Vision Operators and their Parameters
In a vision system, every task needs that the operators to apply should be
{\guillemotleft} well chosen {\guillemotright} and their parameters should be
also {\guillemotleft} well adjusted {\guillemotright}. The diversity of
operators and the multitude of their parameters constitute a big challenge for
users. As it is very difficult to make the {\guillemotleft} right
{\guillemotright} choice, lack of a specific rule, many disadvantages appear
and affect the computation time and especially the quality of results. In this
paper we present a multi-agent architecture to learn the best operators to
apply and their best parameters for a class of images. Our architecture
consists of three types of agents: User Agent, Operator Agent and Parameter
Agent. The User Agent determines the phases of treatment, a library of
operators and the possible values of their parameters. The Operator Agent
constructs all possible combinations of operators and the Parameter Agent, the
core of the architecture, adjusts the parameters of each combination by
treating a large number of images. Through the reinforcement learning
mechanism, our architecture does not consider only the system opportunities but
also the user preferences.Comment: IJCSI, May 201
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks
We propose a novel video object segmentation algorithm based on pixel-level
matching using Convolutional Neural Networks (CNN). Our network aims to
distinguish the target area from the background on the basis of the pixel-level
similarity between two object units. The proposed network represents a target
object using features from different depth layers in order to take advantage of
both the spatial details and the category-level semantic information.
Furthermore, we propose a feature compression technique that drastically
reduces the memory requirements while maintaining the capability of feature
representation. Two-stage training (pre-training and fine-tuning) allows our
network to handle any target object regardless of its category (even if the
object's type does not belong to the pre-training data) or of variations in its
appearance through a video sequence. Experiments on large datasets demonstrate
the effectiveness of our model - against related methods - in terms of
accuracy, speed, and stability. Finally, we introduce the transferability of
our network to different domains, such as the infrared data domain.Comment: To appear on ICCV 201
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