10,677 research outputs found
Self-Configuring and Evolving Fuzzy Image Thresholding
Every segmentation algorithm has parameters that need to be adjusted in order
to achieve good results. Evolving fuzzy systems for adjustment of segmentation
parameters have been proposed recently (Evolving fuzzy image segmentation --
EFIS [1]. However, similar to any other algorithm, EFIS too suffers from a few
limitations when used in practice. As a major drawback, EFIS depends on
detection of the object of interest for feature calculation, a task that is
highly application-dependent. In this paper, a new version of EFIS is proposed
to overcome these limitations. The new EFIS, called self-configuring EFIS
(SC-EFIS), uses available training data to auto-configure the parameters that
are fixed in EFIS. As well, the proposed SC-EFIS relies on a feature selection
process that does not require the detection of a region of interest (ROI).Comment: To appear in proceedings of The 14th International Conference on
Machine Learning and Applications (IEEE ICMLA 2015), Miami, Florida, USA,
201
Learning Opposites with Evolving Rules
The idea of opposition-based learning was introduced 10 years ago. Since then
a noteworthy group of researchers has used some notions of oppositeness to
improve existing optimization and learning algorithms. Among others,
evolutionary algorithms, reinforcement agents, and neural networks have been
reportedly extended into their opposition-based version to become faster and/or
more accurate. However, most works still use a simple notion of opposites,
namely linear (or type- I) opposition, that for each assigns its
opposite as . This, of course, is a very naive estimate of
the actual or true (non-linear) opposite , which has been
called type-II opposite in literature. In absence of any knowledge about a
function that we need to approximate, there seems to be no
alternative to the naivety of type-I opposition if one intents to utilize
oppositional concepts. But the question is if we can receive some level of
accuracy increase and time savings by using the naive opposite estimate
according to all reports in literature, what would we be able to
gain, in terms of even higher accuracies and more reduction in computational
complexity, if we would generate and employ true opposites? This work
introduces an approach to approximate type-II opposites using evolving fuzzy
rules when we first perform opposition mining. We show with multiple examples
that learning true opposites is possible when we mine the opposites from the
training data to subsequently approximate .Comment: Accepted for publication in The 2015 IEEE International Conference on
Fuzzy Systems (FUZZ-IEEE 2015), August 2-5, 2015, Istanbul, Turke
Intelligent Medical Image Segmentation Using Evolving Fuzzy Sets
Image segmentation is an important step in the image analysis process. Current image segmentation techniques, however, require that the user tune several parameters in order to obtain maximum segmentation accuracy, a computationally inefficient approach, especially when a large number of images must be processed sequentially in real time. Another major challenge, particularly with medical image analysis, is the discrepancy between objective measures for assessing and guiding the segmentation process, on the one hand, and the subjective perception of the end users (e.g., clinicians), on the other. Hence, the setting and adjustment of parameters for medical image segmentation should be performed in a manner that incorporates user feedback.
Despite the substantial number of techniques proposed in recent years, accurate segmentation of digital images remains a challenging task for automated computer algorithms. Approaches based on machine learning hold particular promise in this regard because, in many applications, including medical image analysis, frequent user intervention can be assumed as a means of correcting the results, thereby generating valuable feedback for algorithmic learning.
This thesis presents an investigation of the use of evolving fuzzy systems for designing a method that overcomes the problems associated with medical image segmentation. An evolving fuzzy system can be trained using a set of invariant features, along with their optimum parameters, which act as a target for the system. Evolving fuzzy systems are also capable of adjusting parameters based on online updates of their rule base. This thesis proposes three different approaches that employ an evolving fuzzy system for the continual adjustment of the parameters of any medical image segmentation technique.
The first proposed approach is based on evolving fuzzy image segmentation (EFIS). EFIS can adjust the parameters of existing segmentation methods and switch between them or fuse their results. The evolving rules have been applied for breast ultrasound images, with EFIS being used to adjust the parameters of three segmentation methods: global thresholding, region growing, and statistical region merging. The results for ten independent experiments for each of the three methods show average increases in accuracy of 5\%, 12\% and 9\% respectively. A comparison of the EFIS results with those obtained using five other thresholding methods revealed improvements. On the other hand, EFIS has some weak points, such as some fixed parameters and an inefficient feature calculation process.
The second approach proposed as a means of overcoming the problems with EFIS is a new version of EFIS, called self-configuring EFIS (SC-EFIS). SC-EFIS uses the available data to estimate all of the parameters that are fixed in EFIS and has a feature selection process that selects suitable features based on current data. SC-EFIS was evaluated using the same three methods as for EFIS. The results show that SC-EFIS is competitive with EFIS but provides a higher level of automation.
In the third approach, SC-EFIS is used to dynamically adjust more than one parameter, for example, three parameters of the normalized cut (N-cut) segmentation technique. This method, called multi-parametric SC-EFIS (MSC-EFIS), was applied to magnetic resonance images (MRIs) of the bladder and to breast ultrasound images. The results show the ability of MSC-EFIS to adjust multiple parameters. For ten independent experiments for each of the bladder and the breast images, this approach produced average accuracies that are 8\% and 16\% higher respectively, compared with their default values.
The experimental results indicate that the proposed algorithms show significant promise in enhancing image segmentation, especially for medical applications
Level-Set Based Artery-Vein Separation in Blood Pool Agent CE-MR Angiograms
Blood pool agents (BPAs) for contrast-enhanced (CE) magnetic-resonance angiography (MRA) allow prolonged imaging times for higher contrast and resolution. Imaging is performed during the steady state when the contrast agent is distributed through the complete vascular system. However, simultaneous venous and arterial enhancement in this steady state hampers interpretation. In order to improve visualization of the arteries and veins from steady-state BPA data, a semiautomated method for artery-vein separation is presented. In this method, the central arterial axis and central venous axis are used as initializations for two surfaces that simultaneously evolve in order to capture the arterial and venous parts of the vasculature using the level-set framework. Since arteries and veins can be in close proximity of each other, leakage from the evolving arterial (venous) surface into the venous (arterial) part of the vasculature is inevitable. In these situations, voxels are labeled arterial or venous based on the arrival time of the respective surface. The evolution is steered by external forces related to feature images derived from the image data and by internal forces related to the geometry of the level sets. In this paper, the robustness and accuracy of three external forces (based on image intensity, image gradient, and vessel-enhancement filtering) and combinations of them are investigated and tested on seven patient datasets. To this end, results with the level-set-based segmentation are compared to the reference-standard manually obtained segmentations. Best results are achieved by applying a combination of intensity- and gradient-based forces and a smoothness constraint based on the curvature of the surface. By applying this combination to the seven datasets, it is shown that, with minimal user interaction, artery-vein separation for improved arterial and venous visualization in BPA CE-MRA can be achieved
Learning Opposites Using Neural Networks
Many research works have successfully extended algorithms such as
evolutionary algorithms, reinforcement agents and neural networks using
"opposition-based learning" (OBL). Two types of the "opposites" have been
defined in the literature, namely \textit{type-I} and \textit{type-II}. The
former are linear in nature and applicable to the variable space, hence easy to
calculate. On the other hand, type-II opposites capture the "oppositeness" in
the output space. In fact, type-I opposites are considered a special case of
type-II opposites where inputs and outputs have a linear relationship. However,
in many real-world problems, inputs and outputs do in fact exhibit a nonlinear
relationship. Therefore, type-II opposites are expected to be better in
capturing the sense of "opposition" in terms of the input-output relation. In
the absence of any knowledge about the problem at hand, there seems to be no
intuitive way to calculate the type-II opposites. In this paper, we introduce
an approach to learn type-II opposites from the given inputs and their outputs
using the artificial neural networks (ANNs). We first perform \emph{opposition
mining} on the sample data, and then use the mined data to learn the
relationship between input and its opposite . We have validated
our algorithm using various benchmark functions to compare it against an
evolving fuzzy inference approach that has been recently introduced. The
results show the better performance of a neural approach to learn the
opposites. This will create new possibilities for integrating oppositional
schemes within existing algorithms promising a potential increase in
convergence speed and/or accuracy.Comment: To appear in proceedings of the 23rd International Conference on
Pattern Recognition (ICPR 2016), Cancun, Mexico, December 201
A robust lesion boundary segmentation algorithm using level set methods
This paper addresses the issue of accurate lesion segmentation in retinal imagery, using level set methods and
a novel stopping mechanism - an elementary features scheme. Specifically, the curve propagation is guided
by a gradient map built using a combination of histogram equalization and robust statistics. The stopping
mechanism uses elementary features gathered as the curve deforms over time, and then using a lesionness
measure, defined herein, ’looks back in time’ to find the point at which the curve best fits the real object.
We compare the proposed method against five other
segmentation algorithms performed on 50 randomly selected images of exudates with a database of clinician
demarcated boundaries as ground truth
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