30,034 research outputs found
Multithreshold Segmentation Based on Artificial Immune Systems
Bio-inspired computing has lately demonstrated its usefulness with remarkable contributions to shape detection, optimization, and classification in pattern recognition. Similarly, multithreshold selection has become a critical step for image analysis and computer vision sparking considerable efforts to design an optimal multi-threshold estimator. This paper presents an algorithm for multi-threshold segmentation which is based on the artificial immune systems(AIS) technique, also known as theclonal selection algorithm (CSA). It follows the clonal selection principle (CSP) from the human immune system which basically generates a response according to the relationship between antigens (Ag), that is, patterns to be recognized and antibodies (Ab), that is, possible solutions. In our approach, the 1D histogram of one image is approximated through a Gaussian mixture model whose parameters are calculated through CSA. Each Gaussian function represents a pixel class and therefore a thresholding point. Unlike the expectation-maximization (EM) algorithm, the CSA-based method shows a fast convergence and a low sensitivity to initial conditions. Remarkably, it also improves complex time-consuming computations commonly required by gradient-based methods. Experimental evidence demonstrates a successful automatic multi-threshold selection based on CSA, comparing its performance to the aforementioned well-known algorithms
Enhancement of Image Resolution by Binarization
Image segmentation is one of the principal approaches of image processing.
The choice of the most appropriate Binarization algorithm for each case proved
to be a very interesting procedure itself. In this paper, we have done the
comparison study between the various algorithms based on Binarization
algorithms and propose a methodologies for the validation of Binarization
algorithms. In this work we have developed two novel algorithms to determine
threshold values for the pixels value of the gray scale image. The performance
estimation of the algorithm utilizes test images with, the evaluation metrics
for Binarization of textual and synthetic images. We have achieved better
resolution of the image by using the Binarization method of optimum
thresholding techniques.Comment: 5 pages, 8 figure
A Comparison of Nature Inspired Algorithms for Multi-threshold Image Segmentation
In the field of image analysis, segmentation is one of the most important
preprocessing steps. One way to achieve segmentation is by mean of threshold
selection, where each pixel that belongs to a determined class islabeled
according to the selected threshold, giving as a result pixel groups that share
visual characteristics in the image. Several methods have been proposed in
order to solve threshold selectionproblems; in this work, it is used the method
based on the mixture of Gaussian functions to approximate the 1D histogram of a
gray level image and whose parameters are calculated using three nature
inspired algorithms (Particle Swarm Optimization, Artificial Bee Colony
Optimization and Differential Evolution). Each Gaussian function approximates
thehistogram, representing a pixel class and therefore a threshold point.
Experimental results are shown, comparing in quantitative and qualitative
fashion as well as the main advantages and drawbacks of each algorithm, applied
to multi-threshold problem.Comment: 16 pages, this is a draft of the final version of the article sent to
the Journa
Automated Detection of Regions of Interest for Brain Perfusion MR Images
Images with abnormal brain anatomy produce problems for automatic
segmentation techniques, and as a result poor ROI detection affects both
quantitative measurements and visual assessment of perfusion data. This paper
presents a new approach for fully automated and relatively accurate ROI
detection from dynamic susceptibility contrast perfusion magnetic resonance and
can therefore be applied excellently in the perfusion analysis. In the proposed
approach the segmentation output is a binary mask of perfusion ROI that has
zero values for air pixels, pixels that represent non-brain tissues, and
cerebrospinal fluid pixels. The process of binary mask producing starts with
extracting low intensity pixels by thresholding. Optimal low-threshold value is
solved by obtaining intensity pixels information from the approximate
anatomical brain location. Holes filling algorithm and binary region growing
algorithm are used to remove falsely detected regions and produce region of
only brain tissues. Further, CSF pixels extraction is provided by thresholding
of high intensity pixels from region of only brain tissues. Each time-point
image of the perfusion sequence is used for adjustment of CSF pixels location.
The segmentation results were compared with the manual segmentation performed
by experienced radiologists, considered as the reference standard for
evaluation of proposed approach. On average of 120 images the segmentation
results have a good agreement with the reference standard. All detected
perfusion ROIs were deemed by two experienced radiologists as satisfactory
enough for clinical use. The results show that proposed approach is suitable to
be used for perfusion ROI detection from DSC head scans. Segmentation tool
based on the proposed approach can be implemented as a part of any automatic
brain image processing system for clinical use
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