A Fully Adaptive and Hybrid Method for Image Segmentation Using Multilevel Thresholding

Ouadfel, Salima; Meshoul, Souham

doi:10.5815/ijigsp.2013.01.07

Cited by 11 publications

(6 citation statements)

References 51 publications

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“…ATC function can be minimized by applying GA. Hybrid models (Zahara et al 2005;Ouadfel and Meshoul 2013) are also employed for solving the multilevel threshold selection.…”

Section: Related Workmentioning

confidence: 99%

Multilevel threshold selection for image segmentation using soft computing techniques

Mala

Sridevi

2015

Soft Comput

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Multilevel thresholding is the method applied to segment the given image into unique sub-regions when the gray value distribution of the pixels is not distinct. The segmentation results are affected by factors such as number of threshold and threshold values. Hence, this paper proposes different methods for determining optimal thresholds using optimization techniques namely GA, PSO and hybrid model. Parallel algorithms are also proposed and implemented for these methods to reduce the execution time. From the experimental results, it is inferred that proposed methods take less time for determining the optimal thresholds when compared with existing methods such as Otsu and Kapur methods.

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“…ATC function can be minimized by applying GA. Hybrid models (Zahara et al 2005;Ouadfel and Meshoul 2013) are also employed for solving the multilevel threshold selection.…”

Section: Related Workmentioning

confidence: 99%

Multilevel threshold selection for image segmentation using soft computing techniques

Mala

Sridevi

2015

Soft Comput

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“…Image segmentation has received an enormous attention in the literature and so various techniques have been reported. For instance, an approach based on multilevel thresholds [3] uses the fast convergence of Particle Swarm Optimization (PSO), together with the jumping property of simulated annealing.…”

Section: A Literature and Backgroundmentioning

confidence: 99%

Applied Computational Engineering in Magnetic Resonance Imaging: A Tumor Case Study

Ciulla¹,

Bogatinoska²,

Risteski³

et al. 2014

IJIGSP

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This paper solves the biomedical engineering problem of the extraction of complementary and/or additional information related to the depths of the anatomical structures of the human brain tumor imaged with Magnetic Resonance Imaging (MRI). The combined calculation of the signal resilient to interpolation and the Intensity-Curvature Functional provides with the complementary and/or additional information. The steps to undertake for the calculation of the signal resilient to interpolation are: (i) fitting a polynomial function to the signal, (ii) the calculation of the classic-curvature of the signal, (iii) the calculation of the Intensity-Curvature term before interpolation of the signal, (iv) the calculation of the Intensity-Curvature term after interpolation of the signal, (v) the solution of the equation of the two aforementioned Intensity-Curvature terms of the signal provides with the signal resilient to interpolation. The Intensity-Curvature Functional is the result of the ratio between the two Intensity-Curvature terms before and after interpolation. Because of the fact that the signal resilient to interpolation and the Intensity-Curvature Functional are derived through the process of re-sampling the original signal, it is possible to obtain an immense number of images from the original MRI signal. This paper shows the combined use of the signal resilient to interpolation and the Intensity-Curvature Functional in diagnostic settings when evaluating a tumor imaged with MRI. Additionally, the Intensity-Curvature Functional can identify the tumor contour line.

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“…Therefore, it is of importance to calculate this interesting characteristic in order to show what its sensing parameters to evaluate them for the processor under consideration. The detailed analysis of this calculation can be found in [6]. Thus, the probability of adaptive detecting a fluctuating target obeying two degrees of freedom  2 model is…”

Section: Statistical Homogeneous Analysis Of the Gtm Processormentioning

confidence: 99%

“…Partially-correlated  2 targets have attracted great interest in both theoretical research and practical applications. Therefore, the detection of such type of fluctuating targets is of great importance [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Partially-Correlated χ2 Targets Detection Analysis of GTM-Adaptive Processor in the Presence of Outliers

Mashade¹

2014

IJIGSP

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This paper addresses the problem of detecting the partially-correlated  2 fluctuating targets with two and four degrees of freedom. It presents the performance analysis, in its exact form, of GTM-CFAR processor when the operating environment is contaminated with extraneous targets and the radar receiver post-detection integrates M pulses of exponentially correlated targets. Mathematical formulas for the detection and false alarm probabilities are derived, in the absence as well as in the presence of spurious targets which are fluctuating in accordance with the so-called moderately fluctuating  2 targets. A thorough performance assessment by several numerical examples, which has considered the role that each parameter can play in the processor performance, is also given. The results show that the processor performance improves, for weak SNR of the primary target, as the correlation coefficient ρ s increases and this occurs either in the absence or in the presence of outlying targets. As the strength of the target return increases, the processor tends to invert this behavior. The SWI & SWII and SWIII & SWIV models enclose the correlated target cases when the target correlation follows  2 fluctuation models with two and four degrees of freedom, respectively, and this behavior is common for all GTM based detectors.

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A Fully Adaptive and Hybrid Method for Image Segmentation Using Multilevel Thresholding

Cited by 11 publications

References 51 publications

Multilevel threshold selection for image segmentation using soft computing techniques

Multilevel threshold selection for image segmentation using soft computing techniques

Applied Computational Engineering in Magnetic Resonance Imaging: A Tumor Case Study

Partially-Correlated χ2 Targets Detection Analysis of GTM-Adaptive Processor in the Presence of Outliers

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