An efficient method for segmentation of images based on fractional calculus and natural selection

Ghamisi, Pedram; Couceiro, Micael S.; Benediktsson, Jón Atli; Ferreira, Nuno M. F.

doi:10.1016/j.eswa.2012.04.078

Cited by 256 publications

(140 citation statements)

References 34 publications

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“…FODPSO is a promising method to specify a predefined number of clusters with a higher between class variance. In [9], the authors demonstrated that the FODPSO based segmentation method performs considerably better in terms of accuracies than Genetic Algorithm (GA), Bacterial Algorithm (BA), PSO and DPSO, thus finding different number of clusters with a higher between-class variance and more stability in less computational processing time. For further information on the FODPSO algorithm please refer [9,16].…”

Section: Methodsmentioning

confidence: 99%

“…A commonly used exhaustive search method is based on the Otsu criterion [1]. However, exhaustive search to find n -1 optimal thresholds involves evaluation of the fitness for n(L-n+1) n-1 combinations of thresholds [9]. Therefore, this method is not desirable from a computational point of view.…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, the issue of determining n -1 optimal thresholds for n-level image thresholding can be formulated as a multidimensional optimization problem. To solve the aforementioned issue, several biologically inspired algorithms have been explored in image segmentation [9]. One of the most commonly used methods based on split and merging segmentation is Mean Shift Segmentation (MSS) which is widely used in image processing.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Integration of Segmentation Techniques for Classification of Hyperspectral Images

Ghamisi

Couceiro²,

Fauvel³

et al. 2014

IEEE Geosci. Remote Sensing Lett.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Integration of Segmentation Techniques for Classification of Hyperspectral Images

Ghamisi

Couceiro²,

Fauvel³

et al. 2014

IEEE Geosci. Remote Sensing Lett.

Self Cite

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“…Neither edge-based nor region-based ACM method alone can accurately segment medical images with intensity inhomogeneity [14,16,17]. In the attempt to resolve this problem, a combination of edge-based and region-based ACM methods were later introduced by many researchers [22].…”

Section: Introductionmentioning

confidence: 99%

“…The method has proven more successful than edge-based ACM in segmenting noisy medical images due to it robustness in handling noise. However, apart from the visual noises, many medical images such as MRI and ultrasound are also impaired with intensity inhomogeneity problem [11,13,14]. Intensity inhomogeneity is a problem where the distribution of the intensity level in a region of an image is not the same or homogeneous.…”

Section: Introductionmentioning

confidence: 99%

Local region-based ACM with fractional calculus for boundary segmentation in images with intensity inhomogeneity

Kamaruddin

Abdullah

Ibrahim

2016

MJCS

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This study proposes a novel local region-based active contour model (ACM) for image segmentation based on fractional calculus with consideration of the Riemann-Liouville operators. The proposed method aims to achieve accurate boundary segmentation in the presence of severe intensity inhomogeneity. The strength of fractional calculus is exploited with Gaussian Kernel namely, Fractional Gaussian Kernel (FGK) that provides an effective method of edge detection and has good noise immunity. An adaptive window mechanism with various sizes and orientations is employ to maintain and enhance image details especially at the object's boundary and angle. The powerful combination of adaptive window and Fractional Gaussian Kernel (AFGK) provide an efficient way to utilize image information in local regions. Specifically, the fractional differential Heaviside function (FDH) extracts the image gradient and its various intensities for an accurate boundary segmentation outcome. Experiments on both synthetics and medical images demonstrate that the proposed local region-based ACM including fractional calculus realizes accurate boundary segmentation on images even under the most challenging situations, such as severe intensity inhomogeneity interface.

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A derivative concept with respect to an arbitrary kernel and applications to fractional calculus

Jleli

Kirane

Samet

2018

Math Methods in App Sciences

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In this paper, we propose a new concept of derivative with respect to an arbitrary kernel function. Several properties related to this new operator, like inversion rules and integration by parts, are studied. In particular, we introduce the notion of conjugate kernels, which will be useful to guaranty that the proposed derivative operator admits a right inverse. The proposed concept includes as special cases Riemann-Liouville fractional derivatives, Hadamard fractional derivatives, and many other fractional operators. Moreover, using our concept, new fractional operators involving certain special functions are introduced, and some of their properties are studied. Finally, an existence result for a boundary value problem involving the introduced derivative operator is proved. KEYWORDS boundary value problem, conjugate kernels, fractional calculus, k-derivative x 0 (x − ) −1 ( ) d , > 0 and derivatives of order (D 0 )(x) = ( d dx ) n (I n− 0 )(x), Math Meth Appl Sci. 2019;42:137-160. wileyonlinelibrary.com/journal/mma

show abstract

An efficient method for segmentation of images based on fractional calculus and natural selection

Cited by 256 publications

References 34 publications

Integration of Segmentation Techniques for Classification of Hyperspectral Images

Integration of Segmentation Techniques for Classification of Hyperspectral Images

Local region-based ACM with fractional calculus for boundary segmentation in images with intensity inhomogeneity

A derivative concept with respect to an arbitrary kernel and applications to fractional calculus

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