A Survey on Advanced Segmentation Techniques for Brain MRI Image Segmentation

Nilakant, Rupika; Menon, Hema P; Vikram, K.

doi:10.18517/ijaseit.7.4.1271

Cited by 22 publications

(11 citation statements)

References 45 publications

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“…When the number of dimensions is increased, CHS is still able to perform better than basic HS. Metaheuristic algorithm is successfully applied for image segmentation application [24,25]. In line with this, it is possible that CHS which uses metaheuristic algorithm can be applied in image segmentation in the future.…”

Section: Discussionmentioning

confidence: 87%

Cosine Harmony Search (CHS) for Static Optimization

Bohani¹,

Abdullah²,

Omar³

2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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Harmony Search (HS) is the behaviour imitation of a musician looking for a balanced harmony. HS has difficulty finding the best tuning parameter, especially for Pitch Adjustment Rate (PAR). PAR plays a crucial role in selecting historical solution and adjusting it using Bandwidth (BW) value. PAR in HS requires a constant value to be initialized. Furthermore, there is a delay in convergence speed due to the disproportion of global and local search capabilities. Although some HS variants have claimed to overcome this shortcoming by introducing the self-modification of PAR, these justifications have been found to be imprecise and require more extensive experiments. Local Opposition-Based Learning Self-Adaptation Global Harmony Search (LHS) implements a heuristic factor, η for self-modification of PAR. It (η) manages the probability for selecting the adaptive step, either global adaptive step or worst adaptive step. If the value of η is large, the prospects of selecting the global adaptive step is higher, thereby allowing the algorithm to exploit a better harmony value. Conversely, if η is small, the worst adaptive step is prone to selection, therefore the algorithm is closed to the best global solution. In this paper, in addressing the existing HS obstacle, we introduce a Cosine Harmony Search (CHS) which incorporates an additional strategy rule. This additional strategy employs the η inspired by LHS and contains the cosine parameter. This allows for self-modification of pitch tuning to enlarge the exploitation capabilities. We test our proposed CHS on twelve standard static benchmark functions and compare it with basic HS and five state-of-the-art HS variants. Our proposed method and these state-of-the-art algorithms are executed using 30 and 50 dimensions. The numerical results demonstrated that the CHS has outperformed other state-of-the-art algorithms in terms of accuracy and convergence speed evaluations.

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Section: Discussionmentioning

confidence: 87%

Cosine Harmony Search (CHS) for Static Optimization

Bohani¹,

Abdullah²,

Omar³

2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…There are different matters present in brain, normally categorized as i) White matter, ii) Gray matter, iii) Cerebrospinal Fluid (also known as Vasculature). These matters have been extracted using various segmentation techniques [18]. Fig.1 shows the different views of Brain MRI Images.…”

Section: Introductionmentioning

confidence: 99%

Various Techniques used for MRI Brain Image Segmentation

Ali*,

Agrawal

2019

IJITEE

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Image processing methods stems from processing of images from storing, transmitting and representation for autonomous machine perception. It is widely used technique in all engineering areas including diagnosis and treatment of diseases in Medical Science. Segmentation of non trivial images is one of the most challenging task because it requires more sophisticated method to differentiate between each region. The objective of this paper is to provide comprehensive overview of various segmentation methods used for MRI brain images. Hence, study of existing algorithms is all important for achieving accuracy. First, we briefly discuss about brain tumors, imaging modalities of brain and then several segmentation algorithms surveyed in this work. Complexities of existing algorithms and the segmentation outputs and analysis has also been discussed in observations.

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“…In the literature, many foreground detection algorithms [9,7,2,8] have been proposed, whose internal parameters (thresholds, sizes of regions...) and postprocessing techniques are fixed to obtain meaningful results depending on the application.…”

Section: Introductionmentioning

confidence: 99%

Foreground Detection Enhancement Using Pearson Correlation Filtering

Luque‐Baena

Molina‐Cabello

López‐Rubio

et al. 2018

Communications in Computer and Information Science

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Abstract. Foreground detection algorithms are commonly employed as an initial module in video processing pipelines for automated surveillance. The resulting masks produced by these algorithms are usually postprocessed in order to improve their quality. In this work, a postprocessing filter based on the Pearson correlation among the pixels in a neighborhood of the pixel at hand is proposed. The flow of information among pixels is controlled by the correlation that exists among them. This way, the filtering performance is enhanced with respect to some state of the art proposals, as demonstrated with a selection of benchmark videos.

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A Survey on Advanced Segmentation Techniques for Brain MRI Image Segmentation

Cited by 22 publications

References 45 publications

Cosine Harmony Search (CHS) for Static Optimization

Cosine Harmony Search (CHS) for Static Optimization

Various Techniques used for MRI Brain Image Segmentation

Foreground Detection Enhancement Using Pearson Correlation Filtering

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