Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm

Agrawal, Sanjay; Panda, Rutuparna; Bhuyan, Sudipta; Panigrahi, Bijaya Ketan

doi:10.1016/j.swevo.2013.02.001

Cited by 231 publications

(66 citation statements)

References 23 publications

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“…Literature records several algorithms in the field of medical image segmentation [1,2]. Among them, threshold based segmentation procedure is found to be very simple, robust, accurate and has less computational time complexity [3]. However, determining the best threshold automatically for accurate segmentation is a tedious procedure for complex and noisy medical image database.…”

Section: Introductionmentioning

confidence: 99%

Automatic Roi Extraction in Noisy Medical Images

Renukalatha¹,

Kalyanasundaram²

2017

IJIVP

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Accurate segmentation of medical images is pivotal in medical image analysis as it favors the detection and quantification of abnormalities present in human anatomical structures. Since medical images are complex and sometimes noisy, effective extraction of the regions of abnormalities is a tedious process. Many semi-automatic segmentation algorithms with appreciable segmentation accuracy do exist in literature. However, these techniques are iterative, computationally expensive, involve human intervention demanding initial parameter settings and moreover, each one of them is specific to a particular modality. In addition, presence of noise further degrades the quality of the processed image. There is no general algorithm to extract the key regions from all types of noisy medical images. This paper proposes an automatic Region of Interest (ROI) extraction algorithm to detect the important regions in noisy medical images of different modalities using statistical moments. The proposed approach estimates an optimal threshold value automatically using statistical moments through histogram decomposition technique. Initially, the medical image database is preprocessed followed by ROI extraction and the performance of the proposed approach is compared with other techniques to verify its robustness.

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Section: Introductionmentioning

confidence: 99%

Automatic Roi Extraction in Noisy Medical Images

Renukalatha¹,

Kalyanasundaram²

2017

IJIVP

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“…The CS algorithm is applied to the structural design optimization of a vehicle component to illustrate how the present approach can be applied for solving structural design problems. Agrawal et al (2013) use the cuckoo search algorithm to find the optimal thresholds for multi-level threshold in an image are obtained by maximizing the Tsallis entropy. The results are then compared with that of other compared algorithms.…”

Section: Introductionmentioning

confidence: 99%

A particle swarm inspired cuckoo search algorithm for real parameter optimization

Yin

2015

Soft Comput

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The cuckoo search algorithm (CS) is a simple and effective global optimization algorithm. It has been successfully applied to solve a wide range of real-world optimization problems. In this paper, inspired by the particle swarm optimization (PSO), the proposed algorithm uses the best individuals among the entire population to enhance the convergence rate of the standard cuckoo search algorithm. While the PSO directly uses the global best solution of the population to determine new positions for the particles at the each iteration, agents of the CS do not directly use this information but the global best solution in the CS is stored at the each iteration. The global best solutions are used to add into the Information flow between the nest helps increase global and local search abilities of the new approach. Therefore, in the first component, the neighborhood information is added into the new population to enhance the diversity of the algorithm. In the second component, two new search strategies are used to balance the exploitation and exploration of the algorithm through a random probability rule. In other aspect, our algorithm has a very simple structure and thus is easy to implement. To verify the performance of PSCS, 30 benchmark functions chosen from literature are employed. The results show that the proposed PSCS algorithm clearly outperforms the basic CS and PSO algorithm. Compared with some evolution algorithms (CLPSO, CMA-ES, GL-25, DE, OXDE, ABC, GOABC, FA, FPA, CoDE, BA, BSA, BDS and SDS) from literature, experimental results indicate that Communicated by V. Loia.the proposed algorithm performs better than, or at least comparable to state-of-the-art approaches from literature when considering the quality of the solution obtained. In the last part, experiments have been conducted on two real-world optimization problems including the spread spectrum radar poly-phase code design problem and the chaotic system. Simulation results demonstrate that the proposed algorithm is very effective.

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“…Typically, image segmentation based on fuzzy C-means proposed by Bexdek [1], mean shift filters in-vented by Comaniciu [2] and nonlinear diffusion exploited by Perona [3] have become the widely adopted methods in image processing. It was found that the thresholding technique is the most popular technique out of all the existing approaches used for segmentation of various types of images [4].…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive Multiple Evolution Algorithms for Image Segmentation Using Multilevel Thresholding

Sun

Lin³

et al. 2015

Communications in Computer and Information Science

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Abstract. Multilevel thresholding based on Otsu method is one of the most popular image segmentation techniques. However, when the number of thresholds increases, the consumption of CPU time grows exponentially. Although the evolution algorithms are helpful to solve this problem, for the high-dimensional problems, the Otsu methods based on the classical evolution algorithms may get trapped into local optimal or be instability due to the inefficiency of local search. To overcome such drawback, this paper employs the self-adaptive multiple evolution algorithms (MEAs), which automatically protrudes the core position of the excellent algorithm among the selected algorithms. The tests against 10 benchmark functions demonstrate that this multi-algorithms is fit for most problems. Then, this optimizer is applied to image multilevel segmentation problems. Experimental results on a variety of images provided by the Berkeley Segmentation Database show that the proposed algorithm can accurately and stably solve this kind of problems.

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Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm

Cited by 231 publications

References 23 publications

Automatic Roi Extraction in Noisy Medical Images

Automatic Roi Extraction in Noisy Medical Images

A particle swarm inspired cuckoo search algorithm for real parameter optimization

Self-adaptive Multiple Evolution Algorithms for Image Segmentation Using Multilevel Thresholding

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