A multilevel color image segmentation technique based on cuckoo search algorithm and energy curve

Pare, S.; Kumar, Arun; Bajaj, Varun; Singh, Girish Kumar

doi:10.1016/j.asoc.2016.05.040

Cited by 104 publications

(46 citation statements)

References 56 publications

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“…During the process of food searching, the birds and animals make a random walk, wherein, the step by step movement and the migration direction are taken into account with a reason to provide an optimum solution for performing food search. CS algorithm utilizes this social behavior of birds and animals to provide a conclusive optimal solution, and conceptual characteristics of CS algorithms is far better than other random walk problems . Based on the literature survey and references made in this article, it is evident that many nature inspired algorithms have been developed and used for medical image segmentation.…”

Section: Methodsmentioning

confidence: 99%

“…Further, the Levy distribution should comply with the specifications made in the generation of steps. The three significant rules required for the optimization performance of CS algorithm are A cuckoo lays an egg at a time instant and the same is dumped in a nest, which is chosen at random. The nests of best structure and comprising the best quality of eggs would provide better yield and the eggs would pass on to the next generation. To lay out an egg by a cuckoo, a host nest is affirmed based on its availability. Identification of the cuckoo laid egg by a host bird is denoted with a probability of P a ∈ [0,1] …”

Section: Methodsmentioning

confidence: 99%

“…Depending on the aforementioned rules, the basic steps for CS algorithm are constituted as in the steps enlisted below…”

Section: Methodsmentioning

confidence: 99%

“…In the above mentioned equation, α represents the step size and the same should be as α > 0 . In this article, the value of α is assigned as 1.…”

Section: Methodsmentioning

confidence: 99%

“…Scaling parameter λ, offers random walk and random step length is derived from a Levy distribution explained in Equation , which possesses an infinite variance and infinite mean. Levy distribution is briefed in Equation . Usually, random walk values are provided by Levy flights and on the other hand, larger random step lengths are derived using Levy distribution and it is given as

Levy (λ) = t^{- λ} where 1 < λ \leq 3

…”

Section: Methodsmentioning

confidence: 99%

See 4 more Smart Citations

A fully automated hybrid methodology using Cuckoo‐based fuzzy clustering technique for magnetic resonance brain image segmentation

Alagarsamy

Kamatchi

Govindaraj

et al. 2017

Int J Imaging Syst Tech

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This article aims at developing an automated hybrid algorithm using Cuckoo Based Search (CBS) and interval type‐2 fuzzy based clustering, so as to exhibit efficient magnetic resonance (MR) brain image segmentation. An automatic MR brain image segmentation facilitates and enables a radiologist to have a brief review and easy analysis of complicated tumor regions of imprecise gray level regions with minimal user interface. The tumor region having severe intensity variations and suffering from poor boundaries are to be detected by the proposed hybrid technique that could ease the process of clinical diagnosis and this tends to be the core subject of this article. The ability of the proposed technique is compared using standard comparison parameters such as mean squared error, peak signal to noise ratio, computational time, Dice Overlap Index, and Jaccard Tanimoto Coefficient Index. The proposed CBS combined with interval type‐2 fuzzy based clustering produces a sensitivity of 0.7143 and specificity of 0.9375, which are far better than the conventional techniques such as kernel based, entropy based, graph‐cut based, and self‐organizing maps based clustering. Appreciable segmentation results of tumor region that enhances clinical diagnosis is made available through this article and two of the radiologists who have hands on experience in the field of radiology have extended their support in validating the efficiency of the proposed methodology and have given their consent in utilizing the proposed methodology in the processes of clinical oncology.

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Section: Methodsmentioning

confidence: 99%