Identifying metastasis in bone scans with Stochastic Diffusion Search

al-Rifaie, Mohammad Majid; Aber, Ahmed

doi:10.1109/itime.2012.6291355

Cited by 7 publications

(7 citation statements)

References 9 publications

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“…This work was well received as a potential educational tool for doctors in training and medical students. This led to the extension of the research in [5], [6] where the application of this swarm intelligence technique on bone scan was introduced in further details in different venues for researchers with medical and computer backgrounds. Later in [7], the statistical and mathematical models were presented for bone scans, and the application of the technique was extended to mammography.…”

Section: A Previous Work and Summary Of Current Researchmentioning

confidence: 99%

Deploying swarm intelligence in medical imaging identifying metastasis, micro‐calcifications and brain image segmentation

al-Rifaie

Aber²,

Hemanth

2015

IET syst. biol.

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Abstract-This paper proposes an umbrella deployment of swarm intelligence algorithm such as Stochastic Diffusion Search for medical imaging applications. After summarising the results of some previous work which shows how the algorithm assists in the identification of metastasis in bone scans and microcalcifications on mammographs, for the first time, the use of the algorithm in assessing the CT images of aorta is demonstrated along with its performance in detecting the nasogastric tube in chest X-ray. The swarm intelligence algorithm presented in this paper is adapted to address these particular tasks and its functionality is investigated by running the swarms on sample CT images and X-rays whose status have been determined by senior radiologists. Additionally, a hybrid swarm intelligence-Learning Vector Quantisation (LVQ) approach is proposed in the context of Magnetic Resonance (MR) brain image segmentation. The Particle Swarm Optimisation (PSO) is used to train the LVQ which eliminates the iterationdependent nature of LVQ. The proposed methodology is used to detect the tumor regions in the abnormal MR brain images.

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Section: A Previous Work and Summary Of Current Researchmentioning

confidence: 99%

Deploying swarm intelligence in medical imaging identifying metastasis, micro‐calcifications and brain image segmentation

al-Rifaie

Aber²,

Hemanth

2015

IET syst. biol.

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“…Among the possible future research are investigating the algorithm for an adaptive disturbance threshold, dt. Additionally, optimising multi-objective real world problems is yet to be researched; this would be a continuation of an earlier set of works on the deployment of population-based algorithms for detecting metastasis in bone scans and calcifications in mammographs [27]. At last, but not least, given the demonstrated stable and convergenceindependent diversity of Dispersive Flies Optimisation (in the context of the presented benchmarks), another exciting future research is to investigate the performance of DFO in the context of dynamic optimisation problems.…”

Section: A Future Researchmentioning

confidence: 99%

Dispersive Flies Optimisation

al-Rifaie¹

2014

Annals of Computer Science and Information Systems

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Abstract-One of the main sources of inspiration for techniques applicable to complex search space and optimisation problems is nature. This paper proposes a new metaheuristic -Dispersive Flies Optimisation or DFO -whose inspiration is beckoned from the swarming behaviour of flies over food sources in nature. The simplicity of the algorithm, which is the implementation of one such paradigm for continuous optimisation, facilitates the analysis of its behaviour. A series of experimental trials confirms the promising performance of the optimiser over a set of benchmarks, as well as its competitiveness when compared against three other well-known population based algorithms (Particle Swarm Optimisation, Differential Evolution algorithm and Genetic Algorithm). The convergence-independent diversity of DFO algorithm makes it a potentially suitable candidate for dynamically changing environment. In addition to diversity, the performance of the newly introduced algorithm is investigated using the three performance measures of accuracy, efficiency and reliability and its outperformance is demonstrated in the paper.

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“…Therefore, considering this form of SDS, agents just communicate with the ones they are connected to. It was shown that a network with randomly connected agents (random graph), with small number of longrange connections, performs similar to standard SDS or ordered lattice with roughly the same number of connections 4 . The following conclusion has been drawn that restricting the number of interconnectivity in random or small-world networks -which is a lattice with a few additional number of long-range connections -does not have huge effect on the performance of SDS algorithm.…”

Section: Implementation On Hardwarementioning

confidence: 99%

“…Also, the rate of information spread is higher in random graphs and small-world networks than ordered lattices. Analysing the number of connections and the connection topology leads to the following conclusion: it has been argued that when a highdimensional problem is considered, the time at which one of the agents becomes active (time to hit [22]), T h , is bigger than the time required for 4 …”

Section: Implementation On Hardwarementioning

confidence: 99%

“…This work was well received as a potential educational tool for doctors in training and medical students. This led to the extension of the research in [4] where the application of this swarm intelligence technique on bone scan was introduced in further details in different venues for researchers with medical and computer backgrounds. Later in [12], the statistical and mathematical models were introduced for bone scans, and the application of the technique was extended to mammography.…”

Section: Medical Applicationsmentioning

confidence: 99%

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Stochastic Diffusion Search Review

al-Rifaie

Bishop

2013

Paladyn, Journal of Behavioral Robotics

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Stochastic Diffusion Search, first incepted in 1989, belongs to the extended family of swarm intelligence algorithms. In contrast to many nature-inspired algorithms, stochastic diffusion search has a strong mathematical framework describing its behaviour and convergence. In addition to concisely exploring the algorithm in the context of natural swarm intelligence systems, this paper reviews various developments of the algorithm, which have been shown to perform well in a variety of application domains including continuous optimisation, implementation on hardware and medical imaging. This algorithm has also being utilised to argue the potential computational creativity of swarm intelligence systems through the two phases of exploration and exploitation.

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Identifying metastasis in bone scans with Stochastic Diffusion Search

Cited by 7 publications

References 9 publications

Deploying swarm intelligence in medical imaging identifying metastasis, micro‐calcifications and brain image segmentation

Deploying swarm intelligence in medical imaging identifying metastasis, micro‐calcifications and brain image segmentation

Dispersive Flies Optimisation

Stochastic Diffusion Search Review

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