access icon free Deploying swarm intelligence in medical imaging identifying metastasis, micro-calcifications and brain image segmentation

This study proposes an umbrella deployment of swarm intelligence algorithm, such as stochastic diffusion search for medical imaging applications. After summarising the results of some previous works 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 the aorta is demonstrated along with its performance in detecting the nasogastric tube in chest X-ray. The swarm intelligence algorithm presented in this study 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. In addition, 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 is used to train the LVQ which eliminates the iteration-dependent nature of LVQ. The proposed methodology is used to detect the tumour regions in the abnormal MR brain images.

Inspec keywords: diagnostic radiography; biomedical MRI; particle swarm optimisation; medical disorders; neurophysiology; computerised tomography; diseases; swarm intelligence; medical image processing; image segmentation; iterative methods; bone; learning (artificial intelligence); brain; tumours

Other keywords: bone scans; umbrella deployment; medical imaging identifying metastasis; particle swarm optimisation; iteration-dependent nature; metastasis identification; CT imaging; tumour regions; nasogastric tube; abnormal MR brain imaging; aorta; hybrid swarm intelligence-learning vector quantisation approach; magnetic resonance brain image segmentation; mammographs; microcalcifications; chest X-ray; stochastic diffusion

Subjects: Interpolation and function approximation (numerical analysis); Artificial intelligence (theory); Optimisation techniques; X-rays and particle beams (medical uses); Medical magnetic resonance imaging and spectroscopy; Interpolation and function approximation (numerical analysis); Patient diagnostic methods and instrumentation; Knowledge engineering techniques; X-ray techniques: radiography and computed tomography (biomedical imaging/measurement); Optical, image and video signal processing; Computer vision and image processing techniques; Numerical approximation and analysis; Biology and medical computing; Biophysics of neurophysiological processes; Biomedical magnetic resonance imaging and spectroscopy; Optimisation techniques

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