al-Rifaie Mohammad Majid, Aber Ahmed, Hemanth Duraiswamy Jude
Department of Computing, Goldsmiths, University of London, London SE14 6NW, UK.
Department of Cardiovascular Sciences, University of Leicester Royal Infirmary, Leicester, LE2 7LX, UK.
IET Syst Biol. 2015 Dec;9(6):234-44. doi: 10.1049/iet-syb.2015.0036.
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.
本研究提出了一种群体智能算法的总体部署，例如用于医学成像应用的随机扩散搜索。在总结了一些先前工作的结果后，这些结果展示了该算法如何协助识别骨扫描中的转移灶以及乳腺钼靶上的微钙化，首次展示了该算法在评估主动脉CT图像中的应用及其在胸部X光片中检测鼻胃管的性能。本研究中提出的群体智能算法经过调整以解决这些特定任务，并通过在由资深放射科医生确定状态的样本CT图像和X光片上运行群体来研究其功能。此外，在磁共振（MR）脑图像分割的背景下提出了一种混合群体智能-学习向量量化（LVQ）方法。粒子群优化用于训练LVQ，消除了LVQ的迭代依赖性。所提出的方法用于检测异常MR脑图像中的肿瘤区域。
