As the development of medical imageology, radioexamination has become one of the main approaches of disease diagnosis. However, the society and hospital have not taken the hidden danger and harmfulness of radiation seriously. Through discussing the following eight aspects of radiation safety culture management, this paper aims to reduce the risk of radiation and ensure the safety of patients and medical staff: a) Improving the awareness of safety culture and the understanding of patients on safety culture; b) Consummating the safety management system of the radiation; c) Attaching importance to implementing the relevant laws and regulations of radiation; d) Mastering the examination indications, and especially the contraindications of radiation; e) Strengthening the clinical cooperation and exchange; f) Improving the staff’s ability to distinguish hidden danger and identify patients in high risk; g) Strengthening the nursing behavior safety management of the radiation department; and h) Strengthening the biological security management of the radiation department.
Amyloid β-protein (Aβ) deposition is an important prevention and treatment target for Alzheimer’s disease (AD), and early detection of Aβ deposition in the brain is the key to early diagnosis of AD. Magnetic resonance imaging (MRI) is the perfect imaging technology for the clinical diagnosis of AD, but it cannot display the plaque deposition directly. In this paper, based on two feature selection modes-filter and wrapper, chain-like agent genetic algorithm (CAGA), principal component analysis (PCA), support vector machine (SVM) and random forest (RF), we designed six kinds of feature learning classification algorithms to detect the information (distribution) of Aβ deposition through magnetic resonance image pixels selection. Firstly, we segmented the brain region from brain MR images. Secondly, we extracted the pixels in the segmented brain region as a feature vector (features) according to rows. Thirdly, we conducted feature learning on the extracted features, and obtained the final optimal feature subset by voting mechanism. Finally, using the final optimal selected features, we could find and mark the corresponding pixels on the MR images to show the information about Aβ plaque deposition by elastic mapping. According to the experimental results, the proposed pixel features learning methods in this paper could extract and reflect Aβ plaque deposition, and the best classification accuracy could be as high as 80%, thereby showing the effectiveness of the methods. The proposed methods can precisely detect the information of the Aβ plaque deposition, thereby being helpful for improving classification accuracy of diagnosis of AD.
