RESUMEN
Lung cancer is still the most concerned disease around the world. Lung nodule generates in the pulmonary parenchyma which indicates the latent risk of lung cancer. Computer-aided pulmonary nodules detection system is necessary, which can reduce diagnosis time and decrease mortality of patients. In this study, we have proposed a new computer aided diagnosis (CAD) system for detection of early pulmonary nodule, which can help radiologists quickly locate suspected nodules and make judgments. This system consists of four main sections: pulmonary parenchyma segmentation, nodule candidate detection, features extraction (total 22 features) and nodule classification. The publicly available data set created by the Lung Image Database Consortium (LIDC) is used for training and testing. This study selects 6400 slices from 80 CT scans containing totally 978 nodules, which is labeled by four radiologists. Through a fast segmentation method proposed in this paper, pulmonary nodules including 888 true nodules and 11,379 false positive nodules are segmented. By means of an ensemble classifier, Random Forest (RF), this study acquires 93.2, 92.4, 94.8, 97.6% of accuracy, sensitivity, specificity, area under the curve (AUC), respectively. Compared with support vector machine (SVM) classifier, RF can reduce more false positive nodules and acquire larger AUC. With the help of this CAD system, radiologist can be provided with a great reference for pulmonary nodule diagnosis timely.
Asunto(s)
Diagnóstico por Computador/métodos , Neoplasias Pulmonares/diagnóstico , Neoplasias Pulmonares/patología , Nódulos Pulmonares Múltiples/diagnóstico , Nódulos Pulmonares Múltiples/patología , Inteligencia Artificial , Humanos , Interpretación de Imagen Asistida por Computador/métodos , Neoplasias Pulmonares/diagnóstico por imagen , Nódulos Pulmonares Múltiples/diagnóstico por imagen , Sensibilidad y Especificidad , Tomografía Computarizada por Rayos X/métodosRESUMEN
The security of Body Sensor Network (BSN) has become a vital concern, as the massive development of BSN applications in healthcare. A family of biometrics based security methods has been proposed in the last several years, where the bio-information derived from physiological signals is used as entity identifiers (EIs) for multiple security purposes, including node recognition and keying material protection. Among them, a method named as Physiological Signal based Key Agreement (PSKA) was proposed to use frequency-domain information of physiological signals together with Fuzzy Vault scheme to secure key distribution in BSN. In this study, the PSKA scheme was firstly analyzed and evaluated for its practical usage in terms of fuzzy performance, the result of which indicates that the scheme is not as good as claimed. An improved scheme with the deployment of Fuzzy Vault and error correcting coding was then proposed, followed by simulation analysis. The results indicate that the improved scheme is able to improve the performance of Fuzzy Vault and thus the success rate of authentication or key distribution between genuine nodes of a BSN.