A Strategy for SPN Detection Based on Biomimetic Pattern Recognition and Knowledge-Based Features

The 22nd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE 2009), Tainan, Taiwan ROC, 24-27 June 2009Image processing techniques have proved to be effective in improving the diagnosis of lung nodules. In this paper, we present a strategy for solitary pulmonary nodules (SPN) detection using radiology knowledge-based feature extraction scheme and biomimetic pattern recognition (BPR). The proposed feature extraction scheme intends to synthesize comprehensive information of SPN according to radiology knowledge, e.g. grey level features, morphological, texture and spatial context features. Using support vector machine (SVM), Naive Bayes (NB) and BPR as the classifiers to evaluate different feature representation schemes, our experimental study shows that the proposed radiology knowledge-based features can significantly improve the classification effectiveness of SPN detection from nonnodules, in terms of accuracy and F1 value, regardless of the classifiers used. We also note that BPR can deliver a consistent performance using our knowledge-based features, even the ratios between nonnodules and nodules are quite different in the training set.Department of Industrial and Systems EngineeringRefereed conference pape

Automated detection of lung nodules in computed tomography images: a review

Lung nodules refer to a range of lung abnormalities the detection of which can facilitate early treatment for lung patients. Lung nodules can be detected by radiologists through examining lung images. Automated detection systems that locate nodules of various sizes within lung images can assist radiologists in their decision making. This paper presents a study of the existing methods on automated lung nodule detection. It introduces a generic structure for lung nodule detection that can be used to represent and describe the existing methods. The structure consists of a number of components including: acquisition, pre-processing, lung segmentation, nodule detection, and false positives reduction. The paper describes the algorithms used to realise each component in different systems. It also provides a comparison of the performance of the existing approaches.S.L.A. Lee, A.Z. Kouzani and E.J. H

The NA49 Data Acquisition System

NA49 is a fixed-target, heavy-ion experiment at the 200 GeV/nucleon Pb beam of the SPS at CERN, expected to take data in late 1994. Because of the unprecedented number of charged particles (up to 2000) emerging from a single Pb-induced nuclear reaction at these energies, the demands on various parts of the experimental setup are extreme. For the data acquisition system a burst input data rate of 750 Mbyte/sec from more than 150,000 electronic channels has to be buffered and compressed to allow the recording of events to magnetic media at rates around 15 Mbyte/sec with reasonable effort and cost. © 1994 IEEE