文章基本信息

标题：Automated Segmentation and Hybrid Classifier for Identifying Medical Image
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作者：Tak-Yee Wong ; Ching-Hsue Cheng
期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2013
卷号：6
期号：1
出版社：SERSC
摘要：The high prevalence of lung cancer, many researcher concerns about diagnosing pulmo- nary lesions in chest computed tomography (CT). However, specialists would spend a great amount of their time and effort to analysis those CT scans. And the inter-reader variability in the detection of nodules by specialists may exist. Therefore, many automated methods have proposed methods for automatic diagnosis to assist artificial inspection. This study proposes a novel hybrid method to initially classify lungs images. Firstly, adjusting the contrast of chest images can change those images from indistinct to clear, and then use the proposed novel hybrid method to automated identification CT images. From the experiments, this paper can obtain three contributions: (1) Proposed segmentation algorithm can refine the lungs regions and improve the classification performance. (2) The proposed method can be execut- ed before doctor diagnosis or computer-aided system, which can be sure that input CT image need to be detected out the actual positions, shapes or other information of nodules. (3) The results display a higher accuracy in proposed rough classifier based on DWPT-SVD than other classification methods, which verifies that proposed method can reduce time and cost of lung nodule diagnosis.
关键词：Computed tomography; Segmentation algorithm; Singular value decomposition;(SVD); Discrete wavelet packets transform (DWPT)