Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Related Works
  2.1 Singular Value Decomposition
  2.2 Discrete Wavelet Packets Transform
  2.3 Rough Sets Theory
 3. Proposed Method
 4. Experimental and Results
 5. Conclusions
 References

키워드

저자

• Tak-Yee Wong [ Department of Radiology, St. Martin de Porres Hospital ]
• Ching-Hsue Cheng [ Department of Information Management, National Yunlin University of Science and Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605