Earticle

초록

영어

Chest radiography which is the most common imaging method for lung is difficult to distinguish the lung vessels and nodules due to characteristics of representing the chest in a single image and shading occurred by organs. Computed Tomography(CT) scan has excellent lung nodule detection sensitivity because it produces chest images as volume data, but it has a large amount of exposure dose and is expensive. Chest tomosynthesis which generates volume data through continuous shooting comes to the forefront as an early lung cancer screening method with high lung nodule detection sensitivity than chest radiography and low-dose than CT image. However, chest tomosynthesis is difficult to have computer-based automatic segmentation because of blurring occurred while generating the image. Therefore, we propose prediction-based segmentation improvement method based on the central slices with less blurring after performing lung segmentation using region-growing. Using the proposed method, it is to improve the lung segmentation performance by improving the incorrect segmentation results on the outer slices where many blurring occurs. The experiment results showed the improvement of incorrectly segmented lung region.

목차

Abstract
 1. Introduction
 2. Chest Tomosyntehsis
 3. Proposed Chest Tomosynthesis Segmentation
  3.1. Rescaling the Original Image
  3.2. Segmentation Using Region-Growing Method
  3.3. Predication-based Segmentation Improvement Method
 4. Experimental Results
 5. Conclusions
 Acknowledgement
 References

키워드

저자

• Seung-Hoon Chae [ The Research Institute of IT, Chosun University, Korea ]
• Jeongwon Lee [ Electronics and Telecommunications Research Institute, Korea ]
• Chulho Won [ Dept. of Electrical and Computer Engineering, California State University, Fresno, USA ]
• Sung Bum Pan [ Dept. of Electronics Engineering, Chosun University, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605