光电学院外文翻译论文

K. Deep et al.(Eds.):Proceedings of the I附rnational 131， pp 891~springerlinkcom © Springer India 2012
，
Conference on SocProS
2011，
AISC
89 2
S. Kumar， M. Pant， andA.K. Ray
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Abstract. Image segmentation played a vital role in medical imaging system. With the help of image segmentation pulmonary parenchyma can be detected from multi sliced CT images. Pulmonary diseases such as lung cancer， tumor， and mass cells can be detected with 2D Otsu algorithm. 2D Otsu algorithm is a well-known image segmentation method. In CT images segmentation 2D Otsu playa vital role. Main drawback of 2D Otsu method is its computation complexity and computational time. In this paper 2D Otsu algorithm has implemented with Differential Evolution (DE) Algorithm. This results in reducing the computational complexity as well as computational time. Further the results are compared with 2D Otsu and 2D Otsu with PSO， which proves the efficiency of using DE with 2D Otsu.
An image with size MxN can be represented by a 2D gray level intensity function f(x， y). The value of开x， y) is the gray level， ranging from 0 to L-l， where L is thenumber of distinct gray levels. In a 2D thresholding method ， the gray level of a pixel and its local average gray level are both used. The local average gray level is also divided into the same L values， let g衍， y) be the function of the local average gray level [3].
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Segmentation of CT Lung Images Based on 2D Otsu Optimized
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Keywords: Segmentation， CT Lung image， Pulmonary， parenchyma， DE， Thresholding， 2D Otsu.
1 Introduction
CT (Computed Tomography) scan is the standard for pulmonary imaging.It provides high spatial and high temporal resolution， excellent contrast resolution for the pulmonary structures and surrounding anatomy， and the ability to gather a complete three-dimensional (3-D) volume of human thorax in a single breath hold [1]. Pulmonary CT images have been used for applications such as lung parenchyma density analysis[习， airway analysis and lung and diaphragm mechanics analysis. Firstly， lD Otsu method was used for segmenting the lung CT images. But lD Otsu method gives better result only for high contrast images than for low contrast images. So a better 2D Otsu method was proposed for low contrast lung CT images. In this paper a new approach will be suggested for lung CT images segmentation. In this approach Differential Evolution (DE) is used with 2D Otsu method for optimizing the thresholding value of an image for segmentation in less computation time and less computation complexity， and results will be compared with 2D Otsu method and with 2D Otsu with PSO (Particle Swarm Otimization). In first section of the paper introduction of 2D Otsu method ， in second section
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introduction of Differential Evolution， and then the proposed algorithm and finally results and conclusion.
2 2D Otsu Method 2.1 Two-Dimensional Histogram
Segmentation of CT Lung Images Based on 2D Otsu Optimized by Differential Evolution
Sushil Kumar， Millie Pant， andA.K. Ray Indian Institnte of Technology Roorkee， India {kumarsushiliitr，millidma，akray}@gmail.com
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