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The Study of Image Segmentation Based on the Combination of the Wavelet Multi-scale Edge Detection and the Entropy iterative threshold Selection

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Chinese Journal of Biomedical Engineering(English Edition) 2013年第4期22卷 154-160页

作者： ZHANG Qian HE Jian-feng MA Lei PAN Li-peng LIU Jun-qing CHEN Hong-lei Kunming University of Science and Technology Institute of Information Engineering and Automation

This paper proposes an image segmentation method based on the combination of the wavelet multi-scale edge detection and the entropy iterative threshold *** for segmentation is divided into two parts by high- and *** the high-frequency part the wavelet multiscale was used for the edge detection,and the low-frequency part conducted on segmentation using the entropy iterative threshold selection *** the consideration of the image edge and region,a CT image of the thorax was chosen to test the proposed method for the segmentation of the *** results show that the method is efficient to segment the interesting region of an image compared with conventional methods.

关键词： wavelet multi-scale entropy iterative threshold lung images

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An automated pulmonary parenchyma segmentation method based on an improved region growing algorithm in PET-CT imaging

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Frontiers of Computer Science 2016年第1期10卷 189-200页

作者： Juanjuan ZHAO Guohua JI Xiaohong HAN Yan QIANG Xiaolei LIAO College of Computer Science and Technology Taiyuan University of Technology Taiyuan 030024 China Key Laboratory of Advanced Transducers and Intelligent Control Systems Taiyuan University of Technology Taiyuan 030024 China

To address the incomplete problem in pulmonary parenchyma segmentation based on the traditional methods, a novel automated segmentation method based on an eight- neighbor region growing algorithm with left-right scanning and four-corner rotating and scanning is proposed in this pa- per. The proposed method consists of four main stages： image binarization, rough segmentation of lung, image denoising and lung contour refining. First, the binarization of images is done and the regions of interest are extracted. After that, the rough segmentation of lung is performed through a general region growing method. Then the improved eight-neighbor region growing is used to remove noise for the upper, mid- dle, and bottom region of lung. Finally, corrosion and ex- pansion operations are utilized to smooth the lung boundary. The proposed method was validated on chest positron emis- sion tomography-computed tomography （PET-CT） data of 30 cases from a hospital in Shanxi, China. Experimental results show that our method can achieve an average volume overlap ratio of 96.21 ± 0.39% with the manual segmentation results. Compared with the existing methods, the proposed algorithm segments the lung in PET-CT images more efficiently and ac- curately.

关键词： pulmonary parenchyma segmentation bot-tom region of lung image binarization iterative threshold seeded region growing four-corner rotating and scanning,denoising contour refining PET-CT

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