| 引用本文: | 徐超,周东翔,刘云辉,等.光学显微结核杆菌图像的区域分割算法.[J].国防科技大学学报,2014,36(5):79-86.[点击复制] |
| XU Chao,ZHOU Dongxiang,LIU Yunhui,et al.A segmentation algorithm for optical microscopic mycobacterium tuberculosis images based on region segmentation[J].Journal of National University of Defense Technology,2014,36(5):79-86[点击复制] |
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| 光学显微结核杆菌图像的区域分割算法 |
| 徐超1, 周东翔1, 刘云辉2, 樊玮虹1 |
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(1.国防科技大学 电子科学与工程学院，湖南 长沙 410073;2.香港中文大学 机械与自动化工程系，中国 香港 99077)
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| 摘要: |
| 为提高结核杆菌目标的分割精度，提出了一种区域级的光学显微结核杆菌图像分割算法。通过顶帽—底帽变换增强彩色图像对比度，融合图像局部特征和全局信息计算图像梯度，利用分水岭算法实现对图像的初始分割；对分割区域采用相邻区域最大相似度准则进行合并，从而得到完整的目标区域；根据结核杆菌图像的特点，通过分析结核杆菌目标区域的颜色特性，采用多阈值分割的方法滤除区域中的杂质，实现对结核杆菌目标的分割。实验结果表明，可以分割出目标对比度低以及饱和度过低的结核杆菌目标，并且对不同染色背景的图像均能取得较好的分割结果。 |
| 关键词: 图像处理 图像分割 分水岭 区域合并 结核杆菌 |
| DOI：10.11887/j.cn.201405014 |
| 投稿日期：2014-03-19 |
| 基金项目:国家自然科学基金资助项目（61375032） |
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| A segmentation algorithm for optical microscopic mycobacterium tuberculosis images based on region segmentation |
| XU Chao1, ZHOU Dongxiang1, LIU Yunhui2, FAN Weihong1 |
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(1.College of Electronics Science and Engineering, National University of Defense Technology, Changsha 410073, China;2.Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong 9999077, China)
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| Abstract: |
| To improve the segmentation accuracy of mycobacterium tuberculosis (MTB) objects, a segmentation algorithm for optical microscopic MTB images in region level was proposed. Top-bottom hat transform was used to enhance the contrast of the color images, and the image gradients were computed by comprising local features and global information of the images. Watershed algorithm was employed to implement the initial segmentation. Segmented regions were then merged by using the maximum similarity criterion in adjacent regions in order to obtain integrated object regions. The method of multi-threshold segmentation in terms of the color characteristics of MTB object regions was adopted to filter the impurities and to realize the segmentation of MTB objects. Experimental results indicate that the proposed algorithm can segment MTB objects which have low contrast and saturation and can obtain well-segmented results for images in different dyeing backgrounds. |
| Keywords: image processing image segmentation watershed region merging mycobacterium tuberculosis |
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