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摘 要 随着计算机技术的迅速发展，数字图像处理技术在医学领域的研究和应用日益深入和广泛。现代医学已越来越离不开医学图像处理技术。医学图像处理技术在临床诊断、教学科研等方面发挥了重要的作用。计算机图像处理技术与影像技术的结合从根本上改变了医务人员进行诊断的传统方式。充分地利用这些技术可以提高诊断的正确性和准确性，提高诊断效率，降低医疗成本，可以更加充分地发挥各种医疗设备的功能。而且，随着数字化、智能化进程的深人，图像处理技术在医疗卫生领域将会有更加广阔的应用前景。 Java是Sun公司推出的一种面向对象编程语言。Java非常适合于企业网络和Internet环境，现已成为Internet中最受欢迎、最有影响的编程语言之一。目前国内使用Java语言开发的图像处理系统比较少，这也增加了这方面的研究价值。 本文首先对图像增强和图像分割中的几种算法进行了介绍，包括线性灰度变换，伪彩色处理，平滑处理，中值滤波，阈值分割，边缘检测等。然后用Java语言对上述各算法编程实现，并设计Java GUI（图形用户界面）用来显示图像处理的结果，以及创建一个数据库用于存储医学图像。 关键词：医学图像；图像增强；图像分割；面向对象 AbstractAs the computertechnique’s quickly development, the image process technique having been moredeeply and widely in the use and study of medical science. The modern medicalscience can not work well without the medical image processing technology; ithas made an important use in clinical diagnosis and education study. The combinationof the image processing technique and imaging technique has changed the waythat traditional diagnosis. Make adequately use of this techniques will beincrease accuracy, increase the efficiency of diagnosis, decrease the cost ofmedical treatment and make the most use of function with medical treatmentequipments. Moreover, as the deeply with the arithmetic figure and theintelligence, the image processing technique will have a more wonderful future. Java is a kind ofobject-oriented programming language from the company of Sun. The Java isbecoming a most welcome and influence programming language which suits for thebusiness network and the environment of internet. Currently, use Java language todeveloped image processing system is not very frequency in our country. So,this is a cause of increasing the value of study. This projectintroducessome kinds of algorithms in image enhancement and imagesegmentation.It includes linear grey level transformation, pseudo-color processing, smoothprocessing, median filter, threshold segmentation, edge detection and so on. Then, use Java toprogram and realize. And show the result of image processing using Java GUI (Graphical User Interface), as well as create adatabase to stock medical image. Key Words: medicalimage; imageenhancement; image segmentation; object-oriented
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