首页> 外文会议>Conference on Imaging, Manipulation, and Analysis of Biomolecules, Cell, and Tissues; 20080121-23; San Jose,CA(US) >The possibilities of improvement the sensitivity of cancer fluorescence diagnostics by computer image processing
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The possibilities of improvement the sensitivity of cancer fluorescence diagnostics by computer image processing

机译:通过计算机图像处理提高癌症荧光诊断灵敏度的可能性

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Background: Fluorescence diagnostics uses the ability of tissues to fluoresce after exposition to a specific wavelength of light. The change in fluorescence between normal and progression to cancer allows to see early cancer and precancerous lesions often missed by white light. Aim: To improve by computer image processing the sensitivity of fluorescence images obtained during examination of skin, oral cavity, vulva and cervix lesions, during endoscopy, cystoscopy and bronchoscopy using Xillix ONCOLIFE. Methods: Function of image f(x,y):R~2→R~3 was transformed from original color space RGB to space in which vector of 46 values refers to every point labeled by defined xy-coordinates- f(x,y):R~2→R~(46). By means of Fisher discriminator vector of attributes of concrete point analalyzed in the image was reduced according to two defined classes defined as pathologic areas (foreground) and healthy areas (background). As a result the highest four fisher's coefficients allowing the greatest separation between points of pathologic (foreground) and healthy (background) areas were chosen. In this way new function f(x,y):R~2→R~4 was created in which point x,y corresponds with vector Y, H, a~*, c_2. In the second step using Gaussian Mixtures and Expectation-Maximisation appropriate classificator was constructed. This classificator enables determination of probability that the selected pixel of analyzed image is a pathologically changed point (foreground) or healthy one (background). Obtained map of probability distribution was presented by means of pseudocolors. Results: Image processing techniques improve the sensitivity, quality and sharpness of original fluorescence images. Conclusion: Computer image processing enables better visualization of suspected areas examined by means of fluorescence diagnostics.
机译:背景:荧光诊断法利用组织暴露于特定波长的光后发出荧光的能力。正常和癌症进展之间的荧光变化允许看到早期癌症和白光常漏掉的癌前病变。目的:通过计算机图像处理提高使用Xillix ONCOLIFE进行内窥镜检查,膀胱镜检查和支气管镜检查时在皮肤,口腔,外阴和子宫颈病变检查过程中获得的荧光图像的灵敏度。方法:将图像的函数f(x,y):R〜2→R〜3从原始色彩空间RGB转换为46个值的矢量指向由定义的xy坐标标记的每个点的空间f(x,y ):R〜2→R〜(46)。借助于费舍尔鉴别器,根据定义为病理区域(前景)和健康区域(背景)的两个已定义类别,减少了图像中已分析的混凝土点的属性向量。结果,选择了最高的四个费舍尔系数,从而使病理(前景)区域与健康(背景)区域之间的距离最大。这样,创建了新函数f(x,y):R〜2→R〜4,其中点x,y对应于向量Y,H,a〜*,c_2。在第二步中,使用高斯混合和期望最大化来构造适当的分类器。该分类器使得能够确定所选择的分析图像的像素是病理变化的点(前景)或健康的变化点(背景)的概率。利用伪色图表示获得的概率分布图。结果:图像处理技术提高了原始荧光图像的灵敏度,质量和清晰度。结论:计算机图像处理可以通过荧光诊断手段更好地可视化可疑区域。

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