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首页> 外文会议>Image Perception, Observer Performance, and Technology Assessment; Progress in Biomedical Optics and Imaging; vol.7 no.32 >Automatic Image Quality Assessment for Uterine Cervical Imagery
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Automatic Image Quality Assessment for Uterine Cervical Imagery

机译：子宫宫颈图像的自动图像质量评估

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Uterine cervical cancer is the second most common cancer among women worldwide. However, its death rate can be dramatically reduced by appropriate treatment, if early detection is available. We are developing a Computer-Aided-Diagnosis (CAD) system to facilitate colposcopic examinations for cervical cancer screening and diagnosis. Unfortunately, the effort to develop fully automated cervical cancer diagnostic algorithms is hindered by the paucity of high quality, standardized imaging data. The limited quality of cervical imagery can be attributed to several factors, including: incorrect instrumental settings or positioning, glint (specular reflection), blur due to poor focus, and physical contaminants. Glint eliminates the color information in affected pixels and can therefore introduce artifacts in feature extraction algorithms. Instrumental settings that result in an inadequate dynamic range or an overly constrained region of interest can reduce or eliminate pixel information and thus make image analysis algorithms unreliable. Poor focus causes image blur with a consequent loss of texture information. In addition, a variety of physical contaminants, such as blood, can obscure the desired scene and reduce or eliminate diagnostic information from affected areas. Thus, automated feedback should be provided to the colposcopist as a means to promote corrective actions. In this paper, we describe automated image quality assessment techniques, which include region of interest detection and assessment, contrast dynamic range assessment, blur detection, and contaminant detection. We have tested these algorithms using clinical colposcopic imagery, and plan to implement these algorithms in a CAD system designed to simplify high quality data acquisition. Moreover, these algorithms may also be suitable for image quality assessment in telemedicine applications.
机译：子宫宫颈癌是全世界女性中第二大最常见的癌症。但是，如果可以早期发现，通过适当的治疗可以大大降低其死亡率。我们正在开发一种计算机辅助诊断（CAD）系统，以促进阴道镜检查对宫颈癌的筛查和诊断。不幸的是，由于缺乏高质量，标准化的成像数据，阻碍了开发全自动宫颈癌诊断算法的努力。子宫颈图像质量有限的原因可归结为以下几个因素，包括：不正确的仪器设置或位置，闪烁（镜面反射），由于聚焦不良而导致的模糊以及物理污染物。闪光消除了受影响像素中的颜色信息，因此可以在特征提取算法中引入伪像。导致动态范围不足或关注区域过于受限的仪器设置可能会减少或消除像素信息，从而使图像分析算法不可靠。聚焦不良会导致图像模糊，进而丢失纹理信息。另外，各种物理污染物（例如血液）会遮挡所需的场景，并减少或消除来自受影响区域的诊断信息。因此，应将自动反馈提供给调查人员，以促进采取纠正措施。在本文中，我们描述了自动图像质量评估技术，其中包括感兴趣区域的检测和评估，对比度动态范围评估，模糊检测和污染物检测。我们已经使用临床阴道镜图像测试了这些算法，并计划在旨在简化高质量数据采集的CAD系统中实施这些算法。此外，这些算法也可能适用于远程医疗应用中的图像质量评估。

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