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Image processing and analysis in a dual-modality optoacoustic/ultrasonic system for breast cancer diagnosis

机译:双模态光声/超声系统中的图像处理和分析,用于乳腺癌诊断

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Coregistered optoacoustic (OA) and ultrasound (US) images obtained using a dual modality optoacoustic/ultrasonic breast imaging system are used together for enhanced diagnostic capabilities in comparison to each individual technology. Therefore, an operator-independent delineation of diagnostically relevant objects (in our case breast tumors) with subsequent automatic analysis of image features is required. We developed the following procedure: 1) Image filtering is implemented on a US image to minimize speckle noise and simultaneously preserve the sharpness of the boundaries of the extended objects; 2) Boundaries of the objects of interest are automatically delineated starting with an initial guess made by an operator; 3) Both US and OA images are analyzed using the detected boundaries (contrast, boundary sharpness, homogeneity of the objects and background, boundary morphology parameters are calculated). Calculated image characteristics can be used for statistically independent evaluation of structural information (US data) and vascularization (OA data) of the studied breast tissues. Operator-independent delineation of the objects of interest (e.g. tumors and blood vessels) is essential in clinical OA spectroscopy (using multiple laser wavelengths to quantify concentrations of particular tissue chromophores, such as oxy- and deoxy- hemoglobin, water, and lipids). Another potential application of the suggested image analysis algorithm could be in OA imaging system design, when system performance should be evaluated in terms of quality of the images reconstructed from the well-defined objects of interest. The discussed principles of image analysis are illustrated by using real clinical US and OA data.
机译:与每种单独技术相比,使用双模式光声/超声乳腺成像系统获得的共配准光声(OA)和超声(US)图像一起用于增强诊断能力。因此,需要诊断相关对象(在我们的情况下为乳腺肿瘤)的操作员无关的描述,以及随后对图像特征的自动分析。我们开发了以下过程:1)在美国图像上执行图像过滤,以最大程度地减少斑点噪声,并同时保留扩展对象边界的清晰度; 2)从操作员的初步猜测开始,自动划定感兴趣对象的边界; 3)使用检测到的边界对US和OA图像进行分析(对比度,边界清晰度,物体和背景的均匀性,边界形态参数的计算)。计算出的图像特征可用于对研究的乳腺组织的结构信息(US数据)和血管形成(OA数据)进行统计独立评估。在临床OA光谱学中(使用多个激光波长来定量特定组织发色团的浓度,例如氧和脱氧血红蛋白,水和脂质的浓度),重要的是要确定与操作对象无关的对象(例如肿瘤和血管)。建议的图像分析算法的另一个潜在应用可能是在OA成像系统设计中,此时应根据从明确定义的目标对象重建的图像的质量来评估系统性能。通过使用实际的临床US和OA数据说明了所讨论的图像分析原理。

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