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首页> 外文会议>Image Processing pt.3; Progress in Biomedical Optics and Imaging; vol.8,no.31; Proceedings of SPIE-The International Society for Optical Engineering; vol.6512 pt.3 >Fully Automatic Lesion Boundary Detection in Ultrasound Breast Images
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Fully Automatic Lesion Boundary Detection in Ultrasound Breast Images

机译：超声乳腺图像中的全自动病变边界检测

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摘要

We propose a novel approach to fully automatic lesion boundary detection in ultrasound breast images. The novelty of the proposed work lies in the complete automation of the manual process of initial Region-of-Interest (ROI) labeling and in the procedure adopted for the subsequent lesion boundary detection. Histogram equalization is initially used to pre-process the images followed by hybrid filtering and multifractal analysis stages. Subsequently, a single valued thresholding segmentation stage and a rule-based approach is used for the identification of the lesion ROI and the point of interest that is used as the seed-point. Next, starting from this point an Isotropic Gaussian function is applied on the inverted, original ultrasound image. The lesion area is then separated from the background by a thresholding segmentation stage and the initial boundary is detected via edge detection. Finally to further improve and refine the initial boundary, we make use of a state-of-the-art active contour method (i.e. gradient vector flow (GVF) snake model). We provide results that include judgments from expert radiologists on 360 ultrasound images proving that the final boundary detected by the proposed method is highly accurate. We compare the proposed method with two existing state-of-the-art methods, namely the radial gradient index filtering (RGI) technique of Drukker et. Al. and the local mean technique proposed by Yap et. Al., in proving the proposed method's robustness and accuracy.
机译：我们提出了一种在超声乳腺图像中全自动病变边界检测的新颖方法。拟议工作的新颖之处在于，可以完全自动化初始兴趣区域（ROI）标记的手动过程，并采用后续病变边界检测所采用的程序。直方图均衡化最初用于预处理图像，然后进行混合滤波和多重分形分析阶段。随后，使用单值阈值分割阶段和基于规则的方法来识别病变ROI和用作种子点的关注点。接下来，从这一点开始，将各向同性高斯函数应用于倒置的原始超声图像。然后，通过阈值分割阶段将病变区域与背景分离，并通过边缘检测来检测初始边界。最后，为了进一步改善和完善初始边界，我们使用了最先进的主动轮廓方法（即梯度矢量流（GVF）蛇形模型）。我们提供的结果包括放射专家对360幅超声图像的判断，证明通过所提出的方法检测出的最终边界非常准确。我们将提出的方法与两个现有的最先进的方法进行比较，即Drukker等人的径向梯度指数滤波（RGI）技术。铝和Yap等人提出的局部均值技术。 Al。，在证明所提出的方法的鲁棒性和准确性。

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