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Development of a computer tool to detect and classify nodules in ultrasound breast images

机译:开发一种用于对超声乳腺图像中的结节进行检测和分类的计算机工具

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Due to the high incidence rate of breast cancer in women, many procedures have been developed to assist the diagnosis and early detection. Currently, ultrasonography has proved as a useful tool in distinguishing benign and malignant masses. In this context, the computer-aided diagnosis schemes have provided to the specialist a second opinion more accurately and reliably, minimizing the visual subjectivity between observers. Thus, we propose the application of an automatic detection method based on the use of the technique of active contour in order to show precisely the contour of the lesion and provide a better understanding of their morphology. For this, a total of 144 images of phantoms were segmented and submitted to morphological operations of opening and closing for smoothing the edges. Then morphological features were extracted and selected to work as input parameters for the neural classifier Multilayer Perceptron which obtained 95.34% correct classification of data and Az of 0.96.
机译:由于女性乳腺癌的高发率,已开发出许多程序来辅助诊断和早期发现。目前,超声检查已被证明是区分良性和恶性肿块的有用工具。在这种情况下,计算机辅助诊断方案为专家提供了更准确,更可靠的第二意见,从而最大程度地减少了观察者之间的视觉主观性。因此,我们提出一种基于主动轮廓技术的自动检测方法的应用,以精确显示病变轮廓并更好地了解其形态。为此,总共分割了144个幻像图像,并进行了打开和关闭以平滑边缘的形态操作。然后提取形态特征并选择作为神经分类器多层感知器的输入参数,该多层分类器获得了95.34%的正确数据分类和0.96的Az。

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