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Feature extraction of the lesion in mammogram images using segmentation by minimizing the energy and orthogonal transformation adaptive

机译:通过最小化能量的分割和正交变换自适应对乳腺X线照片图像中的病变进行特征提取

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Segmentation and classification of breast masses in mammography play a crucial role in Computer Aided Diagnosis system (CAD). In this paper we propose an approach consisting of two methods. The first is the main stage in image processing which is the mammograms segmentation. This method is based on the theory of all levels and minimization of the energy of the active contour which enables the selection of regions of interest of the mammograms images. While the second method is based on the theory of adaptive orthogonal transformation that will calculate the informative characteristics of regions of interest of mammography images. The characteristics obtained by this computing method allow the increase of the diagnostic certainty. To illustrate the effectiveness of the method we present the results of experiments carried out on the basis of images MIAS mammograms.
机译:乳房X光检查中乳腺肿块的分割和分类在计算机辅助诊断系统(CAD)中起着至关重要的作用。在本文中,我们提出了一种由两种方法组成的方法。第一个是图像处理的主要阶段,即乳房X线照片分割。该方法基于所有级别的理论,并且使活动轮廓的能量最小化,这使得可以选择乳房X线照片的感兴趣区域。第二种方法基于自适应正交变换的理论,该理论将计算乳腺X线照片感兴趣区域的信息特征。通过这种计算方法获得的特性可以提高诊断的确定性。为了说明该方法的有效性,我们介绍了基于图像MIAS乳房X线照片进行的实验结果。

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