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Histogram-Based Approach for Mass Segmentation in Mammograms

机译：基于直方图的乳房X线图像质量分割方法

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Segmentation of abnormalities in mammographic images has been a challenge due to poor contrast between the abnormality and surrounding area. In general Computer Aided Diagnostic (CAD) systems are composed of three major step: i.e segmentation followed by feature extraction and classification. The ultimate results of CAD systems are strongly influenced by the results of segmentation, as poor segmentation leads to incorrect features being extracted and ultimately incorrect classification. In this paper we propose a novel method for the segmentation of masses in mammographic images by using a simple method based on the information provided in histograms. After applying some preprocessing on the mammographic images in order to smooth the image histograms, optimal threshold values were obtained for each marninogram which can deal with uni-modal and bi-modal histograms. Segmentation results show an improved area for the mammogram masses, representing the true shapes of the abnormalities. We tested our algorithm on 233 benign and 233 malignant abnormalities. Results are in line with state of the art methods.
机译：由于异常与周围区域之间的对比度差，因此在乳腺X线摄影图像中进行异常分割一直是一个挑战。通常，计算机辅助诊断（CAD）系统由三个主要步骤组成：即分割，然后进行特征提取和分类。 CAD系统的最终结果在很大程度上受到分割结果的影响，因为不良的分割会导致提取错误的特征并最终导致错误的分类。在本文中，我们基于直方图提供的信息，通过一种简单的方法，提出了一种用于乳腺X线摄影图像中肿块分割的新方法。在对乳腺X射线照片进行一些预处理以平滑图像直方图之后，为每个可以处理单峰和双峰直方图的marininogram获得了最佳阈值。分割结果显示出乳房X线照片肿块的面积得到了改善，代表了异常的真实形状。我们在233次良性和233次恶性异常上测试了我们的算法。结果与最新方法一致。

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