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SCM-motivated enhanced CV model for mass segmentation from coarse-to-fine in digital mammography

机译：基于SCM的增强型CV模型，用于数字乳腺摄影中从粗到细的质量分割

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摘要

A novel approach for mass segmentation from coarse-to-fine in digital mammography, termed as SCM-motivated enhanced CV algorithm, is presented in this paper. As well known, it is difficult to robustly achieve mammogram mass segmentation due to low contrast between normal and lesion tissues, as well as high density tissue interference in mammograms. Therefore, Spiking Cortical Model with biology background is introduced to achieve mammary-specific and mass edge detection, and this mass candidate is regarded as the initial contour of improved CV model followed by, effectively overcoming the drawback that CV method is sensitive to the initial contour; especially, the enhanced CV model innovatively combines the techniques of physical imaging principle, and local region-scalable force, harvesting the coarse-to-fine mass boundary accurately. The proposed method is tested totally on 400 mammograms from two well-known digitized datasets (digital database for screening mammography and mammography image analysis society database), achieving the average detection rate of 93.25%. By comparing with the region-based model with bias field (Method 1) and typical CV model (Method 2), we can reach the conclusion that proposed method is outperform other methods, yielding the average sensitivity of 95.83%, specificity of 99.13%, dice similarity co-efficient of 92.21% and AUC of 98.02%. In addition, this method is verified on the mammograms from Gansu Provincial Cancer Hospital, the detection results reveal that our method can accurately detect the abnormal in clinical application.
机译：本文提出了一种在数字乳腺X射线摄影中从粗到细的质量分割的新方法，称为SCM驱动的增强CV算法。众所周知，由于正常和病变组织之间的对比度低以及乳房X线照片中的高密度组织干扰，很难稳固地实现乳房X线照片的质量分割。因此，引入具有生物学背景的尖刺皮层模型以实现乳腺特异性和肿块边缘检测，并将该肿块候选物视为改进的CV模型的初始轮廓，然后有效克服了CV方法对初始轮廓敏感的缺点。 ;特别是，改进的CV模型创新地结合了物理成像原理技术和局部区域可伸缩力，从而准确地捕获了从粗到细的质量边界。该方法在两个著名的数字化数据集（用于筛查乳腺X射线摄影的数字数据库和乳腺X射线摄影图像分析协会数据库）的400幅乳腺X射线照片上进行了全面测试，平均检出率为93.25％。通过与带有偏场的基于区域的模型（方法1）和典型的CV模型（方法2）进行比较，我们可以得出结论，该方法优于其他方法，平均灵敏度为95.83％，特异性为99.13％，骰子相似系数为92.21％，AUC为98.02％。此外，该方法在甘肃省肿瘤医院的乳房X线照片上得到了验证，检测结果表明，该方法可以在临床应用中准确检测出异常。

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