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Removal of pectoral muscle based on topographic map and shape-shifting silhouette

机译:基于地形图和形状移位剪影的胸肌去除

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In digital mammography, finding accurate breast profile segmentation of women's mammogram is considered a challenging task. The existence of the pectoral muscle may mislead the diagnosis of cancer due to its high-level similarity to breast body. In addition, some other challenges due to manifestation of the breast body pectoral muscle in the mammogram data include inaccurate estimation of the density level and assessment of the cancer cell. The discrete differentiation operator has been proven to eliminate the pectoral muscle before the analysis processing. We propose a novel approach to remove the pectoral muscle in terms of the mediolateral-oblique observation of a mammogram using a discrete differentiation operator. This is used to detect the edges boundaries and to approximate the gradient value of the intensity function. Further refinement is achieved using a convex hull technique. This method is implemented on dataset provided by MIAS and 20 contrast enhanced digital mammographic images. To assess the performance of the proposed method, visual inspections by radiologist as well as calculation based on well-known metrics are observed. For calculation of performance metrics, the given pixels in pectoral muscle region of the input scans are calculated as ground truth. Our approach tolerates an extensive variety of the pectoral muscle geometries with minimum risk of bias in breast profile than existing techniques.
机译:在数字乳房X光检查中,寻找女性乳房X线照片的准确乳房曲线分割被认为是一个具有挑战性的任务。由于其与胸体的高水平相似性,胸肌的存在可能会误导癌症的诊断。此外,乳房X线照片数据中乳腺体胸肌的表现引起的其他一些挑战包括不准确的密度水平估计和癌细胞的评估。离散分化算子已被证明是在分析处理之前消除胸肌。我们提出了一种新颖的方法,即使用离散分化算子于Mediolate-Oblique观察乳房X线图的乳房观察。这用于检测边缘边界并近似强度函数的梯度值。使用凸壳技术实现了进一步的改进。该方法在MIS和20对比度增强的数字乳房X线图提供的数据集上实现。为了评估所提出的方法的性能,观察到放射科学家的目视检查以及基于众所周知的度量计算。为了计算性能度量,输入扫描的胸肌区域中的给定像素被计算为地面真理。我们的方法可容忍各种胸肌几何形状,其乳房剖面中的最小风险与现有技术相比。

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