Modelling mammographic microcalcification clusters using persistent mereotopology
Modelling mammographic microcalcification clusters using persistent mereotopology
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DOI：
10.1016/j.patrec.2014.04.008
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发表时间：
2014-10-01
影响因子：
5.1
通讯作者：
Zwiggelaar, Reyer
中科院分区：
文献类型：
--
作者：
Strange, Harry;Chen, Zhili;Zwiggelaar, Reyer
在乳房X线摄影成像中，微钙化的存在，乳房中的小钙沉积，是乳腺癌的主要指标。然而，并不是所有的微钙化都是恶性的，它们在乳房内的分布可以用来指示微钙化簇是良性的还是恶性的。计算机辅助诊断（CAD）系统可以用来帮助分类这样的微钙化簇。在本文中，提出了一种新的方法，用于分类微钙化簇表示离散mereotopological关系的个体微钙化在一个范围内的尺度的形式mereotopological条形码。这种基于条形码的表示能够对多个区域之间的复杂关系进行建模，并且对乳腺X线摄影微钙化数据的结果显示了这种方法的有效性。在MIAS和DDSM数据集上分别实现了95%和80%的分类准确率。这些结果与现有的最先进的方法相当。这项工作还表明，通过提供对代表良性和恶性病例的条形码的临床解释，可以使用mereotopological条形码来帮助训练有素的临床医生进行诊断。(C)2014作者由爱思唯尔公司出版
In mammographic imaging, the presence of microcalcifications, small deposits of calcium in the breast, is a primary indicator of breast cancer. However, not all microcalcifications are malignant and their distribution within the breast can be used to indicate whether clusters of microcalcifications are benign or malignant. Computer-aided diagnosis (CAD) systems can be employed to help classify such microcalcification clusters. In this paper a novel method for classifying microcalcification clusters is presented by representing discrete mereotopological relations between the individual microcalcifications over a range of scales in the form of a mereotopological barcode. This barcode based representation is able to model complex relations between multiple regions and the results on mammographic microcalcification data shows the effectiveness of this approach. Classification accuracies of 95% and 80% are achieved on the MIAS and DDSM datasets, respectively. These results are comparable to existing state-of-the art methods. This work also demonstrates that mereotopological barcodes could be used to help trained clinicians in their diagnosis by providing a clinical interpretation of barcodes that represent both benign and malignant cases. (C) 2014 The Authors. Published by Elsevier B.V.