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Microcalcification evaluation in computer assisted diagnosis for digital mammography

机译:微乳钙化在数字化乳腺X射线摄影计算机辅助诊断中的评估

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摘要

In order to develop applications for z;isual interpretation of medical images, the early detection and evaluation of microcalcifications in digital mammograms is verg important since their presence is oftenassociated with a high incidence of breast cancers. Accurate classification into benign and malignant groups would help improve diagnostic sensitivity as well as reduce the number of unnecessa y biopsies. The challenge here is the selection of the useful features to distinguish benign from malignant micro calcifications. Our purpose in this work is to analyse a microcalcification evaluation method based on a set of shapebased features extracted from the digitised mammography. The segmentation of the microcalcificationsis performed using a fixed-tolerance region growing method to extract boundaries of calcifications with manually selected seed pixels. Taking into account that shapes and sizes of clustered microcalcificationshave been associated with a high risk of carcinoma based on digerent subjective measures, such as whether or not the calcifications are irregular, linear, vermiform, branched, rounded or ring like, our efforts were addressed to obtain a feature set related to the shape. The identification of the pammeters concerning the malignant character of the microcalcifications was performed on a set of 146 mammograms with their real diagnosis known in advance from biopsies. This allowed identifying the following shape-based parameters as the relevant ones: Number of clusters, Number of holes, Area, Feret elongation, Roughness, and Elongation. Further experiments on a set of 70 new mammogmms showed that the performance of the classification scheme is close to the mean performance of three expert radiologists, which allows to consider the proposed method for assisting the diagnosis and encourages to continue the investigation in the senseof adding new features not only related to the shape
机译:为了开发用于医学图像的z常规解释的方法,早期检测和评估数字化X线照片中的微钙化非常重要,因为它们的存在通常与乳腺癌的高发有关。正确分类为良性和恶性组将有助于提高诊断灵敏度,并减少不必要的活检次数。这里的挑战是如何选择有用的特征以区分良性和恶性微钙化。我们在这项工作中的目的是分析一种微钙化评估方法,该方法基于从数字化乳腺X线摄影术中提取的一组基于形状的特征。使用固定公差区域生长方法执行微钙化的分割,以使用人工选择的种子像素提取钙化边界。基于不同的主观测量,例如钙化是否不规则,线性,蠕形,分支,圆形或环状,考虑到簇状微钙化的形状和大小与癌变的高风险有关,我们致力于获得与形状相关的特征集。在一组146幅乳腺X线照片上对与微钙化的恶性特征有关的pammeters进行了鉴定,并从活检中提前得知了它们的真实诊断。这样可以将以下基于形状的参数标识为相关参数:簇数,孔数,面积,Feret伸长率,粗糙度和伸长率。在一组70个新的乳房X线摄影机上进行的进一步实验表明,分类方案的性能接近三位放射线专家的平均性能,这可以考虑所建议的辅助诊断方法,并鼓励继续进行新的意义上的研究。特征不仅与形状有关

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