Multiresolution Markov Models for Detecting Radial Patterns of Spicules in Mammograms

2008年，英国约有12000名女性(全球45.8万人)死于乳腺癌。英国国家卫生服务体系的乳房筛查项目已经对1900多万名女性进行了筛查，成功检测出了约11.7万例癌症。世界卫生组织最近的一项国际研究得出结论，每500名接受筛查的女性中就会有一人获救。由于筛查项目的扩大，乳房X光检查的数量越来越多，而且需要努力寻找微小的、偶尔的癌症迹象，这给NHS放射科医生带来了越来越大的压力。因此，计算机辅助检测方法现在变得越来越有吸引力。计算机辅助方法的一个引人注目的特点是，计算机阅读器不会感到疲劳和分心，目前从胶片到数字乳房X光检查的转变使基于计算机的方法比以往任何时候都更加方便。它们最近显示出与放射科医生相当的检测率，尽管作为第二阅读器，假阳性只有轻微增加。现在人们对开发先进的统计图像处理方法非常感兴趣，以提供更好和更早的诊断的基于计算机的系统。一个特殊的悬而未决的问题是针刺的检测；这些是曲线结构的异常放射状图案，可以提供癌症异常的早期指示(即使在癌症肿块不明显的地方)。不幸的是，目前最先进的计算机辅助针刺检测算法不能可靠地区分针刺和各种健康的曲线结构，如间质、乳管和血管。因此，算法要么将健康组织归类为针刺，要么将VISA归类为针刺。这主要是因为以前的尝试过于依赖启发式的“阈值”方法。拟议的研究将结合先进的图像处理和概率方法来检测乳房X光照片中的针刺。我们将使用马尔可夫随机场约束的小波收缩来增强乳房X光片中的曲线结构。多分辨率、对比度容忍曲线度量，如相位一致性和方向正则性将从小波系数中计算出来。边际后验分布将通过马尔可夫链蒙特卡罗方法估计，以推断曲线结构的存在。这将被用来缩小与非曲线结构相关联的小波系数。然后将使用曲线增强图像(再次使用多分辨率马尔可夫随机场模型)来估计方向图。最后，使用由粗到精和概率加权的最小二乘法对方位图进行相图分析，从而计算出针叶概率图。该方法将通过公开的数据集进行验证。放射科医生将帮助评估该软件的性能和可用性。

In 2008 around 12,000 women in the UK (458,000 globally) died from breast cancer (cancerresearchuk.org). The National Health Service's breast screening programme has screened over 19 million women and successfully detected around 117,000 cancers (cancerscreening.nhs.uk) and a recent international study by the World Health Organisation concluded that one life will be saved out of every 500 women screened.The growing quantity of mammograms due to an expanding screening programme, and the effort required to search for subtle, occasional signs of cancer, are adding increasing pressure on NHS radiologists. As such, computer aided detection methods are now becoming increasingly attractive. A compelling feature for computer-aided methods is that the computer reader does not suffer from fatigue and distractions and the present move from film to digital mammography (cancerscreening.nhs.uk) makes computer based methods more convenient than ever. They have recently shown a comparable detection rate to radiologists with only a modest increase in false positives, albeit when acting as a second reader.There is now substantial interest in the development of advanced statistical image processing methods to deliver computer-based systems with improved and earlier diagnoses. A particular open problem is the detection of spicules; these are abnormal radial patterns of curvilinear structures which can offer an early indication of cancerous abnormality (even where a cancerous mass is not evident). Unfortunately, current state-of-the-art computer-aided spicule detection algorithms cannot reliably distinguish between spicules and the variety of healthy curvilinear structures, such as stroma, milk ducts, and blood vessels. As a result, the algorithms either classify healthy tissue as spicules or visa versa. This is mainly due to the fact that previous attempts have relied too heavily on heuristic "thresholding" methods.The proposed research will combine advanced image processing and probabilistic methods to detect spicules in mammograms. We will enhance curvilinear structures in mammograms using Markov random Field constrained wavelet shrinkage. Multiresolution, contrast tolerant curvilinear measures such as phase congruence and directional regularity will be computed from the wavelet coefficients. The marginal posterior distribution will be estimated via Markov chain Monte Carlo methods to infer the presence of curvilinear structures. This will then be used to shrink the wavelet coefficients associated with non-curvilinear structures. An orientation map will then be estimated using the curvilinear enhanced image (again using multiresolution Markov random field models). Finally, coarse-to-fine and probabilistically weighted least squares solvers will be used to perform phase portrait analysis of the orientation map and hence compute a spicule probability map.The methods will be validated by publically available datasets. Radiologists will help assess the performance and usability of the software.