Multiresolution Markov Models for Detecting Radial Patterns of Spicules in Mammograms

2008年，英国约有1.2万名女性（全球为45.8万名）死于乳腺癌（cancerresearchuk.org）。英国国家卫生服务机构的乳房筛查项目已经对1900多万名女性进行了筛查，并成功发现了约11.7万例癌症（cancerscreening.nhs.uk）。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，就有1人的生命得到挽救。由于筛查项目的扩大，乳房x光检查的数量不断增加，而且需要努力寻找细微的、偶然的癌症迹象，这给NHS放射科医生增加了越来越大的压力。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，计算机阅读者不会感到疲劳和分心，而且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方便。他们最近显示出与放射科医生相当的检出率，只是误报率略有增加，尽管是作为第二阅读者。现在有很大的兴趣发展先进的统计图像处理方法，以提供基于计算机的系统与改进和早期诊断。一个特殊的开放问题是针状体的检测；这些是曲线结构的异常径向模式，可以提供癌症异常的早期指示（即使癌变肿块不明显）。不幸的是，目前最先进的计算机辅助针状体检测算法不能可靠地区分针状体和各种健康的曲线结构，如基质、乳管和血管。因此，算法要么将健康组织分类为针状组织，要么将其分类为针状组织。这主要是由于以前的尝试过于依赖启发式的“阈值”方法。提出的研究将结合先进的图像处理和概率方法来检测乳房x光片中的针状体。我们将使用马尔可夫随机场约束的小波收缩增强乳房x光片中的曲线结构。从小波系数中计算出相位同余和方向规则等多分辨率、容差曲线度量。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。然后，这将用于缩小与非曲线结构相关的小波系数。然后使用曲线增强图像估计方向图（再次使用多分辨率马尔可夫随机场模型）。最后，将使用粗到精和概率加权最小二乘求解器对取向图进行相位肖像分析，从而计算出针状体概率图。这些方法将通过公开可用的数据集进行验证。放射科医生将帮助评估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.