Multilevel learning-based segmentation of ill-defined and spiculated masses in mammograms
Multilevel learning-based segmentation of ill-defined and spiculated masses in mammograms
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DOI：
10.1118/1.3490477
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发表时间：
2010-11-01
期刊：
影响因子：
3.8
通讯作者：
Xuan, Jianhua
中科院分区：
文献类型：
--
作者：
Tao, Yimo;Lo, Shih-Chung B.;Xuan, Jianhua
目的：提出一种基于学习的方法，结合像素级统计建模和毛刺检测，对边缘和毛刺模糊的乳腺肿块进行分割。方法：该算法涉及多阶段像素级分类，使用一组根据区域强度、形状和纹理计算的综合特征，生成肿块条件概率图(PM)。然后，通过结合肿块的形状和位置的先验知识，从PM中提取出肿块候选以及由乳腺纤维腺组织和其他非肿块组织组成的背景杂波。采用多尺度可控脊线检测算法进行毛刺检测。结果：对54个肿块(51个恶性肿块，3个良性肿块)进行了测试，所有肿块边缘不清，形状或毛刺不规则。五位经验丰富的放射科医生提供了地面真实情况的描述。仅分割整个肿块和边缘部分的面积重叠率分别为0.689(+/-0.160)和0.540(+/-0.164)。基于面积和轮廓的Williams指数测量结果表明，该算法的分割结果与放射科医生的勾画结果吻合较好。结论：该方法能较好地勾勒出肿块体块。最重要的是，它能够包括肿块边缘及其毛刺延伸，这被认为是乳房病变分析的关键特征。(C)2010年美国医学物理学家协会。[DOI：10.1118/1.3490477]
Purpose: A learning-based approach integrating the use of pixel-level statistical modeling and spiculation detection is presented for the segmentation of mammographic masses with ill-defined margins and spiculations.Methods: The algorithm involves a multiphase pixel-level classification, using a comprehensive group of features computed from regional intensity, shape, and textures, to generate a mass-conditional probability map (PM). Then, the mass candidate, along with the background clutters consisting of breast fibroglandular and other nonmass tissues, is extracted from the PM by integrating the prior knowledge of shape and location of masses. A multiscale steerable ridge detection algorithm is employed to detect spiculations. Finally, all the object-level findings, including mass candidate, detected spiculations, and clutters, along with the PM, are integrated by graph cuts to generate the final segmentation mask.Results: The method was tested on 54 masses (51 malignant and 3 benign), all with ill-defined margins and irregular shape or spiculations. The ground truth delineations were provided by five experienced radiologists. Area overlapping ratio of 0.689 (+/- 0.160) and 0.540 (+/- 0.164) were obtained for segmenting entire mass and margin portion only, respectively. Williams index of area and contour based measurements indicated that the segmentation results of the algorithm agreed well with the radiologists' delineation.Conclusions: The proposed approach could closely delineate the mass body. Most importantly, it is capable of including mass margin and its spicule extensions which are considered as key features for breast lesion analyses. (C) 2010 American Association of Physicists in Medicine. [DOI: 10.1118/1.3490477]