Medical Image Analysis Lab, Simon Fraser University, Burnaby, BC V5A 1S6, Canada; Photomedicine Institute, Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC V5Z 4E8, Canada.
Medical Image Analysis Lab, Simon Fraser University, Burnaby, BC V5A 1S6, Canada; Photomedicine Institute, Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC V5Z 4E8, Canada; Cancer Control Research, BC Cancer Agency, Vancouver, BC V5Z 4E8, Canada.
Med Image Anal. 2016 Jan;27:84-92. doi: 10.1016/j.media.2015.03.001. Epub 2015 Apr 13.
An automatic pigmented skin lesions tracking system, which is important for early skin cancer detection, is proposed in this work. The input to the system is a pair of skin back images of the same subject captured at different times. The output is the correspondence (matching) between the detected lesions and the identification of newly appearing and disappearing ones. First, a set of anatomical landmarks are detected using a pictorial structure algorithm. The lesions that are located within the polygon defined by the landmarks are identified and their anatomical spatial contexts are encoded by the landmarks. Then, these lesions are matched by labeling an association graph using a tensor-based algorithm. A structured support vector machine is employed to learn all free parameters in the aforementioned steps. An adaptive learning approach (on-the-fly vs offline learning) is applied to set the parameters of the matching objective function using the estimated error of the detected landmarks. The effectiveness of the different steps in our framework is validated on 194 skin back images (97 pairs).
本工作提出了一种自动色素皮肤损伤跟踪系统，该系统对于早期皮肤癌检测非常重要。系统的输入是同一受试者在不同时间拍摄的一对皮肤背部图像。输出是检测到的损伤之间的对应关系（匹配）以及新出现和消失的损伤的识别。首先，使用图像结构算法检测一组解剖学标志点。识别位于标志点定义的多边形内的病变，并通过标志点对其解剖空间上下文进行编码。然后，通过使用基于张量的算法对关联图进行标记来匹配这些病变。使用结构化支持向量机学习上述步骤中的所有自由参数。应用自适应学习方法（在线学习与离线学习），根据检测到的标志点的估计误差来设置匹配目标函数的参数。在 194 张皮肤背部图像（97 对）上验证了我们框架中不同步骤的有效性。
