Infantile hemangiomas (IHs) are a type of vascular tumors that affect around 10% of newborns. The measurement of the lesion size and the assessment of the evolution is done manually by the physician. This paper presents an algorithm for the automatic computation of the IH lesion surface. The image scale is computed by using the Hough transform and the total variation. As pre-processing, a geometric correction step is included, which ensures that the lesions are viewed as perpendicular to the camera. The image segmentation is based on K-means clustering applied on a five-plane image; the five planes being selected from seven planes with the use of the Karhunen-Loeve transform. Two of the seven planes are 2D total variation filters, based on symmetrical kernels, designed to highlight the IH specific texture. The segmentation performance was assessed on 30 images, and a mean border error of 9.31% was obtained.

中文翻译：

---

婴儿血管瘤的自动分割和测量

婴儿血管瘤（IHs）是一种血管肿瘤，可影响约10％的新生儿。病变大小的测量和演变的评估是由医生手动完成的。本文提出了一种自动计算IH病变表面的算法。通过使用霍夫变换和总变化量来计算图像比例。作为预处理，包括几何校正步骤，该步骤可确保将病变视为垂直于相机。图像分割基于应用于五个平面图像的K均值聚类；使用Karhunen-Loeve变换从七个平面中选择五个平面。七个平面中的两个是基于对称内核的2D总变化滤波器，旨在突出显示IH特定纹理。