Department of Radiology, State University of New York at Stony Brook, Stony Brook, NY, USA.
Department of Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY, USA.
Sci Rep. 2021 Feb 10;11(1):3485. doi: 10.1038/s41598-021-83095-2.
Malignant lesions have a high tendency to invade their surrounding environment compared to benign ones. This paper proposes a dynamic lesion model and explores the 2nd order derivatives at each image voxel, which reflect the rate of change of image intensity, as a quantitative measure of the tendency. The 2nd order derivatives at each image voxel are usually represented by the Hessian matrix, but it is difficult to quantify a matrix field (or image) through the lesion space as a measure of the tendency. We conjecture that the three eigenvalues contain important information of the Hessian matrix and are chosen as the surrogate representation of the Hessian matrix. By treating the three eigenvalues as a vector, called Hessian vector, which is defined in a local coordinate formed by three orthogonal Hessian eigenvectors and further adapting the gray level occurrence computing method to extract the vector texture descriptors (or measures) from the Hessian vector, a quantitative presentation for the dynamic lesion model is completed. The vector texture descriptors were applied to differentiate malignant from benign lesions from two pathologically proven datasets: colon polyps and lung nodules. The classification results not only outperform four state-of-the-art methods but also three radiologist experts.
与良性病变相比，恶性病变具有更高的侵袭周围环境的倾向。本文提出了一种动态病变模型，并探讨了每个图像体素的二阶导数，它反映了图像强度的变化率，作为病变倾向的定量测量。每个图像体素的二阶导数通常由 Hessian 矩阵表示，但很难通过病变空间量化矩阵场（或图像）作为病变倾向的度量。我们推测 Hessian 矩阵的三个特征值包含重要信息，并选择作为 Hessian 矩阵的替代表示。通过将三个特征值视为一个向量，称为 Hessian 向量，它是在由三个正交 Hessian 特征向量形成的局部坐标系中定义的，并进一步适应灰度级出现计算方法从 Hessian 向量中提取向量纹理描述符（或度量），从而完成对动态病变模型的定量表示。将向量纹理描述符应用于从两个经病理证实的数据集（结肠息肉和肺结节）中区分恶性和良性病变。分类结果不仅优于四种最先进的方法，也优于三位放射科医生专家。
