Machine Leaning Assessment of Border Irregularity of Thyroid Nodules from Ultrasound Images

The border irregularity of lesions or tumors is an important sign (or feature) contributing to the prediction of the tumor malignancy. This paper is concerned with developing automatic computer vision methods for assessing and recognizing thyroid nodule border irregularity from ultrasound images. Unlike many existing schemes, our methods rely on a small set of points on the nodule border marked manually by clinicians. To mitigate the absence of a fully segmented lesion boundary, we first apply the cubic-spline interpolation of the region of interest (ROI) points to approximate the lesion border and then select equal numbers of points from the approximated border using equal angular distances. We developed two complementary approaches to investigate the global (big indentations and protrusions) and local (small zigzag) irregularity features of the nodule. The first approach includes two Euclidian distances-based methods and a method inspired by Fractal Dimensions (FD). The distances-based methods facilitate the use of the interpolated border and their radial distance functions measured from ROI points to a reference point (centroid) or reference shape (Convex hull), while the FD inspired method uses interpolated border and a fitted ellipse perimeter ratio to calculate an irregularity index. The second approach facilitates the texture analysis within the constructed ribbons around the border line of different widths using feature vector of uniform local binary pattern (ULBP). We evaluate and compare the performance of our methods from the two approaches by using two datasets consisting of 395 and 100 ultrasound images of thyroid nodules collected from two hospitals and labelled by experienced radiologists respectively. The first is used as training and internal testing set, while the second is used for external testing. We shall show the viability of our methods attaining accuracy rates between 70% and 90%.