A variance-reduction method for thyroid nodule boundary detection on ultrasound images

Abstract

To perform computer-aided diagnosis of thyroid nodules on ultrasound images, the nodule's location and its margin should be clearly defined. However, due to the nodule's biological characteristics, echo structure and quality, operator's subjective factors and operating conditions, identification of thyroid nodule boundary becomes quite difficult. In addition, manual identification of nodule boundary heavily relies on physician's subjective judgment. Even the same physician could give different results on the same image at different times. In this study, we proposed a novel and automatic method for thyroid nodule boundary detection based on Variance-Reduction statistics. Based the operator's initial inputs of the nodule's major and minor axes, the region of interest (ROI) is first generated. With grayscale values of pixels in the ROI, we then implement an algorithm to automatically detect the nodule boundary. The proposed method is validated with ultrasound images of 433 thyroid nodules, and the effectiveness of the method is shown by comparing the two boundary error metrics, the Hausdorff distance (HD) and the mean absolute distance (MD), to previously published results.

A Variance-reduction Approach to Detection of the Thyroid-nodule Boundary on Ultrasound Images

“…[5][6][7] Ultrasound imaging is one of the most effective noninvasive screening tools for the detection of thyroid nodules. [6][7][8][9][10][11][12][13][14] Beside conventional B-mode sonography, elastography which reflects tissue stiffness plays a promising role in the characterization of thyroid nodules. 15 Based on impressions of observing the ultrasound images, clinicians make suggestions for patients to be subject to periodic follow-up or further cytological tests.…”

“…8 However, the acquisition and observation of ultrasound images are mostly subjective and highly depend on the experience and judgment of the operator, and the inter-observer variation often results in significantly different decisions. 6,[11][12][13] According to aforementioned reasons, objective quantification of the sonographic thyroid nodule features has become a pressing issue facing the medical staffs. 6 To effectively quantify the sonographic features, an objective accurate nodule boundary has to be first determined.…”

“…Therefore, computer-aided system was proposed to automatically detect and identify the boundary of thyroid nodules on ultrasound images. [9][10][11][12][13][14] Tsantis et al 9 proposed a hybrid multiscale model which integrated the wavelet edge detection procedure and the Hough transform to extract the final contour of thyroid nodules in ultrasound images. Iakovidis et al 10 presented a segmentation framework applied on thyroid ultrasonography named Genetic Algorithm-Variable Background Active Contour model that tuned the parameters automatically.…”

“…For computer-aided systems, speckle is one of the most challenging problems. [9][10][11][12][13][14]17,18 To reduce the impact of speckle, previous publications used image preprocessing methods or altered the detailed information provided during the execution steps. 9,10,11,14 However, speckle also carries information about tissue characteristics and may be exploited in several applications.…”

“…Therefore, computer-aided system was proposed to automatically detect and identify the boundary of thyroid nodules on ultrasound images. 9-14…”