Abstract
Automation diagnosis of parathyroid nodules is of crucial importance to recognize parathyroid nodules in ultrasound images. Aiming at the different nodule shapes of diverse patients, blurred boundaries, complex backgrounds and inhomogeneous intensity of ultrasound images, we propose a novel hybrid level set model to accurately segment nodules. The adaptive global term weight is determined based on the image local entropy of the region around the evolution contour and two scales are proposed for the local term to drive the evolution contour fast approaching to the boundary in order to avoid large amount of calculation and over-segmentation. We also propose membrane features and relative position features based on prior pathological knowledge to describe the inherent characteristics of parathyroid nodules different from thyroid and other nodules. We fused prior pathological knowledge features, morphology features and texture features of the segmented nodules to recognize parathyroid nodules by the support vector data description(SVDD). The experiment result indicates that the incorporation of the proposed hybrid level set segmentation method and the fused prior pathological knowledge features, morphology features and texture features improve the recognition accuracy and efficiency of parathyroid nodules, which is much higher than that only with morphology and texture features.
Highlights
Metabolism dysregulation of calcium and phosphorus is a common complication for chronic kidney disease (CKD) patients, which leads to secondary hyperparathyroidism (SHPT) [1] and seriously affects the life quality of patients
To improve the segmentation efficiency and accuracy, we proposed the hybrid level set model to segment nodules in ultrasound images
In order to improve the recognition accuracy, we proposed prior pathological knowledge features of membrane features and relative position features to describe the inherent characteristic of parathyroid nodules different from other nodules, which are robust to noises and complicated backgrounds
Summary
Metabolism dysregulation of calcium and phosphorus is a common complication for chronic kidney disease (CKD) patients, which leads to secondary hyperparathyroidism (SHPT) [1] and seriously affects the life quality of patients. Aiming to the blurry boundaries between tissues, inhomogeneous intensity of parathyroid nodules and heavy noises in ultrasound images, this study proposes a hybrid level set model to improve the segmentation accuracy and adaptability to diversiform parathyroid nodules by fully using global and local intensity information in images. Aiming to the highly inhomogeneous intensity, heavy noises and diverse parathyroids in ultrasound images, an adaptive hybrid level set model based on the image local. In this paper we proposed a hybrid level set model aiming to accurately segment nodules with complex backgrounds and inhomogeneous intensity in ultrasound images by adjusting the model weight adaptively. When the change of evolution curve is smaller than the threshold, the smaller scale is used and makes the curve accurately stop at the real boundary
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
