Automatic Recognition of Parathyroid Nodules in Ultrasound Images Based on Fused Prior Pathological Knowledge Features

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.

Keywords

• Parathyroid nodules
• ultrasound images
• hybrid level set
• prior pathological knowledge
• image local entropy
• SVDD