Improved Kidney Stone Detection from Ultrasound Images Using GVF Active Contour

Abstract

Medical image segmentation is of large significance in supporting information about human body structures which assist physicians in correct diagnosis to determine doing radiotherapy or surgeries. Therefore, accurate interest region detection in ultrasound images represents a challenging function and hence needs to apply more trusted tools to gain the best segmentation and classification of kidney stones. This challenge in ultrasound images includes many factors like low contrast, occlusions, signal deviations, and noise made it difficult to determine these stone’s boundaries. This paper applied gradient vector flow (GVF) model which has a large capture range to identify the image boundaries of kidney stones region and estimate variation in stone measurement to prepare a suitable treatment diagnosis.