Texture based Clustering Technique for Fetal Ultrasound Image Segmentation

Abstract

Segmentation of fetal ultrasound image is an important and necessary task in the automation of fetal biometric measurement. Fetal ultrasound image segmentation is tedious because of the fuzzy nature and textured appearance of fetal structures. Hence, texture based Clustering is proposed for segmenting fetal ultrasound images. Clustering is performed using texture properties of the images which are used for segmenting ultrasound images of fetus. Texture based clustering technique can be used for segmenting all fetal anatomies specifically abdomen, the boundaries of which are very vague and difficult to delineate. Synthetic, simulated ultrasound images and 120 ultrasound fetal images were used for validating the method achieving an accuracy of 90%.

A method framework of automatic localization and quantitative segmentation for the cavum septum pellucidum complex and the cerebellar vermis in fetal brain ultrasound images

“…In this paper, we drew inspiration from various studies related to US image analysis, CC, CCC, and CV. These studies included research on the anatomical relationships between these structures ( 6 , 7 ), as well as studies on fetal brain US images ( 8 ) and other imaging modalities such as magnetic resonance imaging (MRI) ( 9 - 11 ). We also referenced a processing tool developed for fetal brain MRIs by Rousseau et al and applied it to the processing of US images ( 12 ).…”