Active contour-based segmentation of normal and fetal spina bifida ultrasound images

Abstract

Fetal spina bifida is a neurological disorder which occurs due to improper closure of the spinal column. Fetus identified with spina bifida suffers from various paralytic disorders throughout their lifespan. Early diagnosis of spina bifida aids in timely medical interventions. The ultrasound imaging is widely preferred for fetal monitoring. This study involves segmentation of the normal and abnormal fetal spine from ultrasound images using active contour algorithm. The images for analysis are collected from a diagnostic centre. The noise present in the images is removed using Wiener filter and anisotropic diffusion (AD) filter. The denoised images are evaluated with the metrics such as signal to noise ratio (SNR), peak signal to noise ratio (PSNR), structural similarity index measure (SSIM) and mean square error (MSE). The contrast enhancement is performed by histogram equalization (HE) and adaptive histogram equalization (AHE) techniques. The contrast enhanced images are validated by measures namely entropy and adaptive mean brightness error (AMBE). From the pre-processed image, the spine region is segmented using the active contour method. The results demonstrate that the AD filter with optimal parameters performs better than the Wiener filter for denoising. For the contrast enhancement, the AHE technique shows better performance compared to HE. The active contour technique is able to segment the spine regions in both the normal and spina bifida images. As early diagnosis of spina bifida is essential, this approach could be clinically significant.