International Journal on Advanced Science, Engineering and Information Technology, Vol. 9 (2019) No. 1, List of accepted papers. , DOI:10.18517/ijaseit.9.1.4455

Segmentation of Carpal Bones Using Gradient Inverse Coefficient of Variation with Dynamic Programming Method

Sa'diah Jantan, Anuar Mikdad Muad, Aini Hussain

Abstract

Segmentation of the carpal bones (CBs) especially for children above seven years old is a challenging task in computer vision mainly because the occurrence of the partial overlapping of the bones. Although active contour methods are widely employed in image segmentation, they are sensitive to initialization and have limitation in segmenting overlapping objects.  This paper presents an automatic active boundary-based segmentation method, gradient inverse coefficient of variation, based on dynamic programming (DP-GICOV) method to segment carpal bones on radiographic images. A mapping procedure is designed based on a priori knowledge about the natural growth and the arrangement of carpal bones in human body. The accuracy of the DP-GICOV was compared qualitatively and quantitatively with the de-regularized level set (DRLS) and multi-scale gradient vector flow (MGVF) on a dataset of 20 images. Results show that the DP-GICOV is highly accurate especially for overlapping bones and it requires minimal user’s intervention.

Keywords:

Carpal bone, segmentation, active contour, gradient inverse coefficient of variation, dynamic programming

Viewed: 53 times (since Sept 4, 2017)

cite this paper