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

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@article{IJASEIT4455,
   author = {Sadiah Jantan and Anuar Mikdad Muad and Aini Hussain},
   title = {Segmentation of Carpal Bones Using Gradient Inverse Coefficient  of Variation with Dynamic Programming Method},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {9},
   number = {1},
   year = {2019},
   pages = {73--80},
   keywords = {carpal bone; segmentation; active contour; gradient inverse coefficient of variation; dynamic programming.},
   abstract = {

Segmentation of the carpal bones (CBs) especially for children above seven years old is a challenging task in computer vision mainly because of poor definitions of the bone contours and the occurrence of the partial overlapping of the bones. Although active contour methods are widely employed in image bone segmentation, they are sensitive to initialization and have limitation in segmenting overlapping objects.  Thus, there is a need for a robust segmentation method for bone segmentation. 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 of children age 5 to 8 years old. 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 is compared qualitatively and quantitatively with the de-regularized level set (DRLS) and multi-scale gradient vector flow (MGVF) on a dataset of 20 images of carpal bones from University of Southern California. The presented method is capable to detect the bone boundaries fast and accurate. Results show that the DP-GICOV is highly accurate especially for overlapping bones, which is more than 85% in many cases, and it requires minimal user’s intervention. This method has produced a promised result in overcoming both issues faced by active contours method; initialization and overlapping objects.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4455},    doi = {10.18517/ijaseit.9.1.4455} }

EndNote

%A Jantan, Sadiah
%A Muad, Anuar Mikdad
%A Hussain, Aini
%D 2019
%T Segmentation of Carpal Bones Using Gradient Inverse Coefficient  of Variation with Dynamic Programming Method
%B 2019
%9 carpal bone; segmentation; active contour; gradient inverse coefficient of variation; dynamic programming.
%! Segmentation of Carpal Bones Using Gradient Inverse Coefficient  of Variation with Dynamic Programming Method
%K carpal bone; segmentation; active contour; gradient inverse coefficient of variation; dynamic programming.
%X 

Segmentation of the carpal bones (CBs) especially for children above seven years old is a challenging task in computer vision mainly because of poor definitions of the bone contours and the occurrence of the partial overlapping of the bones. Although active contour methods are widely employed in image bone segmentation, they are sensitive to initialization and have limitation in segmenting overlapping objects.  Thus, there is a need for a robust segmentation method for bone segmentation. 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 of children age 5 to 8 years old. 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 is compared qualitatively and quantitatively with the de-regularized level set (DRLS) and multi-scale gradient vector flow (MGVF) on a dataset of 20 images of carpal bones from University of Southern California. The presented method is capable to detect the bone boundaries fast and accurate. Results show that the DP-GICOV is highly accurate especially for overlapping bones, which is more than 85% in many cases, and it requires minimal user’s intervention. This method has produced a promised result in overcoming both issues faced by active contours method; initialization and overlapping objects.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4455 %R doi:10.18517/ijaseit.9.1.4455 %J International Journal on Advanced Science, Engineering and Information Technology %V 9 %N 1 %@ 2088-5334

IEEE

Sadiah Jantan,Anuar Mikdad Muad and Aini Hussain,"Segmentation of Carpal Bones Using Gradient Inverse Coefficient  of Variation with Dynamic Programming Method," International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 1, pp. 73-80, 2019. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.9.1.4455.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Jantan, Sadiah
AU  - Muad, Anuar Mikdad
AU  - Hussain, Aini
PY  - 2019
TI  - Segmentation of Carpal Bones Using Gradient Inverse Coefficient  of Variation with Dynamic Programming Method
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 9 (2019) No. 1
Y2  - 2019
SP  - 73
EP  - 80
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - carpal bone; segmentation; active contour; gradient inverse coefficient of variation; dynamic programming.
N2  - 

Segmentation of the carpal bones (CBs) especially for children above seven years old is a challenging task in computer vision mainly because of poor definitions of the bone contours and the occurrence of the partial overlapping of the bones. Although active contour methods are widely employed in image bone segmentation, they are sensitive to initialization and have limitation in segmenting overlapping objects.  Thus, there is a need for a robust segmentation method for bone segmentation. 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 of children age 5 to 8 years old. 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 is compared qualitatively and quantitatively with the de-regularized level set (DRLS) and multi-scale gradient vector flow (MGVF) on a dataset of 20 images of carpal bones from University of Southern California. The presented method is capable to detect the bone boundaries fast and accurate. Results show that the DP-GICOV is highly accurate especially for overlapping bones, which is more than 85% in many cases, and it requires minimal user’s intervention. This method has produced a promised result in overcoming both issues faced by active contours method; initialization and overlapping objects.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4455 DO - 10.18517/ijaseit.9.1.4455

RefWorks

RT Journal Article
ID 4455
A1 Jantan, Sadiah
A1 Muad, Anuar Mikdad
A1 Hussain, Aini
T1 Segmentation of Carpal Bones Using Gradient Inverse Coefficient  of Variation with Dynamic Programming Method
JF International Journal on Advanced Science, Engineering and Information Technology
VO 9
IS 1
YR 2019
SP 73
OP 80
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 carpal bone; segmentation; active contour; gradient inverse coefficient of variation; dynamic programming.
AB 

Segmentation of the carpal bones (CBs) especially for children above seven years old is a challenging task in computer vision mainly because of poor definitions of the bone contours and the occurrence of the partial overlapping of the bones. Although active contour methods are widely employed in image bone segmentation, they are sensitive to initialization and have limitation in segmenting overlapping objects.  Thus, there is a need for a robust segmentation method for bone segmentation. 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 of children age 5 to 8 years old. 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 is compared qualitatively and quantitatively with the de-regularized level set (DRLS) and multi-scale gradient vector flow (MGVF) on a dataset of 20 images of carpal bones from University of Southern California. The presented method is capable to detect the bone boundaries fast and accurate. Results show that the DP-GICOV is highly accurate especially for overlapping bones, which is more than 85% in many cases, and it requires minimal user’s intervention. This method has produced a promised result in overcoming both issues faced by active contours method; initialization and overlapping objects.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=4455 DO - 10.18517/ijaseit.9.1.4455