TY - GEN
T1 - Automatic segmentation of liver tumor on computed tomography images
AU - Arakeri, M. P.
AU - Lakshmana,
PY - 2010
Y1 - 2010
N2 - Segmentation of liver tumor on Computed Tomography (CT) images is a challenging task due to anatomic complexity and the imaging system noise. The conventional region growing method has a widespread use in medical image segmentation because of its robustness to noise. However, region growing algorithm is semi-automatic in which the initial seed point and threshold value have to be manually identified. To avoid these problems, in this paper we propose a automatic region growing method that incorporates fuzzy c-means clustering algorithm to find the threshold value and modified region growing algorithm to find seed point automatically. In this paper, we also describe a framework to create a three dimensional (3D) model of the liver which can be used by the surgeons for tumor volume measurement, liver transplant and surgical planning. The proposed method has been tested on several CT images of liver. The results show that the algorithm successfully detects the edges of the liver tumor distinguishing it from the background without manual intervention.
AB - Segmentation of liver tumor on Computed Tomography (CT) images is a challenging task due to anatomic complexity and the imaging system noise. The conventional region growing method has a widespread use in medical image segmentation because of its robustness to noise. However, region growing algorithm is semi-automatic in which the initial seed point and threshold value have to be manually identified. To avoid these problems, in this paper we propose a automatic region growing method that incorporates fuzzy c-means clustering algorithm to find the threshold value and modified region growing algorithm to find seed point automatically. In this paper, we also describe a framework to create a three dimensional (3D) model of the liver which can be used by the surgeons for tumor volume measurement, liver transplant and surgical planning. The proposed method has been tested on several CT images of liver. The results show that the algorithm successfully detects the edges of the liver tumor distinguishing it from the background without manual intervention.
UR - https://www.scopus.com/pages/publications/77952340475
UR - https://www.scopus.com/pages/publications/77952340475#tab=citedBy
U2 - 10.1145/1741906.1741935
DO - 10.1145/1741906.1741935
M3 - Conference contribution
AN - SCOPUS:77952340475
SN - 9781605588124
T3 - ICWET 2010 - International Conference and Workshop on Emerging Trends in Technology 2010, Conference Proceedings
SP - 153
EP - 155
BT - ICWET 2010 - International Conference and Workshop on Emerging Trends in Technology 2010, Conference Proceedings
T2 - International Conference and Workshop on Emerging Trends in Technology 2010, ICWET 2010
Y2 - 26 February 2010 through 27 February 2010
ER -