Knowledge-Based System Guided Automatic Contour Segmentation of Abdominal Structures in CT Scans
The advice of automating computer applications is being increase to reduce the human interaction. Medical image segmentation is one of these applications, when done manually; it turns into a time-consuming and knowledge intensive task. As a result, automatic segmentation is in the focus of work to speed up segmentation processes. Fast and accurate segmentation would allow physicians to analyze and visualize the human structures and re-plan radiation therapy and surgery. This paper introduces a knowledge system based on different sources of medical knowledge to automate medical image segmentation through active contour methods. The way of getting benefit of the knowledge provided by medical atlas, expert’s rules, image features, image multiple views and image Meta data introduced by this knowledge system. We classify the system in different domains in way can be manage properly to guide active contour segmentation methods for abdominal CT scans. The obtained results are very promising showing significant improvements over other methods where the volume measurements error is 7% and the processing time was improved by 68%.
Mahmoud Saleh Jawarneh,
Mohammed Said Abual-Rub,
Knowledge-Based System Guided Automatic Contour Segmentation of Abdominal Structures in CT Scans, International Journal of Intelligent Information Systems.
Vol. 5, No. 1,
2016, pp. 5-16.
Abdel-massieh, N. H., M. M. Hadhoud, et al. (2010). A fully automatic and efficient technique for liver segmentation from abdominal CT images. Informatics and Systems (INFOS), 2010 The 7th International Conference on.
%Campadelli, P., E. Casiraghi, et al. (2009). "Liver segmentation from computed tomography scans: A survey and a new algorithm." Artificial Intelligence in Medicine 45(2-3): 185-196.
%Casiraghi, E., P. Campadelli, et al. (2009). "Automatic Abdominal Organ Segmentation from CT images." Electronic Letters on Computer Vision and Image Analysis (ELCVIA) 8.
%Chan, T. F. and L. A. Vese (2001). "Active contours without edges." Image Processing, IEEE Transactions on 10(2): 266-277.
%ChangYang, L., W. Xiuying, et al. (2010). Fully automated liver segmentation for low- and high- contrast CT volumes based on probabilistic atlases. 17th IEEE International Conference on Image Processing (ICIP).
%Chien-Cheng, L. and C. Pau-Choo (2000). Recognizing abdominal organs in CT images using contextual neural network and fuzzy rules. Engineering in Medicine and Biology Society, 2000. Proceedings of the 22nd Annual International Conference of the IEEE.
%Chunming, L., X. Chenyang, et al. (2005). Level set evolution without re-initialization: a new variational formulation. Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on.
%Cui, W., Y. Wang, et al. (2013). "Localized FCM Clustering with Spatial Information for Medical Image Segmentation and Bias Field Estimation." International Journal of Biomedical Imaging 2013: 8.
%Ding, F., W. K. Leow, et al. (2005). Segmentation of 3D CT Volume Images Using a Single 2D Atlas. Computer Vision for Biomedical Image Applications, Springer Berlin / Heidelberg. 3765: 459-468.
%Foo, J. L. (2006). A Survey of User Interaction and Automation in Medical Image Segmentation Methods, Iowa State University.
%Furukawa, D., A. Shimizu, et al. (2007). Automatic liver segmentation method based on maximum a posterior probability estimation and level set method. 3D Segmentation In The Clinic: A Grand Challenge. T. H. a. M. S. a. B. v. Ginneken.
%Ginneken, B., T. Heimann, et al. (2007). 3D segmentation in the clinic: a grand challenge. 3D segmentation in the clinic: a grand challenge. B. Ginneken, T. Heimann and M. Stiner: 7–15.
%Ginneken, B. V., T. Heimann, et al. (2007). M.: 3D segmentation in the clinic: A grand challenge. 3D segmentation in the clinic: A grand Challenge2007.
%Hancock, E. R. and J. Kittler (1990). "Edge-labeling using dictionary-based relaxation." Pattern Analysis and Machine Intelligence, IEEE Transactions on 12(2): 165-181.
%Haralick, R. (1973). Textural measures for images classification.
%Harms, J., M. Bartels, et al. (2005). "Computerized CT-Based 3D Visualization Technique in Living Related Liver Transplantation." Transplantation Proceedings 37(2): 1059-1062.
%Ibrahim, H., M. Petrou, et al. (2010). "Automatic Volumetric Liver Segmentation from MRI Data." International Journal of Computer Theory and Engineering (IJCTE) 2: 1793-8201.
%Jia, L. and J. Z. Wang (2008). "Real-Time Computerized Annotation of Pictures." Pattern Analysis and Machine Intelligence, IEEE Transactions on 30(6): 985-1002.
%Jiang, H., H. Tan, et al. (2013). "A Priori Knowledge and Probability Density Based Segmentation Method for Medical CT Image Sequences." BioMed Research International 2014: 11.
%Kallman, H.-E., E. Halsius, et al. (2008). "DICOM Metadata repository for technical information in digital medical images." Acta Oncologica 99999(1): 1-4.
%Kroon, D. J., E. v. Oort, et al. (2008). "Multiple Sclerosis Detection in Multispectral Magnetic Resonance Images with Principal Components Analysis." The MIDAS Journal.
%Lankton, S. and A. Tannenbaum (2008). "Localizing Region-Based Active Contours." Image Processing, IEEE Transactions on 17(11): 2029-2039.
%Lee, J., N. Kim, et al. (2007). Efficient Liver Segmentation exploiting Level-Set Speed Images with 2.5D Shape Propagation. 3D segmentation in the clinic: A grand Challenge2007: 189-196.
%Li, S., T. Fevens, et al. (2006). "Automatic clinical image segmentation using pathological modeling, PCA and SVM." Engineering Applications of Artificial Intelligence 19(4): 403-410.
%Linguraru, M. G., J. K. Sandberg, et al. (2010). Automated segmentation and quantification of liver and spleen from CT images using normalized probabilistic atlases and enhancement estimation. 37: 771-83.
%Liu, Y., T. Jiang, et al. (2005). Segmentation of 3D CT Volume Images Using a Single 2D Atlas. Computer Vision for Biomedical Image Applications, Springer Berlin / Heidelberg. 3765: 459-468.
%Lowe, D. G. (2004). "Distinctive Image Features from Scale-Invariant Keypoints." International Journal of Computer Vision 60(2): 91-110.
%Luo, S., X. Li, et al. (2014). "Review on the Methods of Automatic Liver Segmentation from Abdominal Images." Journal of Computer and Communications 2: 1-7.
%Martí, J., J. Benedí, et al. (2007). Automatic Segmentation of the Liver in CT Using Level Sets Without Edges. Pattern Recognition and Image Analysis, Springer Berlin / Heidelberg. 4477: 161-168.
%Mei, J., Y. Si, et al. (2013). "A novel active contour model for unsupervised low-key image segmentation." Central European Journal of Engineering 3(2): 267-275.
%MICCAI. (2007). "Workshop on 3D Segmentation in the Clinic - A Grand Challenge - Segmentation of the Liver ", from http://mbi.dkfz-heidelberg.de/grand-challenge2007/.
%Möller, A. M. (2009). Challenges in Multimodality Imaging using Positron Emission Tomography, Technische Universität München.
%Nakayama, Y., Q. Li, et al. (2006). "Automated Hepatic Volumetry for Living Related Liver Transplantation At Multisection CT1." Radiology 240(3): 743-748.
%Narkhede, H. P. (2013). "Review of Image Segmentation Techniques." International Journal of Innovative Science and Modern Engineering 1(8): 54-61.
%O’Donnell, L. (2001). Semi-Automatic Medical Image Segmentation. Electrical Engineering and Computer Science, MASSACHUSETTS INSTITUTE OF TECHNOLOGY. Master.
%Pan, S. and B. Dawant (2001). Automatic 3D segmentation of the liver from abdominal CT images: a level-set approach. Medical Imaging 2001: Image Processing, SPIE.
%RadiologyAnatomyAtlas. (2010). "http://web.mac.com/rlivingston/Site/Radiology_Anatomy_Atlas.html." from http://web.mac.com/rlivingston/Site/Radiology_Anatomy_Atlas.html.
%Shin, H. S., B. H. Chung, et al. (2009). "Measurement of Kidney Volume with Multi-Detector Computed Tomography Scanning in Young Korean." Yonsei Med J 50(2): 262-265.
%SIG. (2010). "http://sig.biostr.washington.edu/projects/AnnoteImage/." from http://sig.biostr.washington.edu/projects/AnnoteImage/.
%Slagmolen, P., A. Elen, et al. (2007). Atlas based liver segmentation using nonrigid registration with a b-spline transformation model. 3D Segmentation In The Clinic: A Grand Challenge. T. Heimann, M. Styner and B. v. Ginneken.
%Straka, M., A. L. Cruz, et al. (2004). Bone Segmentation in CT-Angiography Data Using a Probabilistic Atlas, Institute of Computer Graphics and Algorithms, Vienna University of Technology: 505--512.
%Tibamoso, G. and A. Rueda (2007). Semi-automatic Liver Segmentation From omputed Tomography (CT) Scans based on Deformable Surfaces 3D Segmentation In The Clinic: A Grand Challenge.
%Withey, D. J. and Z. J. Koles (2007). Medical Image Segmentation: Methods and Software. Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging, 2007. NFSI-ICFBI 2007. Joint Meeting of the 6th International Symposium on.
%Yasmin, M., M. Sharif, et al. (2013). "Neural networks in medical imaging applications: A survey." World Applied Sciences Journal 22(1): 85-96.
%Yongfu, H., J. Tianzi, et al. (2012). Iterative multi-atlas based segmentation with multi-channel image registration and Jackknife Context Model. Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on.
%Yuqian, Z., Z. Yunlong, et al. (2010). Fuzzy C-means clustering-based multilayer perceptron neural network for liver CT images automatic segmentation. Control and Decision Conference (CCDC), 2010 Chinese.
%Zhou, N., T. Yang, et al. (2014). "An Improved FCM Medical Image Segmentation Algorithm Based on MMTD." Computational and Mathematical Methods in Medicine 2014: 8.
%Zhou, W. and Y. Xie (2014). Interactive contour delineation and refinement in treatment planning of image-guided radiation therapy.