Abstract
Engineering of complex intelligent systems often requires experts to decompose the task into smaller constituent processes. This allows the domain experts to identify and solve specific sub-tasks, which collectively solve the system’s goals. The engineering of individual processes and their relationships represent a knowledge acquisition challenge, which is complicated by incremental ad-hoc revisions that are inevitable in light of evolving data and expertise. Incremental revisions introduce a risk of degrading the system and limit experts’ ability to build complex intelligent systems. We present an incremental engineering method called ProcessNet that structures incremental ad-hoc changes to a system and mitigates the risks of the changes degrading the system. A medical image analysis application developed using ProcessNet demonstrates that despite a large number of ad-hoc, incremental changes the system’s ability and accuracy in segmenting multiple anatomical regions in High Resolution Computed Tomography (HRCT) scans continue to improve.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Jain, A., Duin, R., Mao, J.: Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)
Crevier, D., Lepage, R.: Knowledge-Based Image Understanding Systems: A Survey. Computer Vision and Image Understanding 67(2), 161–185 (1997)
Compton, P.J., Jansen, R.: A philosophical basis for knowledge acquisition. Knowledge Acquisition 2, 241–257 (1990)
Compton, P., Peters, L., Edwards, G., Lavers, T.: Experience with ripple-down rules. Knowledge Based Systems Journal 19(5), 356–362 (2006)
Bekmann, J., Hoffmann, A.: Improved Knowledge Acquisition for High-Performance Heuristic Search. In: International Joint Conference on Artificial Intelligence, pp. 41–46 (2005)
Misra, A., Sowmya, A., Compton, P.: Incremental Learning of Control Knowledge for Lung Boundary Extraction. In: Pacific Knowledge Acquisition Workshop, pp. 1–15 (2004)
Misra, A., Sowmya, A., Compton, P.: Incremental learning for segmentation in medical images. In: IEEE International Symposium on Biomedical Imaging (ISBI 2006). 1, pp. 1360–1363 (2006)
Compton, P., Cao, T., Kerr, J.: Generalising Incremental Knowledge Acquisition. In: Pacific Knowledge Acquisition Workshop, pp. 1–15 (2004)
Finlayson, A.: Incremental Knowledge-acquisition for Complex Multi-agent Environments. PhD Thesis, University of New South Wales, Sydney, Australia (2008)
Doi, K.: Current status and future potential of computer-aided diagnosis in medical imaging. The British Journal of Radiology 78, S3–S19 (2005)
Draper, B., Hanson, A., Riseman, E.: Knowledge-directed vision: control, learning, and integration. Proceedings of the IEEE 84(1-I) (November 1996)
Bovenkamp, E., Dijkstra, J., Bosch, J., Reiber, J.: Multi-agent segmentation of IVUS images. Pattern Recognition, 647–663 (2004)
Matsuyama, T.: Expert systems for image processing: Knowledge-based composition of image analysis processes. In: Proceedings of International Conference on Pattern Recognition, pp. 125–133 (1988)
Clouard, R., Elmoataz, A., Porquet, C., Revenu, M.: Borg: a knowledge-based system for automatic generation of image processing programs. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(2), 128–144 (1999)
Draper, B.A.: From knowledge bases to Markov models to PCA. In: Proceedings of Workshop on Computer Vision System Control Architectures, Graz, Austria (2003)
Draper, B.A., Bins, J., Baek, K.: ADORE: Adaptive Object Recognition. Videre 1(4), 86–99 (2000)
Levner, L., Bulitko, V.: Machine learning for adaptive image interpretation. In: Proceedings of The National Conference on Artificial Intelligence, pp. 890–876 (2004)
Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images. In: International Conference on Computer Vision (ICCV 2001), pp. 105–112 (2001)
El-Baz, A., GimelXfarb, G., Falk, R., Holland, T., Shaffer, T.: A New Stochastic Framework for Accurate Lung Segmentation. In: Proc. of International Conference on Medical Image Computing and Computer-Assisted Intervention, USA, pp. 322–330 (2008)
Massoptier, L., Misra, A., Sowmya, A.: Automatic Lung Segmentation in HRCT Images with Diffuse Parenchymal Lung Disease Using Graph-Cut. In: International Conference Image and Vision Computing New Zealand, pp. 266–270 (2009)
Brown, M.S., Wilson, L.S., Doust, B.D., Gill, R.W., Sun, C.: Knowledge-based method for segmentation and analysis of lung boundaries in chest X-ray images. Computerized Medical Imaging and Graphics 22(6), 463–477 (1998)
Sluimer, I., Schilham, A., Prokop, M., van Ginneken, B.: Computer analysis of computed tomography scans of the lung: a survey. IEEE Transactions on Medical Imaging 25(4), 385–405 (2006)
Rasband, W.S.: ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA (1997-2009), http://rsb.info.nih.gov/ij/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Misra, A., Sowmya, A., Compton, P. (2010). Incremental System Engineering Using Process Networks. In: Kang, BH., Richards, D. (eds) Knowledge Management and Acquisition for Smart Systems and Services. PKAW 2010. Lecture Notes in Computer Science(), vol 6232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15037-1_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-15037-1_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15036-4
Online ISBN: 978-3-642-15037-1
eBook Packages: Computer ScienceComputer Science (R0)