Fast Renal Cortex Localization by Combining Generalized Hough Transform and Active Appearance Models
Automatic localization of objects is one of great important steps in object recognition and analysis, such as segmentation, registration in many medical applications. In this paper, an automated method is proposed to recognize renal cortex on contrast-enhanced abdominal CT images. The proposed method is based on a strategic combination of the Generalized Hough Transform and Active Appearance Model. It consists of two main phases: training and localization. In the training phase, we train the mean shape models of renal cortex by using Active Appearance Model and compute Generalized Hough Transform parameters. In the localization phase, a modified Generalized Hough Transform algorithm is advanced to estimate potential center of gravity for improving the conventional Active Appearance Model matching method, and then a two-pass Active Appearance Model matching method is proposed based on Generalized Hough Transform. The Active Appearance Models and Generalized Hough Transform parameters were trained with 20 CT angiography datasets, and then the proposed method was tested on a clinical data set of 17 CT angiography datasets. The experimental results show that: (1) an overall cortex localization accuracy is 0.9920±0.0038, average distance is 11.00±9.34 pixels. (2) The proposed method is highly efficient such that the overall localization can be finalized within 1.2075±0.3738 seconds for each 2D slice.
KeywordsLocalization kidney renal cortex generalized hough transform active appearance model
Unable to display preview. Download preview PDF.
- 1.American Cancer Society: What are the key statistics about kidney cancer?, http://www.cancer.org/Cancer/KidneyCancer/DetailedGuide/kidney-cancer-adult-key-statistics
- 3.Rusko, L., Bekes, G., Nemeth, G., Fidrich, M.: Fully automatic liver segmentation for contrast-enhanced ct images. In: MICCAI Wshp. 3D Segmentation in the Clinic: A Grand Challenge, vol. 2(7) (2007)Google Scholar
- 12.Sun, Y., Jolly, M.P., Moura, J.: Integrated registration of dynamic renal perfusion mr images. In: 2004 International Conference on Image Processing, ICIP 2004, vol. 3, pp. 1923–1926. IEEE (2004)Google Scholar
- 13.Zöllner, F.G., Sance, R., Rogelj, P., Ledesma-Carbayo, M.J., Rørvik, J., Santos, A., Lundervold, A.: Assessment of 3d dce-mri of the kidneys using non-rigid image registration and segmentation of voxel time courses. Computerized Medical Imaging and Graphics 33(3), 171–181 (2009)CrossRefGoogle Scholar
- 15.Khoshelham, K.: Extending generalized hough transform to detect 3d objects in laser range data. In: Proceedings of ISPRS Workshop on Laser Scanning, LS 2007, pp. 206–210 (2007)Google Scholar