Computer-aided kidney segmentation on abdominal CT images

In this paper, an effective model-based approach for computer-aided kidney segmentation of abdominal CT images with anatomic structure consideration is presented. This automatic segmentation system is expected to assist physicians in both clinical diagnosis and educational training. The proposed method is a coarse to fine segmentation approach divided into two stages. First, the candidate kidney region is extracted according to the statistical geometric location of kidney within the abdomen. This approach is applicable to images of different sizes by using the relative distance of the kidney region to the spine. The second stage identifies the kidney by a series of image processing operations. The main elements of the proposed system are: 1) the location of the spine is used as the landmark for coordinate references; 2) elliptic candidate kidney region extraction with progressive positioning on the consecutive CT images; 3) novel directional model for a more reliable kidney region seed point identification; and 4) adaptive region growing controlled by the properties of image homogeneity. In addition, in order to provide different views for the physicians, we have implemented a visualization tool that will automatically show the renal contour through the method of second-order neighborhood edge detection. We considered segmentation of kidney regions from CT scans that contain pathologies in clinical practice. The results of a series of tests on 358 images from 30 patients indicate an average correlation coefficient of up to 88% between automatic and manual segmentation

[1]  R. Pohle,et al.  Self-learning model-based segmentation of medical images , 2001 .

[2]  Chein-I Chang,et al.  An automatic diagnostic system for CT liver image classification. , 1998, IEEE transactions on bio-medical engineering.

[3]  Linda G. Shapiro,et al.  Knowledge-based organ identification from CT images , 1995, Pattern Recognit..

[4]  Klaus D. Tönnies,et al.  Segmentation of medical images using adaptive region growing , 2001, SPIE Medical Imaging.

[5]  Baigalmaa Tsagaan,et al.  An Automated Segmentation Method of Kidney Using Statistical Information , 2002, MICCAI.

[6]  Jong-Won Park,et al.  Segmentation of kidney without using contrast medium on abdominal CT image , 2000, WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000.

[7]  J. Sequeira,et al.  Medical image segmentation using texture directional features , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Jun Chen,et al.  Comparison of automatic and human segmentation of kidneys from CT images , 2002 .

[9]  Antonio Elias Fabris,et al.  An efficient filling algorithm for non-simple closed curves using the point containment paradigm , 1997, Proceedings X Brazilian Symposium on Computer Graphics and Image Processing.

[10]  Jianping Fan,et al.  Automatic image segmentation by integrating color-edge extraction and seeded region growing , 2001, IEEE Trans. Image Process..

[11]  Baigalmaa Tsagaan,et al.  Segmentation of kidney by using a deformable model , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[12]  W J Kostis,et al.  Computer-aided diagnosis for lung cancer. , 2000, Radiologic clinics of North America.

[13]  Jerry L Prince,et al.  Current methods in medical image segmentation. , 2000, Annual review of biomedical engineering.

[14]  Xun Wang,et al.  Deformable Contour Method: A Constrained Optimization Approach , 2004, International Journal of Computer Vision.

[15]  Jong-Won Park,et al.  Organ segmentation by comparing of gray value portion on abdominal CT image , 2000, WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000.

[16]  Klaus D. Tönnies,et al.  A New Approach for Model-Based Adaptive Region Growing in Medical Image Analysis , 2001, CAIP.

[17]  Enrico Motta,et al.  An Ontology-Driven Similarity Algorithm , 2004 .

[18]  Ron Kikinis,et al.  Statistical Validation of Automated Probabilistic Segmentation against Composite Latent Expert Ground Truth in MR Imaging of Brain Tumors , 2002, MICCAI.

[19]  Baigalmaa Tsagaan,et al.  Development of extraction method of kidneys from abdominal CT images using a three-dimensional deformable model , 2003, Systems and Computers in Japan.

[20]  D Trevathan-Ramirez.  Innovations in breast disease diagnosis. , 1998, Radiologic technology.

[21]  Kil-Houm Park,et al.  Automatic Liver Tumor Detection from CT , 2001 .

[22]  Paul Wintz,et al.  Digital image processing (2nd ed.) , 1987 .

[23]  Jim R. Parker,et al.  Algorithms for image processing and computer vision , 1996 .

[24]  Ron Kikinis,et al.  Statistical validation of image segmentation quality based on a spatial overlap index. , 2004, Academic radiology.