Kidney segmentation from CT images using a 3D NMF-guided active contour model

A hybrid framework that is based on the integration of geometric deformable models and nonnegative matrix factorization (NMF) is introduced for 3D kidney segmentation from abdominal computed tomography (CT) images. The NMF is employed due to its ability to cluster complex data by extracting discriminative features from higher dimensional space. In this paper, regional features from CT appearance, a kidney shape model, and spatial interactions are fused using the NMF to produce a more robust model to guide the deformable model's evolution. The shape model is constructed using a set of training images and is updated during segmentation using an appearance-based method taking into account both voxels' locations and appearances. The spatial interactions are modeled using a pair-wise Potts Markov-Gibbs random field model. Our approach has been tested on 36 in-vivo 3D CT data sets and evaluated using, the Dice coefficient, the 95-percentile Hausdorff distance, and percentage kidney volume difference. Evaluation results show that feature fusion using NMF increases the deformable model's ability to accurately segment complex CT kidney data.

[1]  Georgy L. Gimel'farb,et al.  A new deformable model-based segmentation approach for accurate extraction of the kidney from abdominal CT images , 2011, 2011 18th IEEE International Conference on Image Processing.

[2]  Daw-Tung Lin,et al.  Computer-aided kidney segmentation on abdominal CT images , 2006, IEEE Transactions on Information Technology in Biomedicine.

[3]  L.-K. Shark,et al.  Medical Image Segmentation Using New Hybrid Level-Set Method , 2008, 2008 Fifth International Conference BioMedical Visualization: Information Visualization in Medical and Biomedical Informatics.

[4]  Jakob Wasza,et al.  Article in Press G Model Computerized Medical Imaging and Graphics Segmentation of Kidneys Using a New Active Shape Model Generation Technique Based on Non-rigid Image Registration , 2022 .

[5]  Laurent D. Cohen,et al.  Automatic Detection and Segmentation of Kidneys in 3D CT Images Using Random Forests , 2012, MICCAI.

[6]  Xinjian Chen,et al.  Medical Image Segmentation by Combining Graph Cuts and Oriented Active Appearance Models , 2012, IEEE Transactions on Image Processing.

[7]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[8]  Ayman El-Baz,et al.  3D Kidney Segmentation from CT Images Using a Level Set Approach Guided by a Novel Stochastic Speed Function , 2011, MICCAI.

[9]  Ronald M. Summers,et al.  Computer-aided detection of exophytic renal lesions on non-contrast CT images , 2015, Medical Image Anal..

[10]  Ronald M. Summers,et al.  Automatic 3D kidney segmentation based on shape constrained GC-OAAM , 2011, Medical Imaging.

[11]  Yanmei Liang,et al.  Contextual information-aided kidney segmentation in CT sequences , 2013 .

[12]  Chengwen Chu,et al.  Multi-organ Segmentation Based on Spatially-Divided Probabilistic Atlas from 3D Abdominal CT Images , 2013, MICCAI.

[13]  Huazhong Shu,et al.  Automatic kidney segmentation in CT images based on multi-atlas image registration , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  Baba C. Vemuri,et al.  Nonnegative Factorization of Diffusion Tensor Images and Its Applications , 2011, IPMI.

[15]  Laurent D. Cohen,et al.  Automatic Detection and Segmentation of Kidneys in 3 D CT Images Using Random Forests , 2012 .

[16]  Aly A. Farag,et al.  Precise segmentation of multimodal images , 2006, IEEE Transactions on Image Processing.

[17]  N. Komodakis,et al.  Non-rigid Registration using Discrete MRFs: Application to Thoracic CT Images , 2010 .

[18]  Michael W. Berry,et al.  Document clustering using nonnegative matrix factorization , 2006, Inf. Process. Manag..

[19]  Ayman El-Baz,et al.  A Novel NMF Guided Level-set for DWI Prostate Segmentation , 2014 .

[20]  Pau-Choo Chung,et al.  Multiphase level set with multi dynamic shape models on kidney segmentation of CT image , 2009, 2009 IEEE Biomedical Circuits and Systems Conference.

[21]  F. Y. Wu.  The Potts model , 1982 .