Fast image segmentation for pulmonary lesions using hybrid level set model

The lung cancer radiotherapy treatment widely depends on adequate diagnosis. The radiologists intend to reach an image segmentation efficiency in terms of accuracy and low computation cost. However, the pulmonary lesions segmentation is still considered as a challenging task due to the noise and intensity inhomogeneity present in Computed Tomography (CT). In this study, we proposed to accelerate the nonlinear adaptive level set model, using the Bayesian rule, by incorporated the double well potential in the regularization term to get accurate and fast pulmonary lesion segmentation in CT images. We have tested the proposed method on different sized and localized lesions. All the images were taken from the database without any preprocessing. The experimental results show significant speed improvement without losing the precision of segmentation.

[1]  Ken Masamune,et al.  Region-Growing Based Feature Extraction Algorithm for Tree-Like Objects , 1996, VBC.

[2]  Chunming Li,et al.  Level set evolution without re-initialization: a new variational formulation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[3]  Dazhe Zhao,et al.  Image segmentation and bias correction using local inhomogeneous iNtensity clustering (LINC): A region-based level set method , 2017, Neurocomputing.

[4]  He Guo,et al.  Weighted kernel mapping model with spring simulation based watershed transformation for level set image segmentation , 2017, Neurocomputing.

[5]  Samuel G Armato,et al.  Automated detection of lung nodules in CT scans: effect of image reconstruction algorithm. , 2003, Medical physics.

[6]  David Gur,et al.  Automated lung segmentation in X-ray computed tomography: development and evaluation of a heuristic threshold-based scheme. , 2003, Academic radiology.

[7]  Xuelong Li,et al.  A Nonlinear Adaptive Level Set for Image Segmentation , 2014, IEEE Transactions on Cybernetics.

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

[9]  Bram van Ginneken,et al.  Toward automated segmentation of the pathological lung in CT , 2005, IEEE Transactions on Medical Imaging.

[10]  John W. Fisher,et al.  Submitted to Ieee Transactions on Image Processing a Nonparametric Statistical Method for Image Segmentation Using Information Theory and Curve Evolution , 2022 .

[11]  Hao Li,et al.  Nonparametric Statistical Active Contour Based on Inclusion Degree of Fuzzy Sets , 2016, IEEE Transactions on Fuzzy Systems.

[12]  Eric A. Hoffman,et al.  Lung lobe segmentation by graph search with 3D shape constraints , 2001, SPIE Medical Imaging.

[13]  Yifei Zhang,et al.  A novel approach of lung segmentation on chest CT images using graph cuts , 2015, Neurocomputing.

[14]  Mehdi Astaraki,et al.  Evaluation of localized region-based segmentation algorithms for CT-based delineation of organs at risk in radiotherapy , 2018, Physics and imaging in radiation oncology.

[15]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[16]  Tae-Sun Choi,et al.  Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor , 2014, Comput. Methods Programs Biomed..

[17]  Huijun Gao,et al.  A Curve Evolution Approach for Unsupervised Segmentation of Images With Low Depth of Field , 2013, IEEE Transactions on Image Processing.

[18]  Rachid Deriche,et al.  A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape , 2007, International Journal of Computer Vision.

[19]  Din-Chang Tseng,et al.  Medical Image Segmentation Based on the Bayesian Level Set Method , 2007, MIMI.

[20]  E. Hoffman,et al.  Computer recognition of regional lung disease patterns. , 1999, American journal of respiratory and critical care medicine.

[21]  Wei Jia,et al.  A novel dual minimization based level set method for image segmentation , 2016, Neurocomputing.

[22]  Milan Sonka,et al.  Segmentation of pathological and diseased lung tissue in CT images using a graph-search algorithm , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[23]  J. Sethian,et al.  FRONTS PROPAGATING WITH CURVATURE DEPENDENT SPEED: ALGORITHMS BASED ON HAMILTON-JACOB1 FORMULATIONS , 2003 .

[24]  G J Kemerink,et al.  On segmentation of lung parenchyma in quantitative computed tomography of the lung. , 1998, Medical physics.

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

[26]  Wei Shen,et al.  Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification , 2017, Pattern Recognit..

[27]  Eric A. Hoffman,et al.  Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images , 2001, IEEE Transactions on Medical Imaging.

[28]  A. Mouelhi,et al.  Hybrid segmentation of breast cancer cell images using a new fuzzy active contour model and an enhanced watershed method , 2013, 2013 International Conference on Control, Decision and Information Technologies (CoDIT).

[29]  Takayuki Kitasaka,et al.  Lung area extraction from 3D chest X-ray CT images using a shape model generated by a variable Be'zier surface , 2003, Systems and Computers in Japan.

[30]  Julia A. Schnabel,et al.  A level-set approach to joint image segmentation and registration with application to CT lung imaging , 2017, Comput. Medical Imaging Graph..

[31]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[32]  Haroon Rasheed,et al.  3-D segmentation of lung nodules using hybrid level sets , 2018, Comput. Biol. Medicine.

[33]  He Ma,et al.  An Automatic Detection System of Lung Nodule Based on Multigroup Patch-Based Deep Learning Network , 2018, IEEE Journal of Biomedical and Health Informatics.

[34]  Mohammad Hossein Fazel Zarandi,et al.  Hybrid intelligent approach for diagnosis of the lung nodule from CT images using spatial kernelized fuzzy c-means and ensemble learning , 2018, Math. Comput. Simul..

[35]  ChoiTae-Sun,et al.  Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor , 2014 .

[36]  Chunming Li,et al.  Computerized Medical Imaging and Graphics Active Contours Driven by Local and Global Intensity Fitting Energy with Application to Brain Mr Image Segmentation , 2022 .

[37]  Hoon Ko,et al.  Automatic Lung Segmentation With Juxta-Pleural Nodule Identification Using Active Contour Model and Bayesian Approach , 2018, IEEE Journal of Translational Engineering in Health and Medicine.

[38]  Mingyan Jiang,et al.  A novel level set model with automated initialization and controlling parameters for medical image segmentation , 2016, Comput. Medical Imaging Graph..

[39]  Ahmed S. Moussa,et al.  Tumor volume fuzzification for intelligent cancer staging , 2015, Appl. Soft Comput..

[40]  Wenqing Sun,et al.  Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis , 2017, Comput. Biol. Medicine.

[41]  Bram van Ginneken,et al.  Automatic detection of large pulmonary solid nodules in thoracic CT images. , 2015, Medical physics.