Kernel-based Bayesian clustering of computed tomography images for lung nodule segmentation

Lung nodule segmentation is an interesting research topic, and it serves as an effective solution for the diagnosis of Lung cancer. The existing methods of lung nodule segmentation suffer from accuracy issues due to the heterogeneity of the nodules in the lungs and the presence of visual deviations in the nodules. Thus, there is a requirement for an effective lung nodule segmentation, which assists the physicians in making accurate decisions. Accordingly, this study proposes a lung nodule segmentation process based on the kernel-based Bayesian fuzzy clustering (BFC), which is the integration of kernel functions in the BFC. Initially, the input computed tomography image is pre-processed for ensuring the effective segmentation, and the lobes are identified using the adaptive thresholding strategy. Then, the dominant areas in the lobes are identified using a scale-invariant feature transform descriptor, and the significant nodules are extracted using the grid-based segmentation. Finally, the lung nodules are segmented using the proposed kernel-based BFC. The proposed algorithm is evaluated using the Lung Image Database Consortium and Image Database Resource Initiative database, and it acquires the accuracy, sensitivity, and false positive rate of 0.955, 0.999, and 0.025, respectively.

[1]  Ezhil E. Nithila,et al.  Segmentation of lung nodule in CT data using active contour model and Fuzzy C-mean clustering , 2016 .

[2]  Senthilkumar Krishnamurthy,et al.  Three-dimensional lung nodule segmentation and shape variance analysis to detect lung cancer with reduced false positives , 2016, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[3]  Qiang Li,et al.  Recent progress in computer-aided diagnosis of lung nodules on thin-section CT , 2007, Comput. Medical Imaging Graph..

[4]  Yan Qiang,et al.  Automated Lung Nodule Segmentation Using an Active Contour Model Based on PET/CT Images , 2015 .

[5]  Bram van Ginneken,et al.  A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification , 2009, Medical Image Anal..

[6]  Jason Cong,et al.  An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy , 2015, Comput. Biol. Medicine.

[7]  Caiming Zhang,et al.  Medical image segmentation using improved FCM , 2012, Science China Information Sciences.

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

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

[10]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[11]  Abbas Z. Kouzani,et al.  Automated detection of lung nodules in computed tomography images: a review , 2010, Machine Vision and Applications.

[12]  Temesguen Messay,et al.  Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset , 2015, Medical Image Anal..

[13]  Antoni B. Chan,et al.  On measuring the change in size of pulmonary nodules , 2006, IEEE Transactions on Medical Imaging.

[14]  Aurélio J. C. Campilho,et al.  Hessian based approaches for 3D lung nodule segmentation , 2016, Expert Syst. Appl..

[15]  Marcelo Gattass,et al.  Automatic segmentation of lung nodules with growing neural gas and support vector machine , 2012, Comput. Biol. Medicine.

[16]  Jiazheng Shi,et al.  A top-down region dividing approach for image segmentation , 2008, Pattern Recognit..

[17]  Andrés Ortiz,et al.  Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering , 2014, Inf. Sci..

[18]  João Manuel R. S. Tavares,et al.  Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images , 2017, Medical Image Anal..

[19]  Ghassan Hamarneh,et al.  Medial-Based Deformable Models in Nonconvex Shape-Spaces for Medical Image Segmentation , 2012, IEEE Transactions on Medical Imaging.

[20]  Jayaram K. Udupa,et al.  A Generic Approach to Pathological Lung Segmentation , 2014, IEEE Transactions on Medical Imaging.

[21]  Gareth Funka-Lea,et al.  Graph Cuts and Efficient N-D Image Segmentation , 2006, International Journal of Computer Vision.

[22]  Paul D. Gader,et al.  Bayesian Fuzzy Clustering , 2015, IEEE Transactions on Fuzzy Systems.

[23]  M. Eremets,et al.  Ammonia as a case study for the spontaneous ionization of a simple hydrogen-bonded compound , 2014, Nature Communications.

[24]  Aly A. Farag,et al.  A Novel Approach for Lung Nodules Segmentation in Chest CT Using Level Sets , 2013, IEEE Transactions on Image Processing.

[25]  Marcos Salganicoff,et al.  Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models , 2011, Medical Image Anal..

[26]  R. Bellotti,et al.  A CAD system for nodule detection in low-dose lung CTs based on region growing and a new active contour model. , 2007, Medical physics.

[27]  Zhenyu Liu,et al.  Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation , 2017, Medical Image Anal..