A Novel Approach to Extract Exact Liver Image Boundary from Abdominal CT Scan using Neutrosophic Set and Fast Marching Method

Abstract Liver segmentation from abdominal computed tomography (CT) scan images is a complicated and challenging task. Due to the haziness in the liver pixel range, the neighboring organs of the liver have the same intensity level and existence of noise. Segmentation is necessary in the detection, identification, analysis, and measurement of objects in CT scan images. A novel approach is proposed to meet the challenges in extracting liver images from abdominal CT scan images. The proposed approach consists of three phases: (1) preprocessing, (2) CT scan image transformation to neutrosophic set, and (3) postprocessing. In preprocessing, noise in the CT scan is reduced by median filter. A “new structure” is introduced to transform a CT scan image into a neutrosophic domain, which is expressed using three membership subsets: true subset (T), false subset (F), and indeterminacy subset (I). This transform approximately extracts the liver structure. In the postprocessing phase, morphological operation is performed on the indeterminacy subset (I). A novel algorithm is designed to identify the start points within the liver section automatically. The fast marching method is applied at start points that grow outwardly to detect the accurate liver boundary. The evaluation of the proposed segmentation algorithm is concluded using area- and distance-based metrics.

[1]  Nadeem Akhtar,et al.  K-mean algorithm for Image Segmentation using Neutrosophy , 2014, 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[2]  Syamsiah Mashohor,et al.  Automatic liver segmentation on Computed Tomography using random walkers for treatment planning , 2016, EXCLI journal.

[3]  Michelle Chen,et al.  A Model for Spheroid versus Monolayer Response of SK-N-SH Neuroblastoma Cells to Treatment with 15-Deoxy-PGJ 2 , 2016, Comput. Math. Methods Medicine.

[4]  Yanhui Guo,et al.  A Novel Image Segmentation Algorithm Based on Fuzzy C-means Algorithm and Neutrosophic Set , 2008 .

[5]  Fang Lu,et al.  Automatic 3D liver location and segmentation via convolutional neural network and graph cut , 2016, International Journal of Computer Assisted Radiology and Surgery.

[6]  Abed Heshmati,et al.  Scheme for unsupervised colour-texture image segmentation using neutrosophic set and non-subsampled contourlet transform , 2016, IET Image Process..

[7]  G. Pereira,et al.  The Role of Imaging in Radiation Therapy Planning: Past, Present, and Future , 2014, BioMed research international.

[8]  Martin Styner,et al.  Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets , 2009, IEEE Transactions on Medical Imaging.

[9]  Xiao Song,et al.  Automatic Liver Segmentation from CT Images Using Adaptive Fast Marching Method , 2013, 2013 Seventh International Conference on Image and Graphics.

[10]  Yanhui Guo,et al.  New neutrosophic approach to image segmentation , 2009, Pattern Recognit..

[11]  Yufei Chen,et al.  Liver Segmentation in CT Images Using Chan-Vese Model , 2009, 2009 First International Conference on Information Science and Engineering.

[12]  Hongbo Zhang,et al.  3D liver segmentation using multiple region appearances and graph cuts. , 2015, Medical physics.

[13]  R. Cannan,et al.  Selective internal radiation therapy with 90yttrium microspheres for extensive colorectal liver metastases , 2001, Journal of Gastrointestinal Surgery.

[14]  Bodo Rosenhahn,et al.  3D automatic liver segmentation using feature-constrained Mahalanobis distance in CT images , 2016, Biomedizinische Technik. Biomedical engineering.

[15]  LI Yuan-lu.  A Tree-Like Multiphase Level Set Algorithm for Image Segmentation Based on the Chan-Vese Model , 2006 .

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

[17]  V. Krishnaveni,et al.  Automated brain tumor segmentation on MR images based on neutrosophic set approach , 2015, 2015 2nd International Conference on Electronics and Communication Systems (ICECS).

[18]  Aboul Ella Hassanien,et al.  A hybrid segmentation approach based on Neutrosophic sets and modified watershed: A case of abdominal CT Liver parenchyma , 2015, 2015 11th International Computer Engineering Conference (ICENCO).

[19]  A. Fenster,et al.  Evaluation of Segmentation algorithms for Medical Imaging , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[20]  C. Mathers,et al.  Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012 , 2015, International journal of cancer.

[21]  Xinjian Chen,et al.  Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images , 2015, IEEE Transactions on Image Processing.

[22]  Weiwei Wu,et al.  Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts , 2016, Comput. Math. Methods Medicine.

[23]  Ming Zhang,et al.  A neutrosophic approach to image segmentation based on watershed method , 2010, Signal Process..

[24]  Mark W. Powell,et al.  Automated performance evaluation of range image segmentation algorithms , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[25]  A. Jemal,et al.  Global Cancer Statistics , 2011 .

[26]  F. Smarandache.  A Unifying Field in Logics: Neutrosophic Logic. , 1999 .

[27]  Nuwan Madusanka,et al.  Comparison of active contour and fast marching methods of hippocampus segmentation , 2015, 2015 6th International Conference on Information and Communication Systems (ICICS).

[28]  A. Li,et al.  Selective internal radiation therapy for nonresectable hepatocellular carcinoma with intraarterial infusion of 90yttrium microspheres. , 1998, International journal of radiation oncology, biology, physics.

[29]  Lei Zhang,et al.  Fast Automated Liver Delineation from Computational Tomography Angiography , 2016, MIUA.

[30]  Jian Yang,et al.  Feature Learning Based Random Walk for Liver Segmentation , 2016, PloS one.

[31]  Chunming Li,et al.  Minimization of Region-Scalable Fitting Energy for Image Segmentation , 2008, IEEE Transactions on Image Processing.

[32]  Ravi Murthy,et al.  Yttrium-90 microsphere therapy for hepatic malignancy: devices, indications, technical considerations, and potential complications. , 2005, Radiographics : a review publication of the Radiological Society of North America, Inc.

[33]  Marco Alberto Javarone,et al.  Modeling Radicalization Phenomena in Heterogeneous Populations , 2015, PloS one.

[34]  Chunming Li,et al.  A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI , 2011, IEEE Transactions on Image Processing.

[35]  Carlos Platero,et al.  A Multiatlas Segmentation Using Graph Cuts with Applications to Liver Segmentation in CT Scans , 2014, Comput. Math. Methods Medicine.

[36]  Malek Adjouadi,et al.  An algorithm for PET tumor volume and activity quantification: without specifying camera's point spread function (PSF). , 2012, Medical physics.

[37]  Mirela Teodorescu,et al.  A Unifying Field in Logics. Book Review , 2014 .

[38]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods , 1999 .

[39]  Heng-Da Cheng,et al.  Novel approaches to image segmentation based on neutrosophic logic , 2012 .