Morph-Rec: A Novel Computer-Aided Liver Segmentation Model based on Morphological Reconstruction Operation

REFERENCES

• T. Nadarevic, V. Giljaca, and A. Colli, “Computed tomography for the diagnosis of hepatocellular carcinoma in chronic advanced liver disease,” Cochrane Database Syst. Rev. 2019. DOI: 10.1002/14651858.CD013362.
   Web of Science ®Google Scholar
• L.-Q. Zhou, J.-Y. Wang, S.-Y. Yu, “Artificial intelligence in medical imaging of the liver,” WJG, Vol. 25, pp. 672–682, 2019. DOI: 10.3748/wjg.v25.i6.672.
   PubMed Web of Science ®Google Scholar
• R. Kelsch. Radiology for the USMLE: Abdominal anatomy. Available: https://www.medschooltutors.com/blog/radiology-for-the-usmle-abdominal-anatomy. Accessed 17 Jun. 2021.
   Google Scholar
• K. Mala, V. Sadasivam, and S. Alagappan, “Neural network based texture analysis of CT images for fatty and cirrhosis liver classification,” Appl. Soft. Comput., Vol. 32, pp. 80–86, 2015. DOI: 10.1016/j.asoc.2015.02.034.
   Web of Science ®Google Scholar
• S. S. Kumar, and D. R. S. Moni. Diagnosis of liver tumor from CT images using curvelet transform. 02:6, 2010.
   Google Scholar
• G. I. Sayed, et al., “A hybrid segmentation approach based on Neutrosophic sets and modified watershed: A case of abdominal CT Liver parenchyma,” in 2015 11th International Computer Engineering Conference (ICENCO), Cairo, Egypt: IEEE, 2015, pp. 144–149.
   Google Scholar
• F. Smarandache. A unifying field in logics: Neutrosophic logic: neutrosophy, neutrosophic set, neutrosophic probability and statistics, 4th ed. Rehoboth, NM: American Research Press, 2005.
   Google Scholar
• S. K. Siri, and M. V. Latte, “A novel approach to extract exact liver image boundary from abdominal CT scan using neutrosophic set and fast marching method,” J. Intell. Syst., Vol. 28, pp. 517–532, 2019. DOI: 10.1515/jisys-2017-0144.
   Web of Science ®Google Scholar
• A. Das, U. R. Acharya, S. S. Panda, and S. Sabut, “Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques,” Cogn. Syst. Res., Vol. 54, pp. 165–175, 2019. DOI: 10.1016/j.cogsys.2018.12.009.
   Web of Science ®Google Scholar
• A. Zidan, N. I. Ghali, A. e. Hassamen, and H. Hefny, “Level set-based CT liver image segmentation with watershed and artificial neural networks,” in 2012 12th International Conference on Hybrid Intelligent Systems (HIS). A conditional statistical shape model with integrated error estimation of the conditions; Application to liver segmentation in non-contrast CT images, 2012.
   Google Scholar
• A. Mostafa, et al., “Evaluating swarm optimization algorithms for segmentation of liver images,” in Barstugan Barstugan, AE Hassanien and DA Oliva, Eds. Cham: Springer International Publishing, 2018, pp. 41–62.
   Google Scholar
• M. Barstugan, R. Ceylan, M. Sivri, and H. Erdogan, “Automatic liver segmentation in abdomen CT images using SLIC and AdaBoost algorithms,” in Proceedings of the 2018 8th International Conference on Bioscience, Biochemistry and Bioinformatics – ICBBB 2018, Tokyo, Japan: ACM Press, 2018, pp. 129–133.
   Google Scholar
• A. Mostafa, M. A. Elfattah, and A. Fouad, “Region growing segmentation with iterative K-means for CT liver images,” in 2015 4th International Conference on Advanced Information Technology and Sensor Application (AITS), Harbin, China: IEEE, 2015, pp. 88–91.
   Google Scholar
• B. Prencipe, N. Altini, and G. D. Cascarano, “A novel approach based on region growing algorithm for liver and spleen segmentation from CT scans,” in Intelligent Computing Theories and Application, D-S Huang, V Bevilacqua, and A Hussain, Eds. Cham: Springer International Publishing, 2020, pp. 398–410.
   Google Scholar
• Z. Zhou, Z. Xue-chang, and Z. Si-ming, “Semi-automatic liver segmentation in CT images through intensity separation and region growing,” Procedia. Comput. Sci., Vol. 131, pp. 220–225, 2018. DOI: 10.1016/j.procs.2018.04.206.
   Google Scholar
• R. M. Haralick, S. R. Sternberg, and X. Zhuang, “Image analysis using mathematical morphology,” IEEE Trans Pattern Anal Mach Intell PAMI-9, 532–550, 1987. DOI: 10.1109/TPAMI.1987.4767941.
   PubMed Web of Science ®Google Scholar
• E. R. Davies, “Image filtering and morphology,” in Computer vision, ER Davies, Ed., UK: Elsevier, 2018, pp. 39–92.
   Google Scholar
• Morphological Image Processing. Avaibale: https://www.cs.auckland.ac.nz/courses/compsci773s1c/lectures/ImageProcessing-html/topic4.htm. Accessed 22 Jun. 2021.
   Google Scholar
• L. Vincent, “Morphological grayscale reconstruction in image analysis: applications and efficient algorithms,” in IEEE Trans on Image Process 2:Level set-based CT liver image segmentation with watershed and artificial neural networks, 1993. DOI: 10.1109/83.217222
   Google Scholar
• P. Bilic, P. F. Christ, and E. Vorontsov. (2019) The liver tumor segmentation benchmark (LiTS). arXiv:190104056 [cs].
   Google Scholar
• 3Dircadb | IRCAD France. Available: https://www.ircad.fr/research/3dircadb/. Accessed 17 Jun. 2021.
   Google Scholar
• LiTS – Liver tumor segmentation challenge (LiTS17). In: Academic torrents. https://academictorrents.com/details/27772adef6f563a1ecc0ae19a528b956e6c803ce. Accessed 17 Jun. 2021.
   Google Scholar
• 3D-IRCADb 01 | IRCAD France. Available: https://www.ircad.fr/research/3d-ircadb-01/. Accessed 29 Apr. 2021.
   Google Scholar
• Hounsfield Scale – An overview | ScienceDirect Topics. Available: https://www.sciencedirect.com/topics/medicine-and-dentistry/hounsfield-scale. Accessed 29 Apr. 2021.
   Google Scholar
• R. Mayasari, and N. Heryana, “Reduce noise in computed tomography image using adaptive Gaussian Filter,” International Journal of Computer Techniques, Vol. 6, pp. 17–20, 2019.
   Google Scholar
• D. Chicco, and G. Jurman, “The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,” BMC Genomics, Vol. 21, pp. 6, 2020. DOI: 10.1186/s12864-019-6413-7.
   PubMed Web of Science ®Google Scholar
• L. Zhou, et al., “Multi-stage liver segmentation in CT scans using gaussian pseudo variance level set,” IEEE. Access., Vol. 9, pp. 101414–101423, 2021. DOI: 10.1109/ACCESS.2021.3097387.
   Web of Science ®Google Scholar
• X. Shu, Y. Yang, and B. Wu, “Adaptive segmentation model for liver CT images based on neural network and level set method,” Neurocomputing, Vol. 453, pp. 438–452, 2021. DOI: 10.1016/j.neucom.2021.01.081.
   Web of Science ®Google Scholar
• S. Tomoshige, et al., “A conditional statistical shape model with integrated error estimation of the conditions; Application to liver segmentation in non-contrast CT images,” in Medical image analysis 18: Segmentation of liver and spleen based on computational anatomy models, 2014. DOI: 10.1016/j.media.2013.10.003.
   Google Scholar
• C. Dong, et al., “Segmentation of liver and spleen based on computational anatomy models. Computers in Biology and Medicine 67:An Automated Computer-aided Diagnosis System for Abdominal CT Liver Images,” Procedia. Comput. Sci. 2015. DOI: 10.1016/j.compbiomed.2015.10.007.
   Google Scholar
• G. I. Sayed, A. E. Hassanien, and G. Schaefer, “An automated computer-aided diagnosis system for abdominal CT liver images,” Procedia. Comput. Sci., Vol. 90, pp. 68–73, 2016. DOI: 10.1016/j.procs.2016.07.012.
   Google Scholar
• S. K. Siri, and M. V. Latte, “Combined endeavor of neutrosophic set and Chan-Vese model to extract accurate liver image from CT scan,” Comput. Methods Programs Biomed., Vol. 151, pp. 101–109, 2017. DOI: 10.1016/j.cmpb.2017.08.020.
   PubMed Web of Science ®Google Scholar
• H. Jiang, S. Li, and S. Li, “Registration-based organ positioning and joint segmentation method for liver and tumor segmentation,” BioMed Res. Int., Vol. 2018, pp. 1–11, 2018. DOI: 10.1155/2018/8536854.
   Web of Science ®Google Scholar
• A. M. Anter, and A. E. Hassenian, “CT liver tumor segmentation hybrid approach using neutrosophic sets, fast fuzzy c-means and adaptive watershed algorithm,” Artif. Intell. Med., Vol. 97, pp. 105–117, 2019. DOI: 10.1016/j.artmed.2018.11.007.
   PubMed Web of Science ®Google Scholar
• S. K. Siri, S. P. Kumar, and V. M. Latte, “Threshold-based new segmentation model to separate the liver from CT scan images,” IETE. J. Res., 1–8, 2020. DOI: 10.1080/03772063.2020.1795938.
   Web of Science ®Google Scholar