Abstract
Liver is the important organ and common site for a variety of cancer diseases. The most important steps in treatment planning and evaluation of liver cancer are to identify the presence of liver cancer and to determine the various stages of liver cancer. This paper proposes an automatic method to segment the liver from abdominal computer tomography imaging and classify the liver as normal or abnormal liver. The aim of this work is to develop computer-aided liver analysis to segment the liver and classify the liver, thereby hel** the physician for treatment planning and surgery. The method uses median filter for preprocessing and neutrosophic (NS) domain with FCM thresholding for segmenting the liver. In post processing, morphological operation is done to obtain liver contour. Features are extracted from the segmented liver using gray-level co-occurrence matrix (GLCM). These feature vectors are given as input to train the support vector machine (SVM) classifier, to classify healthy or unhealthy liver. The classifier performances are assessed and analyzed using various quality metrics like accuracy, sensitivity, specificity and misclassification rate.
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References
Gotra, A., Sivakumaran, L.: Liver segmentation: indications, techniques and future directions. Insight Imag. 8, 377–392 (2017)
Jayanthi, M.: New edge preserving filter for better enhancement of liver CT images. Indian J. Sci. Technol. 10(10), 1–7 (2017)
Jayanthi, M.: Segmentation of liver abnormality using label connected algorithm. IJSET 6(7), 247–249 (2017)
Jayanthi, M.: Comparative study of different techniques used for medical image segmentation of liver from abdominal CT scan. In: IEEE WiSPNET 2016 Conference, pp. 1462–1465 (2016)
Sayed, G.I., Ali, M.A., Gaber, T., Hassanien, A.E., Snasel, V.: A Hybrid Segmentation Approach Based on Neutrosophic Sets and Modified Watershed: A Case of Abdominal CT Liver Parenchyma. IEEE, pp. 144–149 (2015)
Kumar, S.S., Moni, R.S., Rajeesh, J.: Contourlet transform based computer-aided diagnosis system for liver tumor on computed tomography images. In: International Conference on Signal Processing, CCN Technologies (2011)
Joshi, D., Londhe, N.D.: Automatic liver tumour detection. IJCTEE 3(1) (2013)
Priyadarsini, S., Selvathi, D.: Survey on segmentation of liver from CT images. In: IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), pp. 234–238 (2012)
Gunasundari, S., Suganya Ananthi, M.: Comparison and evaluation of methods for liver tumor classification from CT Dataset. Int. J. Comput. Appl. 39(18) (2012)
Kumar, S.S., Moni, R.S., Rajeesh, I.: Automatic liver and lesion segmentation: a primary step in diagnosis of liver diseases. Signal Imag. Video Process. https://doi.org/10.1007/s11760-011-0223-y (2011)
Fujita, H., Zhang, X., Kido, S., Hara, T., Zhou, X., Hatanaka, Y., Xu, R.: Introduction to CAD System. In: International Conference on Future Computer, Control and Communication 2010, pp. 200–205 (2010)
Mala, K., Sadasivam, V., Alagappan, S.: Neural network based texture analysis of liver tumor from computed tomography images. Int. J. Biol. Biomed. Med. Sci. 2(1), 33–37 (2007)
Kumar, S.S., Moni, R.S., Rajeesh, I.: Automatic liver and lesion segmentation: a primary step in diagnosis of liver diseases. Signal Imag. Video Process. https://doi.org/10.1007/s11760-011-0223-y (2011)
Gao, L., Heath, D., Kuszyk, B.: Automatic liver segmentation technique for three- dimensional visualization of CT data. Radiology 201, 359–364 (1996)
Zhang, B.: A Novel Approaches to Image Segmentation Based on Neutrosophic Logic (2010)
Priyadarsini, S., Selvathi, D.: Survey on segmentation of liver from CT images. In: IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), pp. 234–238 (2012)
Acknowledgements
I would like to thank Professor Dr. B. Kanmani, Dean of Academics, BMS college of Engineering, for guiding me and providing necessary resources.
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Muthuswamy, J. (2019). Extraction and Classification of Liver Abnormality Based on Neutrosophic and SVM Classifier. In: Pati, B., Panigrahi, C., Misra, S., Pujari, A., Bakshi, S. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 713. Springer, Singapore. https://doi.org/10.1007/978-981-13-1708-8_25
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DOI: https://doi.org/10.1007/978-981-13-1708-8_25
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