Abstract
Liver tumor also known as the hepatic tumor is a type of growth found in or on the liver. Identifying the tumor location can be a tedious, error-prone and need an experts study to identify it. This paper presents a segmentation technique to segment the liver tumor using Gradient Vector Flow (GVF) snakes algorithm. To initiate snakes algorithm the images need to be insensitive to noise, Wiener Filter is proposed to remove the noise. The GVF snake starts its process by initially extending it to create an initial boundary. The GVF forces are calculated and help in driving the algorithm to stretch and bend the initial contour towards the region of interest due to the difference in intensity. The images were classified into tumor and non-tumor categories by Artificial Neural Network Classifier depending on the features extracted which showed notable dissimilarity between normal and abnormal images.
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Baby, J., Rajalakshmi, T., Snekhalatha, U. (2019). Detection of Liver Tumor Using Gradient Vector Flow Algorithm. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_101
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DOI: https://doi.org/10.1007/978-3-030-00665-5_101
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