Abstract
Our proposed research technique intends to provide an effective liver magnetic resonance imaging (MRI) and computed tomography (CT) scan image classification which would play a significant role in medical dataset especially in feature selection and classification. There are a number of existing research works classifying the liver tumor disease. Early detection of liver tumor will help the patients to get cured rapidly. Our proposed research focuses on the classification of medical images with respect to the classification technique artificial neural network (ANN) to classify an image as normal or abnormal. In the pre-processing step, the input image is selected from the database and adaptive median filtering is used for noise removal. For better enhancement, histogram equalization (HE) is done in the noise-removed images. In the pre-processed images, the texture feature such as gray-level co-occurrence matrix (GLCM) and statistical features are extracted. From the extensive feature set, optimal features are selected using the optimal kernel K-means (OKK-means) clustering algorithm along with the oppositional firefly algorithm (OFA). The proposed method obtained 97.5% accuracy in the classification when compared to the existing method.
Author Statement
Research funding: Authors state no funding involved.
Conflict of interest: Authors state no conflict of interest.
Informed consent: Informed consent is not applicable.
Ethical approval: The conducted research is not related to either human or animals use.
References
[1] Atkins MS, Mackiewich BT. Fully automatic segmentation of the brain in MRI. IEEE Trans Med Imaging 1998;17:98–107.10.1109/42.668699Search in Google Scholar
[2] Kapur T, Grimson WEL, Wells WM. Segmentation of brain tissue from magnetic resonance images. Med Image Anal 1996;1:109–27.10.1016/S1361-8415(96)80008-9Search in Google Scholar
[3] Akselrod-Ballin A, Galun M, Gomori JM, Brandt A, Basri R. Prior knowledge driven multi scale segmentation of brain MRI. In: Proceedings of the 10th International Conference on Medical Image Computing and Computer-Assisted Intervention, 2007:118–26.10.1007/978-3-540-75759-7_15Search in Google Scholar PubMed
[4] Stašák J. A contribution to image semantic analysis. In: Proceedings of Electronic Computers and Informatics ECI, 2004,Košice, Poland: The University of Technology, Department of Computers and Informatics of FEI, pp. 132–4.Search in Google Scholar
[5] Gupta S, Sandhu PS, Mohan N. Implementing color image segmentation using biogeography based optimization. In: Proceedings of International Conference on Computer and Communication Technologies, 2012:167–70.Search in Google Scholar
[6] Gómez O, González JA, Morales EF. Image segmentation using automatic seeded region growing and instance-based learning. In: Proceedings of the Congress on Pattern Recognition 12th Iberoamerican Conference on Progress in Pattern Recognition, Image Analysis and Application, 2007:192–201.10.1007/978-3-540-76725-1_21Search in Google Scholar
[7] Wang Z, Jensen JR, Im J. An automatic region-based image segmentation algorithm for remote sensing applications. Environ Modell Softw 2010;25:1149–65.10.1016/j.envsoft.2010.03.019Search in Google Scholar
[8] Demirhan A, Törü M, Güler I. Segmentation of tumor and edema along with healthy tissues of brain using wavelets and neural networks. IEEE J Biomed Health Inform 2015;19:1451–8.10.1109/JBHI.2014.2360515Search in Google Scholar PubMed
[9] Gordillo N, Montseny E, Sobrevilla P. State of the art survey on MRI brain tumor segmentation. Magn Reson Imaging 2013;31:1426–38.10.1016/j.mri.2013.05.002Search in Google Scholar PubMed
[10] Corso JJ, Sharon E, Dube S, El-Saden S, Sinha U, Yuille A. Efficient multilevel brain tumor segmentation with integrated Bayesian model classification. IEEE Trans Med Imaging 2008;27:629–40.10.1109/TMI.2007.912817Search in Google Scholar PubMed
[11] Shen S, Sandham W, Granat M, Sterr A. MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization. IEEE Trans Inf Technol Biomed 2005;9:459–67.10.1109/TITB.2005.847500Search in Google Scholar
[12] Bereciartua A, Picon A, Galdran A, Iriondo P. 3D active surfaces for liver segmentation in multi-sequence MRI images. Comput Methods Programs Biomed 2016;132:149–60.10.1016/j.cmpb.2016.04.028Search in Google Scholar PubMed
[13] Chartrand G, Cresson T, Chav R, Gotra A, Tang A, De Guise JA. Liver segmentation on CT and MR using Laplacian mesh optimization. IEEE Trans Biomed Eng 2017;64:2110–2110.1109/TBME.2016.2631139Search in Google Scholar PubMed
[14] López-Mir F, Naranjo V, Angulo J, Alcañiz M, Luna L. Liver segmentation in MRI: a fully automatic method based on stochastic partitions. Comput Methods Programs Biomed 2014;114:11–28.10.1016/j.cmpb.2013.12.022Search in Google Scholar PubMed
[15] Swierczynski P, Papież BW, Schnabel JA, Macdonald C. A level-set approach to joint image segmentation and registration with application to CT lung imaging. Comput Med Imaging Graph 2018;65:58–68.10.1016/j.compmedimag.2017.06.003Search in Google Scholar PubMed PubMed Central
[16] Huang Q, Ding H, Wang X, Wang G. Fully automatic liver segmentation in CT images using modified graph cuts and feature detection. Comput Biol Med 2018;95:198–208.10.1016/j.compbiomed.2018.02.012Search in Google Scholar PubMed
[17] Anter AM, Hassenian AE. Computational intelligence optimization approach based on particle swarm optimizer and neutrosophic set for abdominal CT liver tumor segmentation. J Comput Sci 2018;25:376–87.10.1016/j.jocs.2018.01.003Search in Google Scholar
[18] Masoumi H, Behrad A, Pourmina MA, Roosta A. Automatic liver segmentation in MRI images using an iterative watershed algorithm and artificial neural network. Biomed Signal Process Control 2012;7:429–37.10.1016/j.bspc.2012.01.002Search in Google Scholar
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