Abstract
Cancer in Liver is the one among all other types of cancer which causes death of carcinogenic victim people throughout the world. GLOBOCAN12 was an initiative for simultaneously generating the expected dominance and mortality incidence that raised out of the cancer over the whole globe. It reported that about 782,000 new cases in the population were reported to have liver cancer, in which around 745,000 people loosed their lives from these kind of diseases worldwide. Some traditional algorithms were found to be widely used in liver segmentation processes. However, it had some limitations such as less effective outcomes in terms of proceeded segmentation operations and also it was very difficult to apply tumor segmentation especially for larger severity intensities of tumor region, which usually gave rise to high computational cost. It was also required to improve the performance of those algorithms for diagnosing even the tiniest parts of liver along with the improvisation needed when there was misclassification of the tumors near the liver boundaries. Along this way as an improvising methodology, an efficient method is proposed in order to overcome all the above discussed issues one by one through our work. The novelty/major contribution of this proposed method is being contributed in three stages namely, preprocessing, segmentation and classification. In preprocessing, the noises of image will be removed and then, the input image edge will be sharpened by using a frequency-based edge sharpening technique which aids in taking the pixels in the images into consideration for proceeding with the next operation of segmentation. The segmentation process gets the appropriated preprocessed images as input and the Outline Preservation Based Segmentation (OPBS) algorithm is used to segment the images in the segmentation phase. The algorithms involving features extraction were preferably deployed to extract the corresponding features from an image. So, the features present in the segmented image serves as the necessary information for the classification purposes. Next, the features were classified in the classification phase by using novel similarity search based hybrid classification technique. The Outline Preservation Based Segmentation and Search Based Hybrid Classification (OPBS-SSHC) used the 3D IR CAD dataset. It was used to analyze with various parameters such as accuracy, precision, recall, and F-measures. Volumetric Overlap Error (VOE), Jaccard, Dice, and Kappa will be determined later on to predict the errors in the segmentation process undertaken. The proposed method of OPBS-SSHC performance was found to be better than other classification techniques of Relevance Vector Machine (RVM), Probabilistic Neural Network (PNN), and Support Vector Machine (SVM), which were considered for comparison by taking the above metrics and coefficients as and when required throughout this extensive comparative study.
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References
AlZu’bi S, Jararweh Y, Al-Zoubi H, Elbes M, Kanan T, Gupta B (2018) Multi-orientation geometric medical volumes segmentation using 3d multiresolution analysis, Multimed Tools Appl, pp. 1–26
Baâzaoui A, Barhoumi W, Ahmed A, Zagrouba E (2017) Semi-automated segmentation of single and multiple tumors in liver CT images using entropy-based fuzzy region growing. IRBM 38:98–108
Christ PF, Ettlinger F, Grün F, Elshaera MEA, Lipkova J, Schlecht S et al (2017) Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks. arXiv preprint arXiv 1702.05970
Cui H, et al. (2019) Scalable deep hashing for large-scale social image retrieval. IEEE Transactions on image processing 29:1271–1284.
Dakua SP, Abinahed J, Al-Ansari AA (2016) Pathological liver segmentation using stochastic resonance and cellular automata. J Vis Commun Image Represent 34:89–102
El-Sayed MA, Hassaballah M, Abdel-Latif MA (2016) Identity verification of individuals based on retinal features using Gabor filters and SVM. J Signal Inf Process 7:49
Hoogi A, Beaulieu CF, Cunha GM, Heba E, Sirlin CB, Napel S, Rubin DL (2017) Adaptive local window for level set segmentation of CT and MRI liver lesions. Med Image Anal 37:46–55
Kumar S, Devapal D (2014) Survey on recent CAD system for liver disease diagnosis. In: 2014 international conference on control, instrumentation, communication and computational technologies (ICCICCT), pp 763–766
Lazaridis M, Axenopoulos A, Rafailidis D, Daras P (2013) Multimedia search and retrieval using multimodal annotation propagation and indexing techniques. Signal Process Image Commun 28:351–367
Li Z, Lu K, Zeng X, Pan X (2010) A blind steganalytic scheme based on DCT and spatial domain for JPEG images. J Multimed 5:200–207
Li G, Chen X, Shi F, Zhu W, Tian J, Xiang D (2015) Automatic liver segmentation based on shape constraints and deformable graph cut in CT images. IEEE Trans Image Process 24:5315–5329
Liao M, Zhao Y-q, Wang W, Zeng Y-z, Yang Q, Shih FY, Zou BJ (2016) Efficient liver segmentation in CT images based on graph cuts and bottleneck detection. Physica Medica 32:1383–1396
Liao M, Zhao Y-q, Liu X-y, Zeng Y-z, Zou B-j, Wang X-f, Shih FY (2017) Automatic liver segmentation from abdominal CT volumes using graph cuts and border marching. Comput Methods Prog Biomed 143:1–12
Lu X, Wu J, Ren X, Zhang B, Li Y (2014) The study and application of the improved region growing algorithm for liver segmentation. Optik-Int J Light Electron Optics 125:2142–2147
Sayed GI, Ali MA, Gaber T, Hassanien AE, Snasel V (2015) 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), pp 144–149
Schueller F, Roy S, Vucur M, Trautwein C, Luedde T, Roderburg C (2018) The role of miRNAs in the pathophysiology of liver diseases and toxicity. Int J Mol Sci 19:261
Selver MA, Fischer F, Gezer S, Hillen W, Dicle O (2014) Semi-automatic segmentation methods for 3-D visualization and analysis of the liver. In: MIE, pp 1133–1137
Sun C, Guo S, Zhang H, Li J, Chen M, Ma S et al (2017) Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs. Artif Intell Med
Wang YY, Wang ZE (2013) Difference curvature driven anisotropic diffusion for image denoising using Laplacian kernel. Appl Mech Mater 347–350:2412–2417
Wu W, Wu S, Zhou Z, Zhang R, Zhang Y (2017) 3D liver tumor segmentation in CT images using improved fuzzy C-means and graph cuts. BioMed Research International 2017:1
Xie L, Shen J, Zhu L (2016) Online cross-modal hashing for web image retrieval. In: Thirtieth AAAI conference on artificial intelligence
Xie L, Shen J, Han J, Zhu L, Shao L (2017) Dynamic multi-view hashing for online image retrieval
Xie L, He L, Shu H, Hu S (2018) Discrete semi-supervised multi-label learning for image classification. In: Pacific Rim conference on multimedia, pp 808–818
Xu Y, Xu C, Kuang X, Wang H, Chang EI, Huang W et al (2016) 3D-SIFT-flow for atlas-based CT liver image segmentation. Med Phys 43:2229–2241
Yang X, Yu HC, Choi Y, Lee W, Wang B, Yang J et al (2014) A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points. Comput Methods Prog Biomed 113:69–79
Yu S-P, Liang C, Xiao Q, Li G-H, Ding P-J, Luo J-W (2018) GLNMDA: a novel method for miRNA-disease association prediction based on global linear neighborhoods. RNA Biol 15:1215–1227
Zareei A, Karimi A (2016) Liver segmentation with new supervised method to create initial curve for active contour. Comput Biol Med 75:139–150
Zeng Y-z, Zhao Y-q, Tang P, Liao M, Liang Y-x, Liao S-h et al (2017) Liver vessel segmentation and identification based on oriented flux symmetry and graph cuts. Comput Methods Prog Biomed 150:31–39
Zhu L, Shen J, Xie L, Cheng Z (2016) Unsupervised topic hypergraph hashing for efficient mobile image retrieval. IEEE Trans Cybern 47:3941–3954
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Sakthisaravanan, B., Meenakshi, R. OPBS-SSHC: outline preservation based segmentation and search based hybrid classification techniques for liver tumor detection. Multimed Tools Appl 79, 22497–22523 (2020). https://doi.org/10.1007/s11042-019-08582-1
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DOI: https://doi.org/10.1007/s11042-019-08582-1