A Review of Research on Accurate Segmentation of Multimodal Tumor Images
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Keywords

Multimodal
Image segmentation
Tumor image

DOI

10.26689/jera.v8i6.9022

Submitted : 2024-11-03
Accepted : 2024-11-18
Published : 2024-12-03

Abstract

Accurate segmentation of tumor images is a key core technology for the diagnosis and treatment of tumor diseases. In this paper, we analyze a variety of novel and targeted algorithms to solve these problems, summarize, and elaborate the method based on multimodal tumor image processing given the characteristics of serious grayscale inhomogeneity, texture instability, and diversity complexity of tumor images.

References

Lambin P, Rios-Velazquez E, Leijenaar R, et al., 2012, Radiomics: Extracting More Information from Medical Images using Advanced Feature Analysis. European Journal of Cancer, 48(4): 441–446.

Siegel R, Naishadham D, Jemal A, 2013, Cancer Statistics. CA: A Cancer Journal for Clinicians, 63(1): 11–30.

Darolti C, Mertins A, Bodensteiner C, et al., 2008, Local Region Descriptors for Active Contours Evolution. IEEE Transactions on Image Processing, 17: 2275–2288.

Gao Y, Bouix S, Shenton ME, et al., 2013, Sparse Texture Active Contour. IEEE Transactions on Image Processing, 22(10): 3866–3878.

Ahmed S, Iftekharuddin KM, Vossough A, et al., 2011, Efficacy of Texture, Shape, and Intensity Feature Fusion for Posterior-Fossa Tumor Segmentation in MRI. IEEE Transactions on Information Technology in Biomedicine, 15(2): 206–213.

Liu Y, Sadowski SM, Weisbrod AB, et al., 2014, Patient-Specific Tumor Growth Prediction using Multimodal Images. Medical Image Analysis, 18(3): 555–566.

Parisot S, Wells W, Chemouny S, et al., 2014, Concurrent Tumor Segmentation and Registration with Uncertainty-Based Sparse Non-Uniform Graphs. Medical Image Analysis, 18(4): 647–659.

National Research Council, 2011, Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease.

Doyle S, Vasseur F, Dojat M, et al., 2013, Fully Automatic Brain Tumor Segmentation from Multiple MR Sequences using Hidden Markov Fields and Variational EM. Proceedings of MICCAI-BRAST, September 22–25, 2013.

Guo X, Schwartz L, Zhao B, 2013, Semi-Automatic Segmentation of Multimodal Brain Tumor Using Active Contours. Proceedings of MICCAI-BRAST, September 22–25, 2013.

Iftekharuddin KM, Zheng J, Islam MA, et al., 2009, Fractal-Based Brain Tumor Detection in Multimodal MRI. Applied Mathematics and Computation, 207(1): 23–41.

Li C, Kao C, Gore JC, et al., 2008, Minimization of Region-Scalable Fitting Energy for Image Segmentation. IEEE Transactions on Image Processing, 17(10): 1940–1949.

Li C, Huang R, Ding Z, et al., 2011, A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities with Application to MRI. IEEE Transactions on Image Processing, 20(7): 2007–2016.

Li CY, Wang X, Eberl S, et al., 2013, Robust Model for Segmenting Images With/Without Intensity Inhomogeneities. IEEE Transactions on Image Processing, 22(8): 3296–3309.

Zhang H, Ye X, Chen Y, 2013, An Efficient Algorithm for Multiphase Image Segmentation with Intensity Bias Correction. IEEE Transactions on Image Processing, 2(10): 3842–3851.

Wang W, Hua M, 2013, Extracting Dominant Textures in Real Time With Multi-Scale Hue-Saturation-Intensity Histograms. IEEE Transactions on Image Processing, 22(11): 4237–4248.

Chan TF, Vese LA, 2001, Active Contours without Edges. IEEE Transactions on Image Processing, 10(2): 266–277.

Lankton S, Tannenbaum A,2008, Localizing Region-Based Active Contours. IEEE Transactions on Image Processing, 17: 2029–2039.

Wang B, Gao XB, et al., 2010, A Unified Tensor Level Set for Image Segmentation. IEEE Trans. Syst. Man Cybern. PartB: Cybern., 40(3): 857–867.