Document Type : Review Article

Authors

1 Department of Biomedical Engineering, Meybod University, Meybod, Iran.

2 Islamic Azad University, Torbat-e Jam Branch, Torbat, Iran.

3 Department of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran.

Abstract

Segmentation is a fundamental element in medical image processing (MIP) and has been extensively researched and developed to aid in clinical interpretation and utilization. This article discusses a method for segmenting abnormal masses or tumors in medical images that is both robust and effective. We suggested a method based on Active Contour (AC) and modified Level-set techniques to detect malignancies in magnetic resonance imaging (MRI), mammography, and computed tomography (CT). To segment malignant masses, the active contour approach, the energy function, the level-set method, and the proposed F function are employed. The system was evaluated using 160 medical images from two databases, including 80 mammograms and 80 MRI brain scans. The algorithm for segmenting suspicious segments has an accuracy, recall, and precision of 96.25%, 95.60%, and 95.71%, respectively. By adding this technique into tissue imaging devices, the accuracy of diagnosing images with a relatively large volume that are evaluated fast is increased. Cost savings, time savings, and high precision are all advantages of the approach that set it apart from similar systems.

Keywords

[1] A. Rezaee, K. Rezaee, J. Haddadnia and H. T. Gorji, “Supervised metaheuristic extreme learning machine for multiple sclerosis detection based on multiple feature descriptors in mr images”, SN Applied Sciences, vol. 2, pp. 1-19, 2020.
[2] F. Ozdemir, Z. Peng, P. Fuernstahl, C. Tanner, O. Goksel, “Active learning for segmentation based on Bayesian sample queries”, Knowledge-Based Systems, vol. 214, 106531, 2021.
[3] K. Rezaee, A. Badiei, S. Meshgini, “A hybrid deep transfer learning based approach for COVID-19 classification in chest X-ray images”, In 2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME), pp. 234-241, 2020.
[4] K. Rezaee, A. Rezaee, N. Shaikhi, J. Haddadnia, “Multi-mass breast cancer classification based on hybrid descriptors and memetic meta-heuristic learning”, SN Applied Sciences, vol. 2, no. 7, pp. 1-19, 2020.
[5] T. Kim, and et al., “Active learning for accuracy enhancement of semantic segmentation with CNN-corrected label curations: Evaluation on kidney segmentation in abdominal CT”, Scientific reports, vol. 10, no. 1, pp. 1-7, 2020.
[6] K. Rezaee, and et al, “Multi-mass breast cancer classification based on hybrid descriptors and memetic meta-heuristic learning”, SN Applied Sciences 2, no. 7, pp. 1-19, 2020
[7] K. Rezaee and J. Haddadnia, “Designing an Algorithm for Cancerous Tissue Segmentation Using Adaptive K-means Cluttering and Discrete Wavelet Transform”, J Biomed Phys Eng., vol. 3, no. 3, pp. 93-104, Sep 2013.
[8] Y. Shi, M. Li, W. Zeng, “MARGM: A multi-subjects adaptive region growing method for group fMRI data analysis”, Biomedical Signal Processing and Control, vol. 69, pp. 102882, 2021.
[9] T. Wu, Z. Yang, “Animal tumor medical image analysis based on image processing techniques and embedded system”, Microprocessors and Microsystems, vol. 81, 103671, 2021.
[10] Y. Alzahrani, Y., B. Boufama, “Biomedical Image Segmentation: A Survey”, SN Computer Science, vol. 2, no. 4, pp. 1-22, 2021.
[11] F. H. Araújo, R. R. Silva, F. N. Medeiros, J. F. R. Neto, P. H. C. Oliveira, A. G. C. Bianchi, D. Ushizima, “Active contours for overlapping cervical cell segmentation” International Journal of Biomedical Engineering and Technology 35, no. 1, pp. 70-92, 2021.
[12] K. Bi, Y. Tan, K. Cheng, Q. Chen, Y. Wang, Y, “Sequential shape similarity for active contour based left ventricle segmentation in cardiac cine MR image”, Mathematical Biosciences and Engineering, vol. 19, no. 2, pp. 1591-1608, 2022.
[13] G. Liu, G., and et al., “Superpixel-based active contour model via a local similarity factor and saliency”, Measurement, no. 110442, 2021
[14] R. Zhang, M. You, “Fast contour detection with supervised attention learning”, Journal of Real-Time Image Processing, vol. 18, no. 3, pp. 647-657, 2021.
[15] Y. Lei, and G. Weng, “A robust hybrid active contour model based on pre-fitting bias field correction for fast image segmentation”, Signal Processing: Image Communication, no. 116351, 2021.
[16] B. D. M. Zhang and Q. Li, “Deep active contour network for medical image segmentation”, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, 2020.
[17] Y. Yang, R. Wang, H. Ren, “Active contour model based on local intensity fitting and atlas correcting information for medical image segmentation” Multimedia Tools and Applications, pp. 1-17, 2021.
[18] S. Husham, A. Mustapha, S. A. Mostafa, M. K. Al-Obaidi, M. A. Mohammed, A. I. Abdulmaged, et al., “Comparative analysis between active contour and otsu thresholding segmentation algorithms in segmenting brain tumor magnetic resonance imaging”, J. Inf. Technol. Manage., vol. 12, pp. 48-61, Dec. 2020.
[19] M. Sharif, U. Tanvir, E. U. Munir, M. A. Khan and M. Yasmin, “Brain tumor segmentation and classification by improved binomial thresholding and multi-features selection”, Journal of Ambient Intelligence and Humanized Computing, pp. 1-20, 2018.
[20] J.A. Sethian, “Evolution, Implementation, and Application of Level Set and Fast Marching Methods for Advancing Fronts”, Journal of Computational Physics vol. 169, pp. 503–555, 2001.
[21] D. Adalsteinsson, J.A. Sethian, “A Fast Level Set Method for Propagating Interfaces”, J.Comp.Phys. pp.269~277, 1995.
[22] S. Osher, R. Fedkiw, “Level Set Methods and Dynamic Implicit Surface”, Springer‌Verlag, 2002.
[23] D Terzopoulos, D Metaxas. “Dynamic 3D Models with Local and Global Deformations: Deformable Superquadrics.” IEEE Transactions on Pattern Analysis and Machine Intelligence vol. 13, no. 7, pp.703-715, 1999.
[24] J. Weickert and G. Kuhne, “Fast methods for implicit active contour models”, in Geometric Level Set Methods in Imaging Vision and Graphics, New York:Springer-Verlag, 2003.
[25] M. Holtzman-Gazit, D. Goldshe, and R Kimmel. “Hierarchical segmentation of thin structure in volumetric medical images”. In: Medical image computing and computer-assisted intervention (MICCAI), Montreal; 2003.
[26] C. Rother, V. Kolmogorov, and A. Blake, “GrabCut: interactive foreground extraction using iterated graph cuts”, ACM Transactions on Graphics (TOG), vol.23 no.3, August 2004.
[27] C. Pluempitiwiriyawej, JMF. Moura, Yi-Jen Lin Wu and Chien Ho. “STACS: New Active Contour Scheme for Cardiac MR Image Segmentation”, IEEE Transactions on Medical Imaging, vol. 24, no. 5, pp 593-602, May 2005.
[28] Y. Boykov and G. Funka-Lea, “Graph Cuts and Efficient N-D Image Segmentation”. In International Journal of Computer Vision (IJCV), vol. 70, no. 2, pp. 109-131, 2006.
[29] Herbulot, S. Jehan-Besson, S. Duffner, M. Barlaud, and G. Aubert. “Segmentation of vectorial image features using shape gradients and information measures”. Journal of Mathematical Imaging and Vision, 25(3):365–386, October 2006.
[30] C.M. Li, C. Kao, J. Gore, Z. Ding, “Implicit active contours driven by local binary fitting energy”, IEEE Conference on Computer Vision and Pattern Recognition, 2007.
[31] K. Ni, X. Bresson, T. Chan, and S. Esedoglu. “Local histogram based segmentation using the wasserstein distance”. International Journal of Computer Vision, vol. 84, pp. 97–111, August 2009.
[32] N. Le, T. Bui, V. K. Vo-Ho, K. Yamazaki, K. Luu, “Narrow Band Active Contour Attention Model for Medical Segmentation”, Diagnostics, vol. 11, no. 8, pp. 1393, 2021.
[33] K. Rezaee, S. M. Rezakhani, M. R. Khosravi, M. K. Moghimi, “A survey on deep learning-based real-time crowd anomaly detection for secure distributed video surveillance”, Personal and Ubiquitous Computing, pp. 1-17, 2021.
[34] K. Rezaee, S. Savarkar, X. Yu, J Zhang, “A hybrid deep transfer learning-based approach for Parkinson's disease classification in surface electromyography signals”, Biomedical Signal Processing and Control, vol. 71, pp. 103161, 2022.
[35] X. Chen, B. M. Williams, S. R. Vallabhaneni, G. Czanner, R. Williams and Y. Zheng, “Learning active contour models for medical image segmentation”, Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 11632-11640, Jun. 2019.
[36] S. Gur, L. Wolf, L. Golgher and P. Blinder, “Unsupervised microvascular image segmentation using an active contours mimicking neural network”, Proc. IEEE Int. Conf. Comput. Vision, pp. 10 722-10 731, 2019.
[37] B. Kim and J. C. Ye, “Mumford–shah loss functional for image segmentation with deep learning”, IEEE Trans. Image Process., vol. 29, pp. 1856-1866, 2020.
[38] Y. Kim, S. Kim, T. Kim and C. Kim, “CNN-based semantic segmentation using level set loss”, Proc. IEEE Winter Conf. Appl. Comput. Vis. (WACV), pp. 1752-1760, Jan. 2019.
[39] P. Hu, B. Shuai, J. Liu and G. Wang, “Deep level sets for salient object detection”, Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 540-549, 2017.
[40] D. Marcos, D. Tuia, B. Kellenberger, L. Zhang, M. Bai, R. Liao, et al., “Learning deep structured active contours end-to-end”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8877-8885, 2018.
[41] A. Hatamizadeh, A. Hoogi, D. Sengupta, W. Lu, B. Wilcox, D. Rubin, et al., “Deep active lesion segmentation”, International Workshop on Machine Learning in Medical Imaging (MLMI)., pp. 98-105, 2019.
[42] E. E. Nithila and S. S. Kumar, “Segmentation of lung from CT using various active contour models”, Biomed. Signal Process. Control, vol. 47, pp. 57-62, 2019.
[43] J. Suckling, J. Parker, D. R. Dance, S. Astley, I. Hutt, C. R. M. Boggis, et al., “The mammographic image analysis society digital mammogram database”, in Proc. 2nd Int. Workshop Digit. Mammography, U.K., York, pp. 375-378, Jul. 1994.
[44] https://www.med.harvard.edu/aanlib/home.html.
[45] L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation”, Proc. ECCV, pp. 801-818, Sep. 2018.
[46] P. Kohli, P. H. Torr and L. Ladick, “Robust higher order potentials for enforcing label consistency”, Int. J. Comput. Vis., vol. 82, no. 3, pp. 302-324, 2009.
[47] Y. Yang, X. Hou, H. Ren, “Efficient active contour model for medical image segmentation and correction based on edge and region information”. Expert Systems with Applications, pp. 116436, 2022.