Abstract
In this work, we propose a framework for the automatic and accurate segmentation of breast lesions from the Dynamic Contrast Enhanced (DCE) MRI. The framework is built using max flow and min cut problems in the continuous domain over phase preserved denoised images. The proposed method is achieved via three steps. First, post-contrast and pre-contrast images are subtracted, followed by image registrations that benefit to enhancing lesion areas. Second, a phase preserved denoising and pixel-wise adaptive Wiener filtering technique is used, followed by max flow and min cut problems in a continuous domain. A denoising mechanism clears the noise in the images by preserving useful and detailed features such as edges. Then, lesion detection is performed using continuous max flow. Finally, a morphological operation is used as a post-processing step to further delineate the obtained results. The efficiency of the proposed method is verified with a series of qualitative and quantitative experiments carried out on 21 cases with two different MR image resolutions. Performance results demonstrate the quality of segmentation obtained from the proposed method.
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References
Akram, M., Iqbal, M., Daniyal, M., Khan, A.U.: Awareness and current knowledge of breast cancer. Biol. Res. 50(1), 33 (2017)
Welch, H.G., Prorok, P.C., O’Malley, A.J., Kramer, B.S.: Breast-cancer tumor size, overdiagnosis, and mammography screening effectiveness. N. Engl. J. Med. 375(15), 1438–1447 (2016)
Abdel-Nasser, M., Melendez, J., Moreno, A., Omer, O.A., Puig, D.: Breast tumor classification in ultrasound images using texture analysis and super-resolution methods. Eng. Appl. Artif. Intell. 59, 84–92 (2017)
Cheng, J.Z., et al.: Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci. Rep. 6, 24454 (2016)
Zhang, J., Saha, A., Zhu, Z., Mazurowski, M.A.: Hierarchical convolutional neural networks for segmentation of breast tumors in MRI with application to radiogenomics. IEEE Trans. Med. Imaging 38(2), 435–447 (2019)
Wan, T., Cao, J., Chen, J., Qin, Z.: Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features. Neurocomputing 229, 34–44 (2017)
Pandey, D., Yin, X., Wang, H., Su, M.Y., Chen, J.H., Wu, J., et al.: Automatic and fast segmentation of breast region-of-interest (ROI) and density in MRIs. Heliyon. 4(12), e01042 (2018)
Bhattacharjee, R., et al.: Comparison of 2D and 3D U-net breast lesion segmentations on DCE-MRI, p. 10 (2021)
Khalil, F., Wang, H., Li, J.: Integrating Markov model with clustering for predicting web page accesses, pp. 63–74 (2007)
Hu, H., Li, J., Wang, H., Daggard, G.: Combined gene selection methods for microarray data analysis. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds.) KES 2006. LNCS (LNAI), vol. 4251, pp. 976–983. Springer, Heidelberg (2006). https://doi.org/10.1007/11892960_117
Li, H., Wang, Y., Wang, H., Zhou, B.: Multi-window based ensemble learning for classification of imbalanced streaming data. World Wide Web 20(6), 1507–1525 (2017). https://doi.org/10.1007/s11280-017-0449-x
Khalil, F., Li, J., Wang, H.: An integrated model for next page access prediction. Int. J. Knowl. Web Intell. 1, 48–80 (2009). https://doi.org/10.1504/IJKWI.2009.027925
Sarki, R., Ahmed, K., Wang, H., Zhang, Y., Ma, J., Wang, K.: Image preprocessing in classification and identification of diabetic eye diseases. Data Sci. Eng. (6), 1–17 (2021). https://doi.org/10.1007/s41019-021-00167-z
Bzdok, D., Krzywinski, M., Altman, N.: Points of significance: machine learning: supervised methods (2018)
Du, S., Zhang, F., Zhang, X.: Semantic classification of urban buildings combining VHR image and GIS data: an improved random forest approach. ISPRS J. Photogramm. Remote Sens. 105, 107–119 (2015)
Fabelo, H., et al.: SVM optimization for brain tumor identification using infrared spectroscopic samples. Sensors 18(12), 4487 (2018)
Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: Advances in Neural Information Processing Systems, pp. 5574–5584 (2017)
Conte, L., Tafuri, B., Portaluri, M., Galiano, A., Maggiulli, E., De Nunzio, G.: Breast cancer mass detection in DCE-MRI using deep-learning features followed by discrimination of infiltrative vs. in situ carcinoma through a machine-learning approach. Appl. Sci. 10(17), 6109 (2020)
Sarki, R., Ahmed, K., Wang, H., Zhang, Y.: Automated detection of mild and multi-class diabetic eye diseases using deep learning. Health Inf. Sci. Syst. 8(1), 1–9 (2020). https://doi.org/10.1007/s13755-020-00125-5
Du, J., Michalska, S., Subramani, S., Wang, H., Zhang, Y.: Neural attention with character embeddings for hay fever detection from twitter. Health Inf. Sci. Syst. 7(1), 1–7 (2019). https://doi.org/10.1007/s13755-019-0084-2
He, J., Rong, J., Sun, L., Wang, H., Zhang, Y., Ma, J.: A framework for cardiac arrhythmia detection from IoT-based ECGs. World Wide Web 23(5), 2835–2850 (2020). https://doi.org/10.1007/s11280-019-00776-9
Wang, K., et al.: Use of falls risk increasing drugs in residents at high and low falls risk in aged care services. J. Appl. Gerontol. 40, 073346481988884 (2019). https://doi.org/10.1177/0733464819888848
Wang, K., Bell, J., Tan, E., Gilmartin-Thomas, J., Dooley, M., Ilomaki, J.: Statin use and fall-related hospitalizations among residents of long-term care facilities: a case-control study. J. Clin. Lipidol. 14. (2020). https://doi.org/10.1016/j.jacl.2020.05.008
Aganj, I., Harisinghani, M.G., Weissleder, R., Fischl, B.: Unsupervised medical image segmentation based on the local center of mass. Sci. Rep. 8(1), 13012 (2018)
Ayaz, M., Shaukat, F., Raja, G.: Ensemble learning based automatic detection of tuberculosis in chest x-ray images using hybrid feature descriptors. Phys. Eng. Sci. Med. 44(1), 183–194 (2021). https://doi.org/10.1007/s13246-020-00966-0
Wadhwa, A., Bhardwaj, A., Verma, V.S.: A review on brain tumor segmentation of MRI images. Magn. Reson. Imaging 61, 247–259 (2019)
Siuly, S., Khare, S., Bajaj, V., Wang, H., Zhang, Y.: A computerized method for automatic detection of schizophrenia using EEG signals. IEEE Trans. Neural Syst. Rehabil. Eng. 1, 1 (2020)
Yuan, J., Bae, E., Tai, X.C.A.: Study on continuous max-flow and min-cut approaches. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2217–2224. IEEE (2010)
Jiang, H., Zhou, R., Zhang, L., Wang, H., Zhang, Y.: Sentence level topic models for associated topics extraction. World Wide Web 22(6), 2545–2560 (2018). https://doi.org/10.1007/s11280-018-0639-1
Supriya, S., Siuly, S., Wang, H., Zhang, Y.: Automated epilepsy detection techniques from electroencephalogram signals: a review study. Health Inf. Sci. Syst. 8(1), 1–15 (2020). https://doi.org/10.1007/s13755-020-00129-1
Villringer, K., et al.: Subtracted dynamic MR perfusion source images (sMRP-SI) provide collateral blood flow assessment in MCA occlusions and predict tissue fate. Eur. Radiol. 26(5), 1396–1403 (2016)
Hu, H., Li, J., Wang, H., Daggard, G.E., Shi, M.: A maximally diversified multiple decision tree algorithm for microarray data classification. Intell. Syst. Bioinform. 73, 35–38 (2006)
Khalil, F., Li, J., Wang, H.: Integrating Markov model with clustering for predicting web page accesses. In: AusWeb 2007: 13th Australasian World Wide Web Conference (2007)
Brock, K.K., Mutic, S., McNutt, T.R., Li, H., Kessler, M.L.: Use of image registration and fusion algorithms and techniques in radiotherapy: report of the AAPM radiation therapy committee task group no. 132. Med. Phys. 44(7), e43–e76 (2017)
Vaishali, S., Rao, K.K., Rao, G.S.: A review on noise reduction methods for brain MRI images. In: 2015 International Conference on Signal Processing and Communication Engineering Systems, pp. 363–365. IEEE (2015)
Pandey, D., Yin, X., Wang, H., Zhang, Y.: Accurate vessel segmentation using maximum entropy incorporating line detection and phase-preserving denoising. Comput. Vis. Image Underst. 155, 162–172 (2017)
Hou, G., Pan, H., Zhao, R., Hao, Z., Liu, W.: Image segmentation via the continuous max-flow method based on Chan-Vese model. In: Wang, Y., et al. (eds.) IGTA 2017. CCIS, vol. 757, pp. 232–242. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7389-2_23
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Pandey, D., Wang, H., Yin, X., Wang, K., Zhang, Y., Shen, J. (2021). Automatic Breast Lesion Segmentation Using Continuous Max-Flow Algorithm in Phase Preserved DCE-MRIs. In: Siuly, S., Wang, H., Chen, L., Guo, Y., Xing, C. (eds) Health Information Science. HIS 2021. Lecture Notes in Computer Science(), vol 13079. Springer, Cham. https://doi.org/10.1007/978-3-030-90885-0_12
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