Leveraging Deep Learning and Grab Cut for Automatic Segmentation of White Blood Cell Images

Abstract:

White blood cell image segmentation provides the opportunity for medical experts to objectively diagnose the medical conditions of patients suffering from Leukemia, for example. Due to the rigorous nature of cell image acquisition (staining process and non-uniform illumination) efficient tools must be deployed to achieve the desired segmentation result. In this paper, a deep learning model is proposed together with a grab cut. The developed deep learning model provides an initial coarse segmentation of white blood cell images. However, the objective of this segmentation is to localize or identify regions of interest from white blood cell images. A bounding is generated from the localized cell image and then used to initiate an automatic cell image segmentation using grab cut. Results of the two publicly available datasets of white blood cell images are considered satisfactory on the proposed model.

• Periodical: Journal of Biomimetics, Biomaterials and Biomedical Engineering (Volume 58)
• Pages: 121-128
• DOI: https://doi.org/10.4028/p-oj4d78
• Online since: August 2022
• Authors: Kazeem Oyeyemi Oyebode*
• Keywords: Deep Learning, Grab Cut, Graph Cut, Image Segmentation, Medical Imaging

References

[1] Y. Boykov, J. Marie-Pierre, Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images. International Conference on Computer Vision, Vancouer, (2001).

DOI: 10.1109/iccv.2001.937505

[2] Y. Boykov , V. Kolmogorov, An experimental comparison of min-cut/max flow algorithms for energy minimization in vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, 26, 9 (2004) 1124-1137.

DOI: 10.1109/tpami.2004.60

[3] R. Carsten, B. Andrew, GrabCut: Interactive Foreground Extraction Using Iterated Graph Cuts, in ACM Transactions on Graphics, (2004).

DOI: 10.1145/1015706.1015720

[4] K. Oyebode, J. Tapamo, Adaptive parameter selection for graph cut-based segmentation on cell images, Image Analysis and Stereology,1,30 (2016) 29-37.

DOI: 10.5566/ias.1333

[5] M. Beheshti, F. Jolon, G. Amin, Bio-Cell Image Segmentation using Bayes Graph- Cut Model, in The International Conference on Digital Image Computing: Techniques and Applications (DICTA), (2015).

DOI: 10.1109/dicta.2015.7371241

[6] W. Cai, L. Lei, M. Yang, A Gaussian Mixture Model-based clustering algorithm for image segmentation using dependable spatial constraints, in 3rd International Congress on Image and Signal Processing, Yantai, (2010).

DOI: 10.1109/cisp.2010.5647653

[7] Y. Al-Kofahi, W. Lassoued, W. Lee., B. Roysam, Improved automatic detection and segmentation of cell nuclei in histopathology images, IEEE Transactions on Biomedical Engineering,57, 4 (2010) 841 - 852.

DOI: 10.1109/tbme.2009.2035102

[8] G. Narjes, V Alireza, T Ardeshir, N. Pardis, Segmentation of White Blood Cells From Microscopic Images Using a Novel Combination of K-Means Clustering and Modified Watershed Algorithm, Journal of Medical Signals and Sensors, 7, 2 (2017) 92–101.

DOI: 10.4103/2228-7477.205503

[9] K. Oyebode, J.R. Tapamo, Automatic segmentation of cell images by improved graph cut-based approach, Journal of Biomimetics, Biomaterials and Biomedical Engineering, 29, (2016) 4-80.

DOI: 10.4028/www.scientific.net/jbbbe.29.74

[10] K. Oyebode, Improved Thresholding Method for Cell Image Segmentation Based on Global Homogeneity Information, Journal of Telecommunication, Electronic and Computer Engineering, 10,1 (2018) 13-16.

[11] M. Merone, P. Soda, On Using Active Contour to Segment HEp-2 Cells, in IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS), (2016).

DOI: 10.1109/cbms.2016.49

[12] T. Chan, L. Vese, An Active Contour Model without Edges, in Scale-Space Theories in Computer Vision, Greece, (1999).

DOI: 10.1007/3-540-48236-9_13

[13] Z. Xin, W. Yong, W. Guoyou, L. Jianguo, Fast and robust segmentation of white blood cell images by self-supervised learning, Micron,107, (2018) 55-7.

DOI: 10.1016/j.micron.2018.01.010

[14] R. Olaf, F. Philipp, B. Thomas, U-Net: Convolutional Networks for Biomedical Image Segmentation, Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, LNCS, 9351,(2015) 234-24.

DOI: 10.1007/978-3-319-24574-4_28

[15] Y. Al-Kofahi, Z. Alla, G. Robert, M. Will, R. Mirabela, A deep learning-based algorithm for 2-D cell segmentation in microscopy images, BMC Bioinformatics, 19, 365 (2018).

DOI: 10.1186/s12859-018-2375-z

[16] Z. Zeng, W. Xie, Y. Zhang, Y. Lu, RIC-Unet: An Improved Neural Network Based on Unet for Nuclei Segmentation in Histology Images, IEEE Access, 7, (2019) 21420-21428.

DOI: 10.1109/access.2019.2896920

[17] J. Leng, Y. Liu, T. Zhang, P. Quan, Z. Cui,Context-Aware U-Net for Biomedical Image Segmentation, in 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain, (2018).

DOI: 10.1109/bibm.2018.8621512

[18] L. Shengrong, Y. Changchun, S. Hui, Z Hao, Seismic fault detection using an encoder–decoder convolutional neural network with a small training set, Journal of Geophysics and Engineering, 16, 1, (2019) 175-189.

DOI: 10.1093/jge/gxy015

[19] I. V. Saetchnikov, E. A. Tcherniavskaia and V. V. Skakun, Object Detection for Unmanned Aerial Vehicle Camera via Convolutional Neural Networks, IEEE Journal on Miniaturization for Air and Space Systems,2, 2, (2021) 98-103.

DOI: 10.1109/jmass.2020.3040976

[20] K. L. de Jong and A. Sergeevna Bosman, Unsupervised Change Detection in Satellite Images Using Convolutional Neural Networks,, 2019 International Joint Conference on Neural Networks (IJCNN) Budapest, Hungary (2019).

DOI: 10.1109/ijcnn.2019.8851762