Abstract
Intracerebral hemorrhage (ICH) diagnosis is a neurological deficit that can occur in the patients suffering from high blood pressure and head trauma. Manual segmentation of ICH is tedious and operator dependent, therefore the purpose of this study is to present a robust fully automated system for hemorrhage detection from Computed Tomography (CT) scan images. The proposed method is based on White Matter Fuzzy c-Means (WMFCM) clustering and wavelet-based thresholding. The suggested method starts with the removal of components which do not look like brain tissues including skull by using a new WMFCM technique. After brain extraction, a new segmentation technique based on wavelet thresholding is used for detection and localization of hemorrhagic stroke. The proposed segmentation method is fast and accurate where standard evaluation metrics like dice similarity coefficients, Jaccard distance, Hausdorff distance, precision, recall, and F1 score are used to measure the accuracy of the proposed algorithm. Our method is demonstrated on a dataset of 20 brain computed tomography (CT) images suffered ICH and results obtained are compared with the ground truth of images. We found that our method can detect ICH with an average dice similarity of 0.82 and perform better as compared to standard fuzzy c-means (FCM) and spatial FCM (SFCM) clustering methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Impact of Stroke (Stroke statistics) (2017). http://www.strokeassociation.org/STROKEORG/AboutStroke/Impact-of-Stroke-Stroke-statistics_UCM_310728_Article.jsp
Banerjee, T.K., Das, S.K.: Fifty years of stroke researches in India. Ann. Indian Acad. Neurol. 19(1), 1–8 (2016)
Heit, J.J., Iv, M., Wintermark, M.: Imaging of intracranial hemorrhage. J. Stroke 19(1), 11 (2017)
Anbeek, P., IÅ¡gum, I., van Kooij, B.J., Mol, C.P., Kersbergen, K.J., Groenendaal, F., Viergever, M.A., de Vries, L.S., Benders, M.J.: Automatic segmentation of eight tissue classes in neonatal brain MRI. PLoS One 8(12), e81895 (2013)
Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)
Chuang, K.S., Tzeng, H.L., Chen, S., Wu, J., Chen, T.J.: Fuzzy c-means clustering with spatial information for image segmentation. Comput. Med. Imaging Graph. 30(1), 9–15 (2006)
Phillips, W.E., Velthuizen, R.P., Phuphanich, S., Hall, L.O., Clarke, L.P., Silbiger, M.L.: Application of fuzzy c-means segmentation technique for tissue differentiation in MR images of a hemorrhagic glioblastoma multiforme. Magn. Reson. Imaging 13(2), 277–290 (1995)
Liang, L., Korogi, Y., Sugahara, T., Shigematsu, Y., Okuda, T., Ikushima, I., Takahashi, M.: Detection of intracranial hemorrhage with susceptibility-weighted MR sequences. AJNR Am. J. Neuroradiol. 20(8), 1527–1534 (1999)
Liu, R., Tan, C.L., Leong, T.Y., Lee, C.K., Pang, B.C., Lim, C.T., Tian, Q., Tang, S., Zhang, Z.: Hemorrhage slices detection in brain ct images. In: Proceedings of the Nineteen International Conference on Pattern Recognition, pp. 1–4. IEEE (2008)
Loncaric, S., Dhawan, A.P., Broderick, J., Brott, T.: 3-D image analysis of intra-cerebral brain hemorrhage from digitized CT films. Comput. Methods Programs Biomed. 46(3), 207–216 (1995)
Cosic, D., Loucaric, S.: Computer system for quantitative: analysis of ICH from CT head images. In: Proceedings of the 19th Annual International Conference of Engineering in Medicine and Biology Society, pp. 553–556. IEEE (1997)
Chan, T.: Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Comput. Med. Imaging Gr. 31(4), 285–298 (2007)
Bardera, A., Boada, I., Feixas, M., Remollo, S., Blasco, G., Silva, Y., Pedraza, S.: Semi-automated method for brain hematoma and edema quantification using computed tomography. Comput. Med. Imaging Gr. 33(4), 304–311 (2009)
Bhadauria, H.S., Singh, A., Dewal, M.L.: An integrated method for hemorrhage segmentation from brain CT imaging. Comput. Electr. Eng. 39(5), 1527–1536 (2013)
Bhadauria, H.S., Dewal, M.L.: Intracranial hemorrhage detection using spatial fuzzy c-mean and region-based active contour on brain CT imaging. SIViP 8(2), 357–364 (2014)
Shahangian, B., Pourghassem, H.: Automatic brain hemorrhage segmentation and classification algorithm based on weighted grayscale histogram feature in a hierarchical classification structure. Biocybern. Biomed. Eng. 36(1), 217–232 (2016)
Li, C., Xu, C., Gui, C., Fox, M.D.: Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Image Process. 19(12), 3243–3254 (2010)
Muschelli, J., Sweeney, E.M., Ullman, N.L., Vespa, P., Hanley, D.F., Crainiceanu, C.M.: PItcHPERFeCT: primary intracranial hemorrhage probability estimation using random forests on CT. Neuroimage Clin. 14, 379–390 (2017)
Dunn, J.C.: A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybern. 3(3), 32–57 (1973)
Antonini, M., Barlaud, M., Mathieu, P., Daubechies, I.: Image coding using wavelet transform. IEEE Trans. Image Process. 1(2), 205–220 (1992)
Donoho, D.L.: De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41(3), 613–627 (1995)
Chang, S.G., Yu, B., Vetterli, M.: Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process. 9(9), 1532–1546 (2000)
Acknowledgements
We thank Dr. Shailendra Raghuwanshi, Head of Radiology Department, Himalayan Institute of Medical Sciences, Dehradun, Uttarakhand, India for providing hemorrhagic stroke CT image dataset.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gautam, A., Raman, B. (2019). Automatic Segmentation of Intracerebral Hemorrhage from Brain CT Images. In: Tanveer, M., Pachori, R. (eds) Machine Intelligence and Signal Analysis. Advances in Intelligent Systems and Computing, vol 748. Springer, Singapore. https://doi.org/10.1007/978-981-13-0923-6_64
Download citation
DOI: https://doi.org/10.1007/978-981-13-0923-6_64
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0922-9
Online ISBN: 978-981-13-0923-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)