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Abnormalities detection in serial computed tomography brain images using multi-level segmentation approach

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Abstract

Segmentation, where pixels are categorized by tissue types, is essential in medical image processing. This paper proposes a multi-level Fuzzy C-Means method to extract an intracranial from its background and skull. Then, a two-level Otsu multi-thresholding method is applied to segment the intracranial structure into cerebrospinal fluid, brain matters and other homogenous regions. Based on symmetrical properties in the intracranial structures, the left-half and right-half segmented intracranial regions are quantitatively compared with respect to the intracranial midline. The segmented regions are found to be very useful in providing information regarding normal and abnormal structures in the intracranial because any asymmetry that is detected would indicate a high probability of abnormalities. Additionally, pixel intensity information such as standard deviation and the maximum value of the pixels of the segmented regions are used to distinguish abnormalities such as bleeding and calcification from normal cases. This experimental work uses a medical image database consisting of 519 normal and 201 abnormal serial computed tomography (CT) brain images from 31 patients. The proposed multi-level segmentation approach proved to effectively isolate important homogenous regions in CT brain images. The extracted features of the regions would provide a strong basis for the application of content-based medical image retrieval (CMBIR).

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Notes

  1. In another work, the terms “hemorrhage” and “hematoma” are used interchangeably [3]. Thus, in this paper, we have considered both terms to be bleeding. According to MedicineNet.com, a trusted source for online health and medical information, hemorrhage is defined as bleeding or the abnormal flow of blood, whereas hematoma is defined as abnormal localized collection of blood in which the blood is usually clotted or partially clotted and is usually situated within an organ or a soft tissue space, such as within a muscle [21].

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Acknowledgements

The authors are grateful to Faculty of Engineering, Multimedia University, Malaysia. We are also grateful to the MOSTI for their support under grant number 01-02-01-SF0014 (the eScienceFund Project), and to our collaborator, the Imaging Department of Hospital Putrajaya, Malaysia, which provides the CT brain images used in this work. Besides, we also would like to thank Dr. Fatimah Othman for her guidance.

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Authors and Affiliations

  1. Department of Electric, Electronic and Systems, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    W. Mimi Diyana W. Zaki

  2. Faculty of Engineering, Multimedia University, 63100, Cyberjaya, Selangor, Malaysia

    M. Faizal A. Fauzi & W. Siti Haimatul Munirah W. Ahmad

  3. Faculty of Engineering and Technology, Multimedia University, 75450, Melaka, Malaysia

    Rosli Besar

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  1. W. Mimi Diyana W. Zaki
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  2. M. Faizal A. Fauzi
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  4. W. Siti Haimatul Munirah W. Ahmad
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Correspondence to W. Mimi Diyana W. Zaki.

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Zaki, W.M.D.W., Fauzi, M.F.A., Besar, R. et al. Abnormalities detection in serial computed tomography brain images using multi-level segmentation approach. Multimed Tools Appl 54, 321–340 (2011). https://doi.org/10.1007/s11042-010-0524-0

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