The week's pick
Random Articles
Reseach Article
Semi-automated Classification of CT Scans in Traumatic Brain Injury Patients
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 113 - Number 9 |
| Year of Publication: 2015 |
| Authors: Adnan N. Qureshi |
10.5120/19851-1765
|
Adnan N. Qureshi . Semi-automated Classification of CT Scans in Traumatic Brain Injury Patients. International Journal of Computer Applications. 113, 9 ( March 2015), 1-8. DOI=10.5120/19851-1765
Abstract
A 'silent epidemic' affecting millions worldwide every year is the Traumatic Brain Injury. Management of these patients essentially involves neuroimaging and noncontrast Computed Tomography (CT) scans are the first choice amongst doctors. However, interobserver variability, considered 'Achilles heel' amongst radiologists, can lead to missed diagnoses and grave consequences. This paper presents a hybrid approach for semi-automated classification of CT features according to Marshall CT Scheme. The proposed method uses template matching, artificial neural networks and active contours for segmentation of significant anatomical landmarks and estimation of haematoma volume on brain CT scans. The proposed method is efficient and robust in segmenting cross-sectional, noncontrast CT scans and has been evaluated on images from subjects with different ages and both genders. The hybrid method has an average ICC 0:97 and Jaccard Index 0:86 compared with the manual demarcations by radiology experts and performs better than the state of the art. Hence, the approach can be used to provide second opinions very close to the experts' intuition.
References
- Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine S¨usstrunk. Slic superpixels. Technical report, 2010.
- Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Susstrunk. Slic superpixels compared to state-of-the-art superpixel methods. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(11):2274–2282, 2012.
- HS Bhadauria, Annapurna Singh, and ML Dewal. An integrated method for hemorrhage segmentation from brain ct imaging. Computers & Electrical Engineering, 39(5):1527– 1536, 2013.
- Tony F Chan and Luminita A Vese. Active contours without edges. Image Processing, IEEE Transactions on, 10(2):266– 277, 2001.
- Anastasia Dubrovina and Ron Kimmel. Active contours for multi-region image segmentation with a single level set function. Technical report, Technion technical report CIS-2012- 06, 2012.
- Murray Goldstein. Traumatic brain injury: a silent epidemic. Annals of neurology, 27(3):327–327, 1990.
- Igor Kononenko. Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in medicine, 23(1):89–109, 2001.
- Rashmi U Kothari, Thomas Brott, Joseph P Broderick, William G Barsan, Laura R Sauerbeck, Mario Zuccarello, and Jane Khoury. The abcs of measuring intracerebral hemorrhage volumes. Stroke, 27(8):1304–1305, 1996.
- David Leonhardt. Why doctors so often get it wrong. The New York Times, 2006.
- Lawrence F Marshall, Sharon Bowers Marshall, Melville R Klauber, Marjan van Berkum Clark, Howard M Eisenberg, John A Jane, Thomas G Luerssen, Anthony Marmarou, and Mary A Foulkes. A new classification of head injury based on computerized tomography. Special Supplements, 75(1S):S14–S20, 1991.
- CA McLauchlan, K Jones, and HR Guly. Interpretation of trauma radiographs by junior doctors in accident and emergency departments: a cause for concern? Journal of accident & emergency medicine, 14(5):295–298, 1997.
- KN Bhanu Prakash, Shi Zhou, Tim C Morgan, Daniel F Hanley, and Wieslaw L Nowinski. Segmentation and quantification of intra-ventricular/cerebral hemorrhage in ct scans by modified distance regularized level set evolution technique. International journal of computer assisted radiology and surgery, 7(5):785–798, 2012.
- Adnan N Qureshi. A framework for segmentation and estimation of intracranial measurements in ct scans. In Biomedical Engineering Conference (CIBEC), 2014 Cairo International, pages 129–132. IEEE, 2014.
- Adnan N. Qureshi and Vitaly Schetinin. Computer-aided segmentation and estimation of indices in brain CT scans. In Medical Image Understanding and Analysis - MIUA 2014. 18th Annual Conference, Egham, UK, July 9-11, 2014. Proceedings. , pages 161–166, 2014.
- Saatman, Duhaime, Bullock, Maas, Valadka, Manley, Workshop Scientific Team, and Advisory Panel Members. Classification of Traumatic Brain Injury for Targeted Therapies. Journal of Neurotrauma, 25:719–738, 2008.
- Mohammadreza Soltaninejad, Tryphon Lambrou, Adnan N. Qureshi, Nigel M. Allinson, and Xujiong Ye. A hybrid method for haemorrhage segmentation in trauma brain CT. In Medical Image Understanding and Analysis - MIUA 2014. 18th Annual Conference, Egham, UK, July 9-11, 2014. Proceedings. , pages 99–104, 2014.
- Hilaire J Thompson, Wayne C McCormick, and Sarah H Kagan. Traumatic brain injury in older adults: epidemiology, outcomes, and future implications. Journal of the American Geriatrics Society, 54(10):1590–1595, 2006.
- Sreenath Rao Vantaram and Eli Saber. Survey of contemporary trends in color image segmentation. Journal of Electronic Imaging, 21(4):040901–1, 2012.
- Li Wang, Chunming Li, Quansen Sun, Deshen Xia, Chiu- Yen Kao, et al. Active contours driven by local and global intensity fitting energy with application to brain mr image segmentation. Computerized Medical Imaging and Graphics, 33(7):520, 2009.
- Anthony Yezzi Jr, Satyanad Kichenassamy, Arun Kumar, Peter Olver, and Allen Tannenbaum. A geometric snake model for segmentation of medical imagery. Medical Imaging, IEEE Transactions on, 16(2):199–209, 1997.
Index Terms
Keywords