Semi-automated method for brain hematoma and edema quantification using computed tomography

In this paper, a semi-automated method for brain hematoma and edema segmentation, and volume measurement using computed tomography imaging is presented. This method combines a region growing approach to segment the hematoma and a level set segmentation technique to segment the edema. The main novelty of this method is the strategy applied to define the propagation function required by the level set approach. To evaluate the method, 18 patients with brain hematoma and edema of different size, shape and location were selected. The obtained results demonstrate that the proposed approach provides objective and reproducible segmentations that are similar to the manually obtained results. Moreover, the processing time of the proposed method is about 4 min compared to the 10 min required for manual segmentation.

[1]  Alan L. Yuille,et al.  Multilevel Segmentation and Integrated Bayesian Model Classification with an Application to Brain Tumor Segmentation , 2006, MICCAI.

[2]  V. Caselles,et al.  A geometric model for active contours in image processing , 1993 .

[3]  Anthony J. Yezzi,et al.  A geometric snake model for segmentation of medical imagery , 1997, IEEE Transactions on Medical Imaging.

[4]  Xavier Cufí,et al.  Yet Another Survey on Image Segmentation: Region and Boundary Information Integration , 2002, ECCV.

[5]  J. Broderick,et al.  Volume of Intracerebral Hemorrhage: A Powerful and Easy‐to‐Use Predictor of 30‐Day Mortality , 1993, Stroke.

[6]  Joseph P Broderick,et al.  Natural History of Perihematomal Edema in Patients With Hyperacute Spontaneous Intracerebral Hemorrhage , 2002, Stroke.

[7]  Jerry L. Prince,et al.  A Survey of Current Methods in Medical Image Segmentation , 1999 .

[8]  Yoshiyasu Takefuji,et al.  Optimization neural networks for the segmentation of magnetic resonance images , 1992, IEEE Trans. Medical Imaging.

[9]  S. Mayer,et al.  Recombinant Activated Factor VII for Acute Intracerebral Hemorrhage , 2007, Stroke.

[10]  S J Phillips,et al.  Incidence rates of stroke in the eighties: the end of the decline in stroke? , 1989, Stroke.

[11]  James A. Sethian,et al.  A unified approach to noise removal, image enhancement, and shape recovery , 1996, IEEE Trans. Image Process..

[12]  E. Jauch,et al.  Critical pathways for the management of stroke and intracerebral hemorrhage: a survey of US hospitals. , 2007, Critical pathways in cardiology.

[13]  Dubravko Ćosić,et al.  Computer system for quantitative: analysis of ICH from CT head images , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[14]  Joan Serra,et al.  Image segmentation , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[15]  T Brott,et al.  Intracerebral hemorrhage more than twice as common as subarachnoid hemorrhage. , 1993, Journal of neurosurgery.

[16]  Richard L. Van Metter,et al.  Handbook of Medical Imaging , 2009 .

[17]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..

[18]  Johannes Wikner,et al.  Comparison of ABC/2 estimation technique to computer-assisted planimetric analysis in warfarin-related intracerebral parenchymal hemorrhage. , 2006, Stroke.

[19]  A David Mendelow,et al.  Early surgery versus initial conservative treatment in patients with spontaneous supratentorial intracerebral haematomas in the International Surgical Trial in Intracerebral Haemorrhage (STICH): a randomised trial , 2005, The Lancet.

[20]  Steven W. Zucker,et al.  Region growing: Childhood and adolescence* , 1976 .

[21]  Xavier Bresson,et al.  A level set method for segmentation of the thalamus and its nuclei in DT-MRI , 2007, Signal Process..

[22]  S. Loncaric,et al.  Fuzzy expert system for edema segmentation , 1998, MELECON '98. 9th Mediterranean Electrotechnical Conference. Proceedings (Cat. No.98CH36056).

[23]  J. Broderick,et al.  Relative Edema Volume Is a Predictor of Outcome in Patients With Hyperacute Spontaneous Intracerebral Hemorrhage , 2002, Stroke.

[24]  Tuan-Kay Lim,et al.  Nasopharyngeal carcinoma tumor volume measurement.. , 2004, Radiology.

[25]  Jerry L. Prince,et al.  An Adaptive Fuzzy Segmentation Algorithm for Three-Dimensional Magnetic Resonance Images , 1999, IPMI.

[26]  S. Mayer,et al.  Efficacy and safety of recombinant activated factor VII for acute intracerebral hemorrhage. , 2008, The New England journal of medicine.

[27]  Guido Gerig,et al.  Level-set evolution with region competition: automatic 3-D segmentation of brain tumors , 2002, Object recognition supported by user interaction for service robots.

[28]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[29]  Sameer Singh,et al.  Medical Image Segmentation Using Level Sets , 2002 .

[30]  James A. Sethian,et al.  Level Set Methods and Fast Marching Methods , 1999 .

[31]  Jean-Philippe Thirion,et al.  Image matching as a diffusion process: an analogy with Maxwell's demons , 1998, Medical Image Anal..

[32]  J A Maldjian,et al.  Radiologic estimation of hematoma volume in intracerebral hemorrhage trial by CT scan. , 2006, AJNR. American journal of neuroradiology.

[33]  J T Hoff,et al.  Mechanisms of Edema Formation After Intracerebral Hemorrhage: Effects of Extravasated Red Blood Cells on Blood Flow and Blood-Brain Barrier Integrity , 2001, Stroke.

[34]  Sven Loncaric,et al.  Quantitative intracerebral brain hemorrhage analysis , 1999, Medical Imaging.

[35]  Lawrence O. Hall,et al.  Automatic segmentation of non-enhancing brain tumors in magnetic resonance images , 2001, Artif. Intell. Medicine.