Computer aided detection of cavernous malformation in T2-weighted brain MR images

Cavernous malformation or cavernomas is abnormal development of brain blood vessels and affect an estimated 0.5% of the world population. These could cause seizures, intracerebral hemorrhage and various neurological deficits based on the location of the lesion. Radiologists usually analysis brain magnetic resonance (MR) images to detect cavernomas. However, automatic detection of cavernomas by computer has not been investigated enough. This paper proposes a computer aided cavernomas detection method based on MR images analysis. The proposed method includes three steps: brain extraction based on deformable contour (to remove the non-brain tissues from image), template matching (to find suspected cavernomas regions) and post-processing (to get rid of false positives based on size, shape and brightness information). The performance of the proposed technique is evaluated and a sensitivity of 0.92 is obtained after testing.

[1]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[2]  S. Bauer,et al.  A survey of MRI-based medical image analysis for brain tumor studies , 2013, Physics in medicine and biology.

[3]  Dwarikanath Mahapatra,et al.  Skull Stripping of Neonatal Brain MRI: Using Prior Shape Information with Graph Cuts , 2012, Journal of Digital Imaging.

[4]  Kenneth Revett,et al.  Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm , 2014, Expert Syst. Appl..

[5]  A. Marchel,et al.  Familial cerebral cavernous malformation. , 2012, Folia neuropathologica.

[6]  Ming-Ching Chang,et al.  A skull stripping method using deformable surface and tissue classification , 2010, Medical Imaging.

[7]  Russell Greiner,et al.  Quick detection of brain tumors and edemas: A bounding box method using symmetry , 2012, Comput. Medical Imaging Graph..

[8]  Dinggang Shen,et al.  Learning-Based Meta-Algorithm for MRI Brain Extraction , 2011, MICCAI.

[9]  Peng Wang,et al.  Computer‐aided detection of metastatic brain tumors using automated three‐dimensional template matching , 2010, Journal of magnetic resonance imaging : JMRI.

[10]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[11]  Karuppana Gounder Somasundaram,et al.  Fully automatic brain extraction algorithm for axial T2-weighted magnetic resonance images , 2010, Comput. Biol. Medicine.

[12]  Junaed Sattar Snakes , Shapes and Gradient Vector Flow , 2022 .

[13]  R. Leahy,et al.  Magnetic Resonance Image Tissue Classification Using a Partial Volume Model , 2001, NeuroImage.

[14]  A. Marchel,et al.  Original article Familial cerebral cavernous malformation , 2012 .

[15]  P. Kalavathi,et al.  Methods on Skull Stripping of MRI Head Scan Images—a Review , 2016, Journal of Digital Imaging.

[16]  W. Harper,et al.  Cerebral cavernous malformations: natural history and prognosis after clinical deterioration with or without hemorrhage. , 1997, Journal of neurosurgery.

[17]  Douglas C Noll,et al.  An approach for computer-aided detection of brain metastases in post-Gd T1-W MRI. , 2012, Magnetic resonance imaging.