A hybrid pixel-based classification method for blood vessel segmentation and aneurysm detection on CTA

In the present study, a hybrid semi-supervised pixel-based classification algorithm is proposed for the automatic segmentation of intracranial aneurysms in Computed Tomography Angiography images. The algorithm was designed to discriminate image pixels as belonging to one of the two classes: blood vessel and brain parenchyma. Its accuracy in vessel and aneurysm detection was compared with two other reliable methods that have already been applied in vessel segmentation applications: (a) an advanced and novel thresholding technique, namely the frequency histogram of connected elements (FHCE), and (b) the gradient vector flow snake. The comparison was performed by means of the segmentation matching factor (SMF) that expressed how precise and reproducible was the vessel and aneurysm segmentation result of each method against the manual segmentation of an experienced radiologist, who was considered as the gold standard. Results showed a superior SMF for the hybrid (SMF=88.4%) and snake (SMF=87.2%) methods compared to the FHCE (SMF=68.9%). The major advantage of the proposed hybrid method is that it requires no a priori knowledge of the topology of the vessels and no operator intervention, in contrast to the other methods examined. The hybrid method was efficient enough for use in 3D blood vessel reconstruction.

[1]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[2]  Gabriele Lohmann,et al.  Analysis and synthesis of textures: a co-occurrence-based approach , 1995, Comput. Graph..

[3]  Eduard Gröller,et al.  Non-linear model fitting to parameterize diseased blood vessels , 2004, IEEE Visualization 2004.

[4]  Francis K. H. Quek,et al.  A review of vessel extraction techniques and algorithms , 2004, CSUR.

[5]  B. Tomandl,et al.  CT angiography of intracranial aneurysms: a focus on postprocessing. , 2004, Radiographics : a review publication of the Radiological Society of North America, Inc.

[6]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[7]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[8]  Jerry L. Prince,et al.  Gradient vector flow deformable models , 2000 .

[9]  Dimitrios Karnabatidis,et al.  CT angiography with three-dimensional techniques for the early diagnosis of intracranial aneurysms. Comparison with intra-arterial DSA and the surgical findings. , 2004, European journal of radiology.

[10]  Olivier D. Faugeras,et al.  CURVES: Curve evolution for vessel segmentation , 2001, Medical Image Anal..

[11]  Sven Loncaric,et al.  Model-based quantitative AAA image analysis using a priori knowledge , 2005, Comput. Methods Programs Biomed..

[12]  M. Lipton Intracranial aneurysms. , 1997, The New England journal of medicine.

[13]  S. Koskinen,et al.  Detection of Intracranial Aneurysms with Two-dimensional and Three-dimensional Multislice Helical Computed Tomographic Angiography , 2004, Neurosurgery.

[14]  J. Garland THE NEW ENGLAND JOURNAL OF MEDICINE , 1977, The Lancet.

[15]  N Roberts,et al.  Segmentation and numerical analysis of microcalcifications on mammograms using mathematical morphology. , 1997, The British journal of radiology.

[16]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[17]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[18]  D Cavouras,et al.  Computer-based grading of haematoxylin-eosin stained tissue sections of urinary bladder carcinomas. , 2001, Medical informatics and the Internet in medicine.

[19]  P Ravazoula,et al.  A computer-based diagnostic and prognostic system for assessing urinary bladder tumour grade and predicting cancer recurrence , 2002, Medical informatics and the Internet in medicine.

[20]  S. Chawla Advances in Multidetector Computed Tomography: Applications in Neuroradiology , 2004, Journal of computer assisted tomography.

[21]  A. Algra,et al.  Prevalence and risk of rupture of intracranial aneurysms: a systematic review. , 1998, Stroke.

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

[23]  K. Choudhari,et al.  Detection of intracranial aneurysms with two-dimensional and three-dimensional multislice helical computed tomographic angiography. , 2005, Neurosurgery.

[24]  James Sayre,et al.  Detection and characterization of very small cerebral aneurysms by using 2D and 3D helical CT angiography. , 2003, AJNR. American journal of neuroradiology.

[25]  W. Schievink,et al.  Intracranial aneurysms. , 1997, The New England journal of medicine.

[26]  Panagiota Spyridonos,et al.  Neural network-based segmentation and classification system for automated grading of histologic sections of bladder carcinoma. , 2002, Analytical and quantitative cytology and histology.

[27]  Miguel A. Patricio,et al.  A COMPARATIVE STUDY OF CONTEXTUAL SEGMENTATION METHODS FOR DIGITAL ANGIOGRAM ANALYSIS , 2004, Cybern. Syst..

[28]  D. Cavouras,et al.  An image-analysis system based on support vector machines for automatic grade diagnosis of brain-tumour astrocytomas in clinical routine , 2005, Medical informatics and the Internet in medicine.

[29]  Kaleem Siddiqi,et al.  3D Flux Maximizing Flows , 2001, EMMCVPR.

[30]  J. Wardlaw,et al.  The detection and management of unruptured intracranial aneurysms. , 2000, Brain : a journal of neurology.