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Abstrakty
Brain aneurysm is one of the most life-threatening events, which is associated with a high rate of mortality and disability. There are many factors, which specify the best treatment option for each particular patient. In this paper, an automatic computer-aided extraction algorithm for brain aneurysm, from fused digital subtraction angiography (DSA) images is proposed. In this algorithm, firstly, to remove vessel structure, morphological operations based on multi-directional structure elements and nonlinear diffusion filtering are used. Then, by applying circular Hough transform and region growing algorithms, the aneurysm extraction procedure is performed. In this step, to overcome to poor edge gradient of aneurysm, we define a labeled diffused image which specifies the region growing conditions. Finally, by using morphological operators, the aneurysm extraction performance of our algorithm is improved. In addition, the radius of extracted aneurysm is defined and reported as a geometric feature. The experimental results indicate that our proposed algorithm obtains accuracy rate of 77.5% for the aneurysm extraction on 30 abnormal cases.
Wydawca
Czasopismo
Rocznik
Tom
Strony
264--275
Opis fizyczny
Bibliogr. 26 poz., rys., wykr.
Twórcy
autor
- Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran
autor
- Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran
Bibliografia
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- [2] Brisman J, Song J, Newell D. Medical progress: cerebral aneurysms. N Eng J Med 2006;355(9):928–39.
- [3] Khurana VG, Spetzler RF. The brain aneurysm. Author House; 2006.
- [4] Kouskouras C, Charitanti A, Giavroglou C, Foroglou N, Selviaridis P, Kontopoulos V, et al. Intracranial aneurysms: evaluation using CTA and MRA. Correlation with DSA and intraoperative findings. Neuroradiology 2004;46(10):842–50.
- [5] Spencer T, Olson JA, McHardy KC, Sharp PF, Forrester JV. An image processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus. Comp Biomed Res 1996;29 (4):284–302.
- [6] Abelazeem S, Hafez M, Auda G. Using circular Hough transform for detecting microaneurysms in fluorescein angiograms of the ocular fundus. International Conference on Industrial Electronics, Technology and Automation; 2001.
- [7] Abdelazeem S. Microaneurysm detection using vessels removal and circular Hough transform. 9th National Radio Science Conference. 2002. pp. 421–6.
- [8] Hentschke CM, Beuing O, Nickl R, Tonnies KD. Automatic cerebral aneurysm detection in multimodal angiographic images. The IEEE Conference on Nuclear Science Symposium and Medical Imaging. 2011. pp. 3116–20.
- [9] Uchiyama Y, And H, Yokoyama R, Hara T, Fujita H, Iwana T. Computer-aided diagnosis scheme for detection of unruptured intracranial aneurysm in MR angiography. 27th IEEE Annual Conference on Engineering in Medicine and Biology 2005;3031–4.
- [10] Mondal S, Chandra A. One novel algorithm for the detection of Cerebral Aneurysm using morphological filtering. International Conference on Communications and Signal Processing (ICCSP) 3–5 April 2014;137–41.
- [11] Mitra J, Chandra A. Detection of cerebral aneurysm by performing thresholding-spatial filtering-thresholding operations on digital subtraction angiogram. Adv Intell Syst Comput 2013;177:915–21.
- [12] Zakaria H, Kurniawan A, Mengko TLR, Santoso OS. Detection of cerebral aneurysms by using time based parametric color coded of cerebral angiogram. International Conference on Electrical Engineering and Informatics. 2011. pp. 1–4.
- [13] Mazrouei Sebdani F, Pourghassem H. A robust and real-time road line extraction algorithm using Hough transform in intelligent transportation system application. IEEE International Conference on Computer Science and Automation Engineering (CSAE), vol. 3. 2012. pp. 256–60.
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- [15] Momeni S, Pourghassem H. An automatic fuzzy-based multi-temporal brain digital subtraction angiography image fusion algorithm using curvelet transform and content selection strategy. J Med Syst 2014;38(August (8)): 1–16.
- [16] Shahbeig S, Pourghassem H. A fast and automatic algorithm for optic disc extraction in retinal images using PCA-based pre-processing and Curvelet transform. J Opt Soc Am A 2013;30(January (1)):13–21.
- [17] Shajahan SN, Roy RC. An improved retinal blood vessel segmentation algorithm based on multistructure elements morphology. Int J Comp Appl 2012;57(November (16)): 31–8.
- [18] Prona P, Malik J. Scale-space and edge detection using anistropic diffusion. IEEE Trans Pattern Anal Mach Intell 1990;12(7):629–39.
- [19] Baopu L, Nong S, Zhiguo C. Contrast-modulated nonlinear diffusion for X-ray angiogram images. 27th Annual Conference on Engineering in Medicine and Biology; 2005. pp. 1736–8.
- [20] Li X, Chen T. Nonlinear diffusion with multiple edginess thresholds. Pattern Recogn 1994;27(8):1029–37.
- [21] Nourmohamadi N, Pourghassem H. Optical disc and diabetic retinopathy detection in retinal images using morphological operation and region growing. Int Rev Comp Softw 2012;7(4):1463–9.
- [22] Hojjatoleslami SA, Kittler J. Region growing: a new approach. IEEE Trans Image Process 1998;7(7):1079–84.
- [23] Xiaomin P, Hongyu G, Jianping D. Computerized detection of lung nodules in ct images by use of multiscale filters and geometrical constraint region growing. International Conference on Bioinformatics and Biomedical Engineering. 2010. pp. 1–4.
- [24] Deriche R. Using Canny's criteria to derive a recursively implemented optimal edge detector. Int J Comp Vis 1987;1 (April (2)):167–87.
- [25] Aquino A, Gegundez-Arias ME, Marin D. Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction technique. IEEE Trans Med Imaging 2010;29(11):1860–9.
- [26] Cameron JR, Skofronick JG. Medical physics. Canada: John Wiley & Sons Inc.; 1978.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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