Extraction of Cardiovascular Structures Using Artificial Neural Network and Mathematical Morphology


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Abstract


This paper deals with image processing algorithm that gives a solution for the problem of medical image segmentation. Biological neural network is an artificial abstract model of different parts of the brain or nervous system, featuring essential properties of the system using biologically realistic models. Artificial Neura Networks (ANNs) have been developed for a wide range of applications such as image analysis, enhancement, segmentation, feature extraction and pattern recognition. Among these, image segmentation is more important as it is widely used for object recognition. The watershed transformation algorithm used for segmentation of blood vessels from angiographic images results in some faulty segmented pixels while the use of neural network technique increases the performance with occurrence of some errors. Artificial neural network results in improved quality of image segmentation reflecting the mean square error to be minimum. The proposed algorithm using the combination of morphological filters and back propagation neural network are used for extracting blood vessels from angiographic images of human heart as they have linear structure and Gaussian like profile. Results on various medical data from a set of normal patients are presented and show that this algorithm can be used as a robust segmentation tool.
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Keywords


Angiocardiography; Artificial Neural Network; Back Propagation; Image Segmentation; Morphological Operations

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References


S. Chaudhuri, S.Chatterjee, N. Katz, M. Nelson and Goldbaum, Detection of blood vessels in retinal images using two-dimensional matched filters, IEEE Trans. Medical Imaging, Vol.8, no.3, pp.263-269,1989.

Stefan Worz and Karl Rohr, Segmentation and Quantification of human vessels using a 3-D cylindrical intensity model, IEEE Trans. on Image processing, Vol.16, no.8, 1994-2004, 2007.

R. Erik. Urbach and H. F. Michael Wilkinson, Efficient 2-D grayscale morphological transformations with arbitrary flat structuring elements, IEEE Trans. on Image processing, Vol.17, no.1, pp 1-8, 2008.

R.Latha, S.Senthilkumar, V.Manohar, Segmentation of linear structures from medical images, Procedia Computer Science, Vol.2, pp 303-306, 2010.

R.Latha, S.Senthilkumar, Feature extraction using watershed transformation, Advances in Intelligent and Soft Computing, Frontiers in Computer Education, Vol. 133, pp 899-906, 2012.

R.Latha, S.Senthilkumar, Linear feature extraction using fuzzy watershed algorithm, European journal of Scientific Research, Vol.72, no. 1, pp 58-63, 2012.

H.Masoumi, A.Behrad, M.A. Pourmina, A. Roosta, Automatic liver segmentation in MRI images using an iterative watershed algorithm and artificial neural network, Biomedical Signal Processing and Control, Article in press, 2012.

Angel Domingo Sappa, Unsupervised contour closure algorithm for range image edge-based segmentation, IEEE Transactions on Image processing, Vol.15, No.2, pp 377-384, 2006.

B.Soumya and B.Sheela Rani, Colour image segmentation using fuzzy clustering techniques and competitive neural network, Applied Soft Computing, Vol.11, pp 3170-3178, 2011.

Gonzalez and Woods, Digital Image Processing (Pearson education, India).

J. Serra, Image analysis and Mathematical Morphology (London, U.K: Academic, 1982).

S.N.Sivanandam and S.N.Deepa, Principles of Soft Computing (second edition, Wiley, India).

Zhang Xiao-hong, Zhu Yuan-yuan, Fan Zhong-kui, Region of interest Automatic Extraction for Color Image based on Mathematical Morphology, IEEE Ninth International Conference on Computer and Information Technology (pp. 113-117 Year of Publication: 2009).

Samiappan, D., Chakrapani, V., Classification of ultrasound carotid artery images using texture features, (2013) International Review on Computers and Software (IRECOS), 8 (4), pp. 933-940..


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