CHARACTERIZATION OF STROKE LESION USING FRACTAL ANALYSIS

Chunduri Karthik; Karthik R; Menaka R

doi:10.22159/ajpcr.2017.v10s1.19558

Authors

Chunduri Karthik Department of Electronics and Communication, School of Electronics Engineering, VIT University Chennai Campus, Chennai, Tamil Nadu, India.
Karthik R Department of Electronics and Communication, School of Electronics Engineering, VIT University Chennai Campus, Chennai, Tamil Nadu, India.
Menaka R Department of Electronics and Communication, School of Electronics Engineering, VIT University Chennai Campus, Chennai, Tamil Nadu, India.

DOI:

https://doi.org/10.22159/ajpcr.2017.v10s1.19558

Keywords:

Lesion, Multifractal analysis, Magnetic resonance imaging

Abstract

Objective: The characterization of stroke lesions is a challenging research issue due to the wide variability in the structure of lesion patterns. The objective of this research work is to characterize the stroke lesion structures using fractal analysis.

Methods: To characterize the complex nature of the lesion structures, fractal box counting analysis is presented in this work. Three parameters from fractal dimension (FD) are considered to characterize the nature of the normal and abnormal brain tissues.

Results: The experimental results are presented for 15 different datasets. Three different parameters namely FD average, FD deviation, and FD

lacunarity are extracted to quantify the properties of the stroke lesion. The observations indicate that there is a significant proportion of separation
of feature values between the normal and abnormal brain tissues.

Conclusion: This work presents an efficient scheme for characterizing the stroke lesions using fractal parameters. It could be further enhanced by incorporating features extracted from other non-linear techniques.

Â

Downloads

Download data is not yet available.

References

Grzesik A, Bernarding J, Braun J, Koennecke HC, Wolf KJ, Tolxdorff T Characterization of stroke lesions using a histogram-based data analysis including diffusion- and perfusion-weighted imaging. Proceedings of SPIE 3978, Medical Imaging 2000: Physiology and Function from Multidimensional Images, 23. April, 20; 2000.

Bernarding J, Braun J, Hohmann J, Mansmann U, Berlage MH, Stapf C, et al. Histogram-based characterization of healthy and ischemic brain tissues using multiparametric MR imaging including apparent diffusion coefficient maps and relaxometry. Magn Reson Med 2000;4:52-61.

Karthik R, Menaka R. Statistical characterization of ischemic stroke lesions from MRI using discrete wavelet transformation. ECTI Trans Electr Eng Electron Commun 2016;14(2):57-64.

Leistner S, Koennecke HC, Dreier JP, Strempel AK, Kathke M, Nikolova A, et al. Clinical characterization of symptomatic

microangiopathic brain lesions. Front Neurol 2011;2:61.

Schaefer PW, Hassankhani A, Putman C, Sorensen AG, Schwamm L, Koroshetz W, et al. Characterization and evolution of diffusion MR imaging abnormalities in stroke patients undergoing intra-arterial thrombolysis. AJNR Am J Neuroradiol 2004;25(6):951-7.

Rajini NH, Bhavani R. Computer aided detection of ischemic stroke using segmentation and texture feature. Measurement 2013;46:1865-74.

Tang FH, Ng DK, Chow DH. An image feature approach for computer-aided detection of ischemic stroke. Comput Biol Med 2011;41:529-36.

Thangavel M, Chandrasekaran M, Madheswaran M. Carotid plaque classification using contourlet features and support vector machines. J Comput Sci 2014;10(9):1642-9.

Sajjadi M, Amirfattahi R, Ahmadzadeh MR, Saghafi MA. A new filter bank algorithm for enhancement of early signs of ischemic stroke in brain CT images. In: Signal and Image Processing Applications (ICSIPA), 2011 IEEE International Conference on. 16, 18 November. 2011 p. 384-9.

Acharya UR, Sree SV, Swapna G, Martis RJ, Suri JS. Automated EEG analysis of epilepsy: A review. Knowl Based Syst 2013;45:147-65.

Lopes R, Betrouni N. Fractal and multifractal analysis: A review. Med Image Anal 2009;13(4):634-49.