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http://dx.doi.org/10.7742/jksr.2015.9.6.369

Texture Feature Analysis Using a Brain Hemorrhage Patient CT Images  

Park, Hyonghu (Dept. of Radiological Science, International University of Korea)
Park, Jikoon (Dept. of Radiological Science, International University of Korea)
Choi, Ilhong (Dept. of Radiological Science, International University of Korea)
Kang, Sangsik (Dept. of Radiological Science, International University of Korea)
Noh, Sicheol (Dept. of Radiological Science, International University of Korea)
Jung, Bongjae (Dept. of Radiological Science, International University of Korea)
Publication Information
Journal of the Korean Society of Radiology / v.9, no.6, 2015 , pp. 369-374 More about this Journal
Abstract
In this study we proposed a texture feature analysis algorithm that distinguishes between a normal image and a diseased image using CT images of some brain hemorrhage patients, and generates both Eigen images and test images which can be applied to the proposed computer aided diagnosis system in order to perform a quantitative analysis for 6 parameters. And through the analysis, we derived and evaluated the recognition rate of CT images of brain hemorrhage. As the results of examining over 40 example CT images of brain hemorrhage, the recognition rates representing a specific texture feature-value are as follows: some appeared to be as high as 100% including average gray level, average contrast, smoothness, and Skewness while others showed a little low disease recognition rate: 95% for uniformity and 87.5% for entropy. Consequently, based on this research result, if a software that enables a computer aided diagnosis system for medical images is developed, it will lead to the availability for the automatic detection of a diseased spot in CT images of brain hemorrhage and quantitative analysis. And they can be used as computer aided diagnosis data, resulting in the increased accuracy and the shortened time in the stage of final reading.
Keywords
Texture Feature Analysis; Brain Hemorrhage; Recognition Rate;
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1 Joyce. KE, Hayaska. S, Laurienti. PJ, "A genetic algorithm for controlling an agent-based model of the functional human brain", Biomed Sci Instrum, vol. 48, pp.210-217, 2012.
2 Khotanlou. H, Afrasiabi. M, "Segmentation of Multiple Sclerosis Lesions in Brain MR Images Using Spatially Constrained Possibilistic Fuzzy C-Means Classification", J Med Signals Sens, Vol. 1, pp.149-155, 2011.
3 H. J. Kim, W. K. Bae, J. J. Cha, K. W. Kim, W. S. Jo, I. Y. Kim, K. S. Lee, "Radiologic Findings of Acute Spontaneous Subdural Hematomas", The Journal of the Korean Radiological Society, Vol. 38, No. 3, pp.391-396, 1998.   DOI
4 S. M. Lee, "Clinical Feature and Outcome in Spontaneous Cerebellar Hemorrhage: Determination of Treatment Strategies", Journal of the Korean Neurological Association, Vol. 22, No. 4, pp.290-294, 2004.
5 Shiraishi. J, Li. Q, Appelbaum. D, Doi K, "Computer-aided diagnosis and artificial intelligence in clinical imaging", Semin Nucl Med, Vol.41, pp.449-462, 2011.   DOI   ScienceOn
6 El. Yazaji. M, Battas. O, Agoub. M, Moussaoui. D, Gutknecht. C, Dalery. J, d'Amato. T, Saoud. M, "Validity of the depressive dimension extracted from principal component analysis of the PANSS in drug-free patients with schizophrenia", Schizophr Res, Vol. 56, pp.121-127, 2002.   DOI   ScienceOn
7 Gletsos. M, Mougiakakou. SG, Matsopoulos. GK, Nikita. KS, Nikita. AS, Kelekis. D, "A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier", IEEE Trans Inf Technol Biomed, Vol. 7, pp.153-162, 2003.   DOI   ScienceOn
8 Heller. MA, "Texture perception in sighted and blind observers", Percept Psychophys, Vol. 45, pp.49-54, 1989.   DOI   ScienceOn
9 Kontos. D, Ikejimba. LC, Bakic. PR, Troxel. AB, Conant. EF, Maidment. AD, "Analysis of parenchymal texture with digital breast tomosynthesis: comparison with digital mammography and implications for cancer risk assessment", Radiology, Vol. 261, pp.80-91, 2011.   DOI   ScienceOn
10 Chen. XJ, Wu. D, He. Y, Liu. S, "Study on application of multi-spectral image texture to discriminating rice categories based on wavelet packet and support vector machine", Guang Pu Xue Yu Guang Pu Fen Xi, Vol. 29, pp.222-225, 2009.