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Analysis of texture alterations in myocardium on echocardiographic images
International Congress Series 1256 (2003) 1125 – 1130
Analysis of texture alterations in myocardium on echocardiographic images Vytenis Punys a,*, Jurate Puniene a, Renaldas Jurkevicius b, Jonas Punys a a
Image Processing and Multimedia Laboratory, Kaunas University of Technology, Studentu st. 56-305, LT-3031 Kaunas, Lithuania b Institute of Cardiology, Kaunas Medical University, Sukileliu st.17, LT-3007, Kaunas, Lithuania Received 15 March 2003; received in revised form 15 March 2003; accepted 18 March 2003
Keywords: Echocardiographic image; Structure of myocardium tissue; Statistical parameters of texture; F-test
1. Introduction Echocardiography –ultrasonography of a living heart has become an important basic tool of diagnosis, treatment evaluation, and research in cardiology. It has achieved a prominent place among the other cardiac imaging modalities for many practical and safety reasons. Important reason for the success of echocardiography is that the information it provides is helpful in understanding the mechanisms and evaluating the status and causes of cardiovascular disease in patients [1,2]. A heart disease is correlated with the alterations in the biological tissue of its muscles. These alterations are imaged in echocardiograms as the reflection and transmission of ultrasound in tissue depends on tissue density, elasticity and acoustic impedance. Changes of these parameters are represented as the alterations of an ultrasound image texture. However, the changes of texture are rather imperceptible by the human eye, which is able to differentiate a limited number of grey values and to perceive simultaneously a limited number of graphic elements. Even it is more difficult for the human eye to evaluate tissue granularity and direction of fibres.
* Corresponding author. Tel./fax: +370-37-451577. E-mail address: [email protected] (V. Punys). 0531-5131/03 D 2003 Published by Elsevier Science B.V. doi:10.1016/S0531-5131(03)00449-7
1126
V. Punys et al. / International Congress Series 1256 (2003) 1125–1130
It was pointed out that myocarditis and fibrosis exert influence upon alterations of brightness, heterogeneity and contrast [3,4]. Image processing techniques may be applied for evaluating these alterations by carrying out quantitative analysis of some regions of a heart image texture.
2. Materials and methods Statistical parameters of the image texture on the defined region of interest can present information about the state of myocardium. Our goal was to analyze a heart tissue and to evaluate myocardium on the base of statistical parameters of the heart texture alterations. In Fig. 1 some views of a heart with regions of interest are presented. The texture analysis of the echocardiographic images has been carried out in the following steps:
Compiling heart image database; Selecting images of interest; Feature analysis and its selection; Texture classification.
Diagnostic information might be included in the heart image texture. Statistical texture parameters [5] enable to extract information about tissue properties and to classify the different regions on an image, which define normal or abnormal tissue fragments of a heart. There are some simple statistical characteristics as a mean, a variation, a histogram, though the higher order statistics of the texture have to be taken into account [6]. Texture features can be defined by its:
Spatial frequency components. High spatial frequencies dominate for the fine textures, while coarse textures are characterized by low spatial frequencies. Edge per unit area. Coarse textures have a small number of edges per unit area; fine textures have a high number of them. Grey level spatial dependency that means co-occurrence (conditional distribution) of the grey levels and spatial interrelationship of them. The distribution changes slightly for coarse textures and it changes rapidly for fine textures. Grey level run length. Course textures have many pixels in a run for some grey level and fine textures have only few pixels in it. Auto regression model parameters, which present relation between the neighborhood pixels. The parameter values for course textures are similar and for fine textures they have wide variations.
Spatial frequency components are estimated by discrete Fourier transform. It is well known and frequently used technique in image processing. Textural edginess can be defined by a gradient for any local area (the sum of absolute value of the differences of neighboring pixels). Gradient techniques [7] are widely applied in a contour estimation.
V. Punys et al. / International Congress Series 1256 (2003) 1125–1130
1127
Fig. 1. Regions of interest for the evaluation of myocardium structure in different examination schemes: (a) parasternal view, (b) four-chamber view.
Co-occurrence defines the spatial distribution and the spatial dependence. Let us analyze this texture feature in more detail. Suppose that the texture area has N resolution levels in horizontal and vertical directions. The texture is presented by Ng grey levels. The
1128
V. Punys et al. / International Congress Series 1256 (2003) 1125–1130
grey level co-occurrence is defined by a matrix of conditional frequencies Pi,j(d, h) which depends on distance d between neighboring grey levels i and j. The parameter h defines the angular relationship. For h = 0j, it is a conditional probability estimation of a left – right transition of grey levels i and j. Run length of a grey level is a collinear connected set of pixels, which belong to the same grey level. Grey level runs are characterized by the grey level value, the length of the run (a number of pixels in the run) and the run direction. The run length matrix elements r(i, j) define how often a particular grey level value i is met in a particular run length j, i = 1, Nl, j = 1, Nr, Nl is the number of grey values, Nr is the run lengths. Usually four matrices r(i, j) are calculated in the directions 0j, 45j, 90j, 135j. Some statistics can be calculated from the matrix r(i, j):
Short run emphasis
Nl X Nr X rði; jÞ
j2
i¼1 j¼1
Long run emphasis
Nl X Nr X
, Nl X Nr X
2
j rði; jÞ
, Nl X Nr X
i¼1 j¼1
Grey level non uniformity
Nl Nr X X
rði; jÞ
!2 , Nl X Nr X
j¼1
Nr Nl X X j¼1
Fraction of an image in runs
ð2Þ
rði; jÞ:
i¼1 j¼1
i¼1
Run length non uniformity
ð1Þ
rði; jÞ:
i¼1 j¼1
rði; jÞ
!2 , Nl X Nr X
i¼1
Nl X Nr X i¼1 j¼1
rði; jÞ:
ð3Þ
i¼1 j¼1
, rði; jÞ
rði; jÞ:
ð4Þ
i¼1 j¼1
Nl X Nr X
jrði; jÞ:
ð5Þ
i¼1 j¼1
Principal component technique has been applied for a covariance matrix to list the statistical parameters in decreasing order according to their informativeness. The eigenvalues of the covariance matrix define the informativeness of the statistical parameters [8]. The next step is to define how many of these statistical parameters should be used for the myocardium state evaluation. There are some test criteria to check the hypothesis [9] about a number of the significant statistical parameters: Fisher test, multidimensional discrimination measure, v2-statistics. We have carried out the hypothesis testing of the parameter significance on the base of the Fisher test [10].
V. Punys et al. / International Congress Series 1256 (2003) 1125–1130
1129
3. Results Texture analysis was performed on 140 cine loops of 85 patients. They were selected from the collection of 820 cine loops (230 patients). The selected images were grouped according to the main characteristics of examination: (1) Scanning view—parasternal view, four-chamber view, two-chamber view, and shortaxis view (these are typical heart cross sections for echocardiography); (2) Scanning technique—native (when image is based on the analysis of the same frequency echo-signal as an emitted one), and harmonic (when image is based on the analysis of next harmonic of the echo-signal compared with the emitted one); (3) Transducer frequency—modern ultrasound equipment operate at different frequencies, e.g. 2.5, 3, 3.5, 4 MHz. Three most representative image groups were selected for texture analysis for two classes of patients—sound and with hypertension: (1) Parasternal view—4 MHz harmonic images, (2) Four-chamber view—3 MHz native images, (3) Four-chamber view—3.5 MHz native images. Four regions of interest marked by a cardiologist on the end-systolic and the enddiastolic frames of each ultrasound cine loop have been investigated. Two hundred eighty texture parameters of the myocardium have been calculated. The statistical analysis of the texture parameters proved that features extracted from native images are rather correlated. However, those, which were extracted from harmonic Table 1 The most significant myocardium texture parameters from harmonic images Texture parameters (group: parameter)
Fisher test
Parameters selected according to Fisher test Simple characteristics mean variance Local histogram features skewness kurtosis Co-occurrence matrix P(5, 0) differences P(5, 0) entropy S(0, 5) correlation S(0, 5) sum variance
1.67 1.52 1.16 1.65 0.95 0.88 0.87 0.85
Parameters selected according to classification error probability Run-length features 135j GlevNonUnif. Co-occurrence matrix P(5, 0) differences P(0, 5) sum variance P(0, 2) sum variance P(3, 3) sum variance P(0, 5) contrast
Classification error probability
0.11 0.06 0.14 0.19 0.19 0.18
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V. Punys et al. / International Congress Series 1256 (2003) 1125–1130
images, suite better for distinguishing the myocardium tissue from a healthy one. Most significant of them are presented in Table 1. We keep increasing the database of heart images and keep evaluating statistical parameters and their significance.
4. Conclusions 1. Statistical analysis of the heart tissue texture on harmonic and native echocardiographic images showed that texture features extracted from native images were rather correlated. The features extracted from harmonic images suite better to distinguish the hypertrophic myocardium tissue from a healthy one. 2. The most informative statistical parameters located during the automated myocardium tissue analysis have been: local mean intensity, the form of local histogram, local variance and run-length nonuniformity when the statistical analysis was limited to the measurements of the most informative position of the transducer from the compiled database. 3. Common informative (discriminative) texture features were mostly based on local and co-occurrence variance. It has been proved by testing the hypothesis and using the Fisher criterion ( F-test) and a probability of the classification error.
Acknowledgements The research has been supported by the Lithuanian State Science and Studies Foundation (Reg. Number 26045) and by the Kaunas University of Technology, Lithuania. We would like to express our gratitude to Prof. Materka and his team for ability to use their package of computer programs MaZda (http://eletel.p.lodz.pl/cost/software.html).
References [1] G. Wied, G. Bahr, P. Bartels, Automatic analysis of cell images, in: G. Wied, G. Bahr (Eds.), Automated Cell Identification and Cell Sorting, Academic Press, New York, 1970, pp. 195 – 360. [2] A.K. Bhondari, N.C. Nanda, Myocardial texture characterization by two dimensional echocardiography, American Journal of Cardiology 123 (1989) 832 – 840. [3] E. Lieback, I. Hardouin, R. Meyer, J. Bellach, R. Hetzer, Clinical value of echocardiographic tissue characterization in the diagnosis of myocarditis, European Heart Journal 17 (1996) 135 – 142. [4] H. Feigenbaum, Echocardiographic tissue diagnosis, European Heart Journal 17 (1996) 6 – 7. [5] R.M. Haralick, Statistical and structural approaches to texture, Proc. of the Fourth International Joint Conference on Pattern Recognition, Kyoto, Japan, 1978, pp. 45 – 69. [6] A. Materka, MaZda: computer program for quantitative analysis of image texture, Proc. of Int. Conference Informatics for Health Care, Kaunas University of Technology, Lithuania, 2002, pp. 39 – 59. [7] E. Polak, Computational Methods in Optimization, Mir, Moscow, 1974, in Russian. [8] C.R. Rao, Linear Statistical Inference and its Applications, Wiley, 1965. [9] M.G. Kendall, A. Stuart, The Advanced Theory of Statistics, vol. 3, Charles Griffin, London, 1966. [10] J. Janko, Statisticke tabulky, GOSTstatizdat, Moscow, 1961, in Russian.
Analysis of texture alterations in myocardium on echocardiographic images Vytenis Punys a,*, Jurate Puniene a, Renaldas Jurkevicius b, Jonas Punys a a
Image Processing and Multimedia Laboratory, Kaunas University of Technology, Studentu st. 56-305, LT-3031 Kaunas, Lithuania b Institute of Cardiology, Kaunas Medical University, Sukileliu st.17, LT-3007, Kaunas, Lithuania Received 15 March 2003; received in revised form 15 March 2003; accepted 18 March 2003
Keywords: Echocardiographic image; Structure of myocardium tissue; Statistical parameters of texture; F-test
1. Introduction Echocardiography –ultrasonography of a living heart has become an important basic tool of diagnosis, treatment evaluation, and research in cardiology. It has achieved a prominent place among the other cardiac imaging modalities for many practical and safety reasons. Important reason for the success of echocardiography is that the information it provides is helpful in understanding the mechanisms and evaluating the status and causes of cardiovascular disease in patients [1,2]. A heart disease is correlated with the alterations in the biological tissue of its muscles. These alterations are imaged in echocardiograms as the reflection and transmission of ultrasound in tissue depends on tissue density, elasticity and acoustic impedance. Changes of these parameters are represented as the alterations of an ultrasound image texture. However, the changes of texture are rather imperceptible by the human eye, which is able to differentiate a limited number of grey values and to perceive simultaneously a limited number of graphic elements. Even it is more difficult for the human eye to evaluate tissue granularity and direction of fibres.
* Corresponding author. Tel./fax: +370-37-451577. E-mail address: [email protected] (V. Punys). 0531-5131/03 D 2003 Published by Elsevier Science B.V. doi:10.1016/S0531-5131(03)00449-7
1126
V. Punys et al. / International Congress Series 1256 (2003) 1125–1130
It was pointed out that myocarditis and fibrosis exert influence upon alterations of brightness, heterogeneity and contrast [3,4]. Image processing techniques may be applied for evaluating these alterations by carrying out quantitative analysis of some regions of a heart image texture.
2. Materials and methods Statistical parameters of the image texture on the defined region of interest can present information about the state of myocardium. Our goal was to analyze a heart tissue and to evaluate myocardium on the base of statistical parameters of the heart texture alterations. In Fig. 1 some views of a heart with regions of interest are presented. The texture analysis of the echocardiographic images has been carried out in the following steps:
Compiling heart image database; Selecting images of interest; Feature analysis and its selection; Texture classification.
Diagnostic information might be included in the heart image texture. Statistical texture parameters [5] enable to extract information about tissue properties and to classify the different regions on an image, which define normal or abnormal tissue fragments of a heart. There are some simple statistical characteristics as a mean, a variation, a histogram, though the higher order statistics of the texture have to be taken into account [6]. Texture features can be defined by its:
Spatial frequency components. High spatial frequencies dominate for the fine textures, while coarse textures are characterized by low spatial frequencies. Edge per unit area. Coarse textures have a small number of edges per unit area; fine textures have a high number of them. Grey level spatial dependency that means co-occurrence (conditional distribution) of the grey levels and spatial interrelationship of them. The distribution changes slightly for coarse textures and it changes rapidly for fine textures. Grey level run length. Course textures have many pixels in a run for some grey level and fine textures have only few pixels in it. Auto regression model parameters, which present relation between the neighborhood pixels. The parameter values for course textures are similar and for fine textures they have wide variations.
Spatial frequency components are estimated by discrete Fourier transform. It is well known and frequently used technique in image processing. Textural edginess can be defined by a gradient for any local area (the sum of absolute value of the differences of neighboring pixels). Gradient techniques [7] are widely applied in a contour estimation.
V. Punys et al. / International Congress Series 1256 (2003) 1125–1130
1127
Fig. 1. Regions of interest for the evaluation of myocardium structure in different examination schemes: (a) parasternal view, (b) four-chamber view.
Co-occurrence defines the spatial distribution and the spatial dependence. Let us analyze this texture feature in more detail. Suppose that the texture area has N resolution levels in horizontal and vertical directions. The texture is presented by Ng grey levels. The
1128
V. Punys et al. / International Congress Series 1256 (2003) 1125–1130
grey level co-occurrence is defined by a matrix of conditional frequencies Pi,j(d, h) which depends on distance d between neighboring grey levels i and j. The parameter h defines the angular relationship. For h = 0j, it is a conditional probability estimation of a left – right transition of grey levels i and j. Run length of a grey level is a collinear connected set of pixels, which belong to the same grey level. Grey level runs are characterized by the grey level value, the length of the run (a number of pixels in the run) and the run direction. The run length matrix elements r(i, j) define how often a particular grey level value i is met in a particular run length j, i = 1, Nl, j = 1, Nr, Nl is the number of grey values, Nr is the run lengths. Usually four matrices r(i, j) are calculated in the directions 0j, 45j, 90j, 135j. Some statistics can be calculated from the matrix r(i, j):
Short run emphasis
Nl X Nr X rði; jÞ
j2
i¼1 j¼1
Long run emphasis
Nl X Nr X
, Nl X Nr X
2
j rði; jÞ
, Nl X Nr X
i¼1 j¼1
Grey level non uniformity
Nl Nr X X
rði; jÞ
!2 , Nl X Nr X
j¼1
Nr Nl X X j¼1
Fraction of an image in runs
ð2Þ
rði; jÞ:
i¼1 j¼1
i¼1
Run length non uniformity
ð1Þ
rði; jÞ:
i¼1 j¼1
rði; jÞ
!2 , Nl X Nr X
i¼1
Nl X Nr X i¼1 j¼1
rði; jÞ:
ð3Þ
i¼1 j¼1
, rði; jÞ
rði; jÞ:
ð4Þ
i¼1 j¼1
Nl X Nr X
jrði; jÞ:
ð5Þ
i¼1 j¼1
Principal component technique has been applied for a covariance matrix to list the statistical parameters in decreasing order according to their informativeness. The eigenvalues of the covariance matrix define the informativeness of the statistical parameters [8]. The next step is to define how many of these statistical parameters should be used for the myocardium state evaluation. There are some test criteria to check the hypothesis [9] about a number of the significant statistical parameters: Fisher test, multidimensional discrimination measure, v2-statistics. We have carried out the hypothesis testing of the parameter significance on the base of the Fisher test [10].
V. Punys et al. / International Congress Series 1256 (2003) 1125–1130
1129
3. Results Texture analysis was performed on 140 cine loops of 85 patients. They were selected from the collection of 820 cine loops (230 patients). The selected images were grouped according to the main characteristics of examination: (1) Scanning view—parasternal view, four-chamber view, two-chamber view, and shortaxis view (these are typical heart cross sections for echocardiography); (2) Scanning technique—native (when image is based on the analysis of the same frequency echo-signal as an emitted one), and harmonic (when image is based on the analysis of next harmonic of the echo-signal compared with the emitted one); (3) Transducer frequency—modern ultrasound equipment operate at different frequencies, e.g. 2.5, 3, 3.5, 4 MHz. Three most representative image groups were selected for texture analysis for two classes of patients—sound and with hypertension: (1) Parasternal view—4 MHz harmonic images, (2) Four-chamber view—3 MHz native images, (3) Four-chamber view—3.5 MHz native images. Four regions of interest marked by a cardiologist on the end-systolic and the enddiastolic frames of each ultrasound cine loop have been investigated. Two hundred eighty texture parameters of the myocardium have been calculated. The statistical analysis of the texture parameters proved that features extracted from native images are rather correlated. However, those, which were extracted from harmonic Table 1 The most significant myocardium texture parameters from harmonic images Texture parameters (group: parameter)
Fisher test
Parameters selected according to Fisher test Simple characteristics mean variance Local histogram features skewness kurtosis Co-occurrence matrix P(5, 0) differences P(5, 0) entropy S(0, 5) correlation S(0, 5) sum variance
1.67 1.52 1.16 1.65 0.95 0.88 0.87 0.85
Parameters selected according to classification error probability Run-length features 135j GlevNonUnif. Co-occurrence matrix P(5, 0) differences P(0, 5) sum variance P(0, 2) sum variance P(3, 3) sum variance P(0, 5) contrast
Classification error probability
0.11 0.06 0.14 0.19 0.19 0.18
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V. Punys et al. / International Congress Series 1256 (2003) 1125–1130
images, suite better for distinguishing the myocardium tissue from a healthy one. Most significant of them are presented in Table 1. We keep increasing the database of heart images and keep evaluating statistical parameters and their significance.
4. Conclusions 1. Statistical analysis of the heart tissue texture on harmonic and native echocardiographic images showed that texture features extracted from native images were rather correlated. The features extracted from harmonic images suite better to distinguish the hypertrophic myocardium tissue from a healthy one. 2. The most informative statistical parameters located during the automated myocardium tissue analysis have been: local mean intensity, the form of local histogram, local variance and run-length nonuniformity when the statistical analysis was limited to the measurements of the most informative position of the transducer from the compiled database. 3. Common informative (discriminative) texture features were mostly based on local and co-occurrence variance. It has been proved by testing the hypothesis and using the Fisher criterion ( F-test) and a probability of the classification error.
Acknowledgements The research has been supported by the Lithuanian State Science and Studies Foundation (Reg. Number 26045) and by the Kaunas University of Technology, Lithuania. We would like to express our gratitude to Prof. Materka and his team for ability to use their package of computer programs MaZda (http://eletel.p.lodz.pl/cost/software.html).
References [1] G. Wied, G. Bahr, P. Bartels, Automatic analysis of cell images, in: G. Wied, G. Bahr (Eds.), Automated Cell Identification and Cell Sorting, Academic Press, New York, 1970, pp. 195 – 360. [2] A.K. Bhondari, N.C. Nanda, Myocardial texture characterization by two dimensional echocardiography, American Journal of Cardiology 123 (1989) 832 – 840. [3] E. Lieback, I. Hardouin, R. Meyer, J. Bellach, R. Hetzer, Clinical value of echocardiographic tissue characterization in the diagnosis of myocarditis, European Heart Journal 17 (1996) 135 – 142. [4] H. Feigenbaum, Echocardiographic tissue diagnosis, European Heart Journal 17 (1996) 6 – 7. [5] R.M. Haralick, Statistical and structural approaches to texture, Proc. of the Fourth International Joint Conference on Pattern Recognition, Kyoto, Japan, 1978, pp. 45 – 69. [6] A. Materka, MaZda: computer program for quantitative analysis of image texture, Proc. of Int. Conference Informatics for Health Care, Kaunas University of Technology, Lithuania, 2002, pp. 39 – 59. [7] E. Polak, Computational Methods in Optimization, Mir, Moscow, 1974, in Russian. [8] C.R. Rao, Linear Statistical Inference and its Applications, Wiley, 1965. [9] M.G. Kendall, A. Stuart, The Advanced Theory of Statistics, vol. 3, Charles Griffin, London, 1966. [10] J. Janko, Statisticke tabulky, GOSTstatizdat, Moscow, 1961, in Russian.