Ultrasound image texture processing for evaluating fatty liver in peripartal dairy cows Viren Amin*a, Gerd Bobeb, Jerry Youngb, Burim Ametajb, and Donald Beitzb a Center for Nondestructive Evaluation and bDepartment of Animal Science Iowa State University, Ames, Iowa 50011 ABSTRACT The objective of this work is to characterize the liver ultrasound texture as it changes in diffuse disease of fatty liver. This technology could allow non-invasive diagnosis of fatty liver, a major metabolic disorder in early lactation dairy cows. More than 100 liver biopsies were taken from fourteen dairy cows, as a part of the USDA-funded study for effects of glucagon on prevention and treatment of fatty liver. Up to nine liver biopsies were taken from each cow during peripartal period of seven weeks and total lipid content was determined chemically. Just before each liver biopsy was taken, ultrasonic B-mode images were digitally captured using a 3.5 or 5 MHz transducer. Effort was made to capture images that were non-blurred, void of large blood vessels and multiple echoes, and of consistent texture. From each image, a region-of-interest of size 100-by-100 pixels was processed. Texture parameters were calculated using algorithms such as first and second order statistics, 2D Fourier transformation, co-occurrence matrix, and gradient analysis. Many cows had normal liver (3% to 6% total lipid) and a few had developed fatty liver with total lipid up to 15%. The selected texture parameters showed consistent change with changing lipid content and could potentially be used to diagnose early fatty liver non-invasively. The approach of texture analysis algorithms and initial results on their potential in evaluating total lipid percentage is presented here. Keywords: Ultrasound imaging, texture processing, fatty liver, liver lipid
1. INTRODUCTION Ultrasound B-mode images have been used to aid in diagnosing liver diseases. A distinct texture pattern on ultrasound images of the liver depends on the granular structure of liver parenchyma as well as the characteristics of the imager. Many researchers have attempted to associate image texture analysis and diagnosis of different diseases of liver. Generally, liver has been categorized as being normal, having diffuse pathology (such as fatty liver), and having focal pathology (such as cirrhosis). In the present study, we attempt to characterize the liver texture as it changes at different stages of diffuse disease such as fatty liver in dairy cows. Fatty liver (i.e., hepatic lipidosis), an accumulation of triacylglycerol in liver cells, has been reported to occur in the peripartal and early postpartal period in up to 50% of all dairy cows. Fatty liver can impair functions of the liver, precede ketosis, decrease immune response and increase susceptibility for infectious diseases (lameness, mastitis, and metritis), and increase susceptibility for retained placentas and partueient paresis. Thus, fatty liver is a major metabolic disorder in dairy cows. Researchers at Iowa State University have been studying effects of glucagon in preventing and treating fatty liver in peripartal dairy cows ([1] and [2]). Currently, the triacylglycerol and total lipid content of the liver can be determined reliably only by puncture biopsy of the liver (obtaining one to five grams of liver tissue) followed by chemical or histological analyses. This diagnostic procedure is not easily adaptable to on-farm use because the biopsy involves some risk and is timeconsuming, and the chemical analysis also is expensive and time-consuming. The objective of the current work is to develop ultrasonic technology that will allow accurate non-invasive determination of the percentage of lipid in the liver of earlylactation dairy cows. This technology will allow non-invasive diagnosis of fatty liver and will distinguish among cows that are normal or have mild or severe cases of fatty liver. Many techniques of analyzing image texture have been proposed in literature for various applications. We adapted a selected set of four techniques for ultrasound B-scan texture analysis for this work. These techniques included first-order statistics, second-order statistics using co-occurrence matrix, gradient analysis, and 2D Fourier transformation analysis. These techniques are described in the next section. An early study of ultrasound image texture parameters for evaluating lipid content in 29 dairy cow livers has been promising [3]. *
Mailing address: Viren R. Amin, Center for Nondestructive Evaluation, Iowa State University, 1915 Scholl Road, Ames, Iowa 50011; Email:
[email protected]; Phone: 515-294-9736
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2. DATA ACQUISITION The cows used in this study were part of the USDA-funded study for effects of glucagon on prevention and treatment of fatty liver. Since the study started in December 1999, more than 100 liver biopsies have been taken. Up to nine liver biopsies were taken from each of several dairy cows (Holsteins) at periodic intervals between 7 days before expected calving dates and 42 days after individual cows calved. The samples of liver were analyzed chemically for total lipid content. Just before each liver biopsy was taken, ultrasonic B-mode images were digitally captured using an Aloka Micrus 500 ultrasound scanner. Two anatomical sites, between the 11th and 12th ribs and between the 10th and 11th ribs, and two transducers, a 3.5 MHz transducer for body composition (Aloka model UST-5011U-3.5) and a 5 MHz transducer for reproductive evaluation in large animals (Aloka model UST-588U-5), were used to collect images. For each site and each transducer, at least four images were captured using a computer equipped with a frame grabber. Attention was paid to capture good quality images that were not blurred from respiration movement or motion of cow or transducer, void of large blood vessels, of consistent texture, and void of multiple echoes from sharp interfaces such as subcutaneous tissue layers. Each image was identified with an ID that included cow’s ID and a number representing the serial scan. This ID was entered on ultrasound scanner as well as on a hand-held keypad connected to a computer for digitizing and saving image frames on a Zip disk. After the scanning and biopsy session, the image files from the Zip disk were transferred to another computer for processing later. Each image was 512 pixels wide by 486 pixels deep with pixel values ranging from zero to 255, i.e., 8-bit acquisition representing 256 shades of gray. It is acknowledged that the proposed approach of image texture analysis provides a biased result for the given equipment and set of conditions only. Our objective was to develop the technique using a commercially available portable ultrasound scanner for specific equipment settings. The ultrasound equipment settings including gain (near, far, and overall), focus, and time-gain-compensation were kept the same throughout the study.
3. IMAGE TEXTURE ANALYSIS Image analysis was done using a PC-based image processing research software, developed at Iowa State University [4], that implemented texture processing algorithms as a library module. Images were processed using a region-of-interest of size 100-by-100 pixels that represented about 4 cm wide by 3 cm deep region of the liver using a 3.5 MHz transducer. Texture parameters were calculated using four texture-processing algorithms: first-order statistics such as histogram analysis; secondorder statistics using co-occurrence matrix analysis; gradient processing, and 2D Fourier transformation analysis. These algorithms are described in next section. 3.1. First-order statistics Discrete array of image region-of-interest can be represented by a first-order probability distribution of image pixel amplitude (gray level). The shape of the image histogram characterizes the image. For example, histogram with wide amplitude distribution indicates a high-contrast image. Histogram parameters such as means, variance, skewness, kurtosis, mode, and percentile distributions provide information about image darkness, brightness, and contrast. It is important to note here that these features depend on the equipment, and calibration or consistent equipment settings and procedures across the scanning sessions are crucial. In many studies, the mean ultrasound echo intensity was shown not to correlate significantly with fat content in the livers of fish [5], rat [6], and human [7]. Bondestam et al. [5] assessed the correlation between the ultrasound echo intensity of the liver of six live burbot fish and concentration of the different tissue components such as water, fat, collagen, and protein, and histological findings. They reported that increasing size of fat droplets and clustering of droplets correlated well with the echo intensity, but there was no statistically significant correlation between echo intensity and chemically measured fat concentration. 3.2. Second-order statistics Many researchers have used texture assessment by second-order-statistics, based on co-occurrence matrix analysis. Haralick et al. [8] presented a set of features from co-occurrence matrix that has been widely used in image texture processing applications. The co-occurrence matrix is a joint probability distribution of pairs of geometrically related image pixels. For liver ultrasound images, four parameters, contrast, angular second moment, entropy, and correlation were among the most useful parameters in studies by Layer [6, 9], Nicholas et al. [10], and Haberkorn at al. [7]. Several co-occurrence parameters
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were also shown effective in characterizing ultrasound texture for different levels of intramuscular percentage of fat (marbling) for beef quality grading [11]. Co-occurrence parameters provide information on texture or speckle patterns in an image. Grey level spatial-dependence probability distribution matrices were calculated at angles 0, 45, 90, and 135 degrees. These matrices were then used to calculate several texture parameters such as contrast, sum entropy, difference variance, and correlation, as defined in [8]. 3.3. Gradient analysis Gradient transformation technique is used to assess changes in image pixel values with respect to adjacent pixel value. The technique provides two types of transformed images including the magnitude and the phase or direction of the gradient. Gradient magnitude and gradient phase images were calculated from ROI box. These transformed images were then used to compute statistical parameters including mean, variance, skewness, kurtosis, and percentile distribution. The gradient is calculated as a derivative of the image amplitude at each pixel in region-of-interest. The gradient is specified as a vector having both magnitude and direction. The magnitude GM at position x,y is given by
G M ( x, y ) =
2
2
( I ( x +1, y)− I ( x −1, y)) + ( I ( x, y −1)− I ( x, y +1))
and the direction Gφ at position x,y is given by
I ( x + 1, y ) − I ( x − 1, y ) . Gφ ( x, y ) = tan −1 I ( x, y − 1) − I ( x, y + 1) From GM and Gφ, simple distribution parameters such as mean, variance, skewness, kurtosis, mode, and maximum were calculated. 3.4. Fourier domain analysis For the description of image texture, information derived from Fourier spectral power has been widely shown to be useful (e.g., [10]). However, the textural content of the Fourier phase information is low [12]. Fourier transformation techniques transform data into a form that provides information on the occurrence and frequency of repetitive features in an image. Transformed images from the selected ROI are used to determine the distribution of power at different frequencies. From such distributions, the rate of change in power from one frequency to another is calculated using curve fitting algorithms and ratios of powers within different frequency ranges. For a given image, let the region-of-interest be I(x,y) of size NxN where I(x,y) represents the gray level in x and y spatial coordinates. The Fourier transform F(u,v) is calculated as
F (u , v) =
1 N2
N −1 N −1
∑∑ I ( x, y)e
− j 2π ( xu + yv ) / N
,
y =0 x =0
where u and v are spatial frequencies and 0 < u, v < N-1. The Fourier power spectrum is computed as Fp(u,v) = F(u,v) F*(u,v) = |F(u,v)|2, where Fp is the sample power spectrum and * denotes the complex conjugate. The power spectrum is circularly shifted so that the center represents (0,0) frequency. The coarse texture shows high values of Fp concentrated near origin, while the fine texture shows a more spread out distribution of values. Similarly, a texture with edges or lines in a given direction θ has high values of Fp concentrated near θ+π/2, while homogeneous texture does not show any directional concentration of Fp values. From Fourier power spectrum, two types of features are commonly calculated using annular and wedge sampling geometries. The ring shaped samples are calculated as
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∑F
FR (r1 , r2 ) = 2
2
2
p
r1 ≤u + v < r2 0
