6 JEST-M, Vol 3, Issue 1, 2014 Computer-Aided Quantitative Analysis of Liver using Ultrasound Images Email: poojaanandram @gmail.com P.G. Student, Department of Electronics and Communications Engineering, V V College of Engineering, Mysore Abstract- Fatty liver is a common liver disease even in case of non alcoholic persons, whose diagnosis is mainly qualitative, and often depends only on the subjective judgment of technicians and doctors. Therefore, feature extraction and quantitative analysis of liver B-scan ultrasonic images is very much essential. This greatly improves the medical field clinical diagnostic accuracy rate, repeatability and efficiency. It also provides a measure of severity for hepatic steatosis. This paper proposes a novel method of non alcoholic fatty liver diagnosis based on liver B-mode ultrasonic images & according to different characteristics of fatty liver and healthy liver, important image features are extracted and selected to distinguish between the two categories. It is shown that the computer aided diagnostic results are satisfactorily consistent with those made by doctors. Keywords NFLSD, SVM, NFFGR I. INTRODUCTION Fatty liver is a reversible condition where large vacuoles of triglyceride fat accumulate in liver cells. Its main causes are obesity, alcohol, diabetes. At present the global incidence rate of fatty liver is still increasing due to growth of obesity, alcoholism and diabetes. It is reversible in its early stage, so the early detection and treatment is crucial for control of the disease. The diagnostic gold standard, liver biopsy, is not well accepted by the patients because it is invasive. Nonalcoholic fatty liver disease is a term used to describe the accumulation of fat in the liver of people who drink little or no alcohol. It is common and for most people, causes no signs, symptoms and no complications. Non alcoholic fatty liver disease progresses through the following stages: Simple steatosis (fatty liver NAFLD), Fatty liver with inflammation (non alcoholic steatohepatitis, NASH) [11], Fatty liver with liver hardening and liver scarring (liver cirrhosis), Liver cancer and/or complete liver failure, death if liver transplant is performed. Clinical diagnosis of B-ultrasound images of fatty liver [1], to a large extent, relies on the image quality and experience of technicians and doctors. Using the subjective judgment and non-quantitative description, doctors determine the incidence and the severity of fatty liver. However, the poor image quality, speckle noise, and various physical conditions of patients obstruct a unified diagnostic standard. In this approach, an attempt is made to develop completely new computational measures corresponding to some textural properties, so as to ensure general applicability, while at the same time minimize the cost of computation. The computational form for each property is derived by expressing a perceptual description of the property in terms of spatial changes in intensity and/or dynamic range of intensity. A computer-aided liver ultrasound image quantitative analysis [8] is necessary and will contribute in establishing a clinical objective non alcoholic fatty liver diagnosis method. This also improves clinical diagnostic accuracy, repeatability, and efficiency. Generally the diagnostic problem was transformed into pattern recognition of liver ultrasound image characteristics. Based on the fatty liver ultrasound image characteristics and the practical clinical diagnostic criteria, this paper explains the extraction of image features such as near-field light-spot density, near-far-field grayscale ratio, grayscale co-occurrence matrix [2], texture feature number [3] [4] neighborhood gray-tone difference matrix. This helps to analyze the statistical differences of the parameters on the normal liver and fatty liver [9], and selects the most effective features to form the best feature vectors. II. METHODOLOGY A. Data collection Ultrasound images are acquired from local hospitals. Cases of normal liver and cases of fatty liver of varying degrees are confirmed by ultrasound practitioners with years of clinical experience. Analysis of ultrasound image characteristics quantitatively needs two Region Of Interest (ROIs). They are as follows:
7 JEST-M, Vol 3, Issue 1, 2014 One ROI in the near-field (located at the bottom of subcutaneous fat), and the other ROI in the far-field (located at the top of the liver capsule)ultrasound Images obtained after RF Signal and consequent steps of preprocessing are in RGB format (M N P), though they look as a grayscale image from naked eye. So the color Image to gray scale Image conversion is very important, except when we need to extract color textural features. An example of ultrasound image and two ROIs selected is shown in Figure 1. original ROI image in the near-field, denoted by I 1 (x,y), where (x,y) are pixel coordinates and the density of light-spots in the area is measured from the following steps: a) A Laplace of Gaussian (LoG) operator with the following mask is applied to retain the high frequency component denoted by f(x,y) from I1(x,y). 0 0-1 0 0 0-1 -2-1 0-1 -2 16-2 -1 NEAR FIELD 0-1 -2-1 0 0 0-1 0 0 b) Thresholding is applied with a threshold T to the filtered image f(x,y) and the resultant image g(x,y) is shown as follows. g(x,y)= 255 f(x,y) T 0 f(x,y T (1) Figure 1. Ultrasound image of liver [1] FAR FIELD B. Feature Extraction Some observations for ultrasound diagnosis of fatty liver are that grayscale in the near-field is observably enhanced, light-spots are smaller, and grayscale in the far-field attenuates depending on the disease severity. Combining fatty liver diagnostic criteria and grayscale and texture characteristics of liver ultrasound images, a set of features are extracted and significance of these features are analyzed statistically using feature selection methods. Five features which show a good ability to distinguish between fatty liver and normal liver are selected for the characterization and classification. These features are near field lightspot density, near-far-field grayscale ratio, angular second-order moment and contrast in grayscale cooccurrence matrix, and complexity in neighborhood gray-tone difference matrix. 1) Near-field Light-Spot Density (NFLSD) Ultrasonic images contain a large number of light-spots, which represent high-frequency signal within the images. A high-pass filter is applied to the c) The connected areas are identified with the help of 8-neighborhood connectivity[5] in the binary image g(x,y) and the number of connected areas are extracted as a measure of light-spot density within the ROI Figure 2. Sequence of images to obtain NFLSD
8 JEST-M, Vol 3, Issue 1, 2014 2) Near-Far-Field Grayscale Ratio (NFFGR) Fat in the liver absorbs and scatters more energy of ultrasound waves, thereby attenuates the ultrasound echo signal. In ultrasound images of fatty liver, grayscale in the far-field is lower than that in the near-field [1]. The higher the liver fat content is greater the attenuation. Because ultrasound devices are always pre-adjusted on the basis of healthy livers before examination, grayscale in the near-field and far-field is generally uniform for healthy livers. NFFGR was calculated by ratio of summed grayscale in the near-field ROI to far-field ROI, shown as follows: NFFGR= / Where H is height and width of ROI; I2 (x, y) is grayscale image for the far-field ROI. (2) 3) Grayscale Co-occurrence Matrix Grayscale co-occurrence matrix helps in texture analysis, which is a matrix function of distance and angle between pixels. Angular second-order moment and contrast[1] in grayscale co-occurrence matrix are effective characteristics to separate normal liver and fatty liver. Perceptually, an image is said to have a high level of contrast if areas of different intensity levels are clearly visible. Thus high contrast means that the intensity difference between neighboring regions is large [2]. This is usually the case when the dynamic range of gray scale is large or when it is stretched. 4) Neighborhood gray tone difference matrix Neighborhood gray-tone difference matrix reflects grayscale difference between pixels with certain grayscale and their neighboring pixels. Complexity parameter is a useful feature to distinguish between normal liver and fatty liver. For the grayscale image I1 (x, y) with size H H, and a W W window was defined, where W = 2k +1 (k generally takes 1 or 2), and the mean grayscale value matrix was calculated in the following equation: The computations for NGTDM [7][13] are as follows: Let f(k, I) be the gray tone of any pixel at ( k, I ) having gray tone value i. Then the average gray-tone over a neighborhood centered at, but excluding (k,i ) is found using the expression: [ ] For (m,n) (0,0) Where d specifies the neighborhood size and W = (2d + 1) 2, Then the i th entry in the NGTDM is for i ϵ Ni if Ni 0 (3) (4) where {Ni) is the set of all pixels having gray tone i (except in the peripheral regions of width d).the features are extracted for d=1 and d=2 Complexity refers to the visual information content of a texture. A texture is considered complex if the information content is high. This occurs when there are many patches or primitives present in the texture, and more so when the primitives have different average intensities. Complexity is given by: (5) C. Feature Classification Practical standards for diagnosis of fatty liver disease are Light-spots are less. NFLSD value is low. The grayscale in the near-field is observably enhanced. Grayscale in the far-field attenuates depending on the disease severity. Grayscale in the near field and far field is non uniform. Near-far-field gray scale ratio is not equal to unity. More the fat accumulation NFFGR tends to zero. Low contrast for a fatty liver. Ultrasound reflection and scattering area increase Image texture gets thicker. ASM is bigger. There are many classification algorithms [6] to properly make the needed classification. SVM [8] is a useful pattern recognition method. It transforms a pattern recognition problem into a quadratic programming optimization, ensuring the overall optimal solution and avoiding the local convergence. SVM deals with the small sample set and high-dimensional nonlinear pattern recognition problems with unique advantages, and thus can be effectively applied to small
9 JEST-M, Vol 3, Issue 1, 2014 sample estimation and prediction problems. In this paper SVM is used for classification of fatty liver and nomal liver. III. RESULTS The Combining fatty liver diagnostic criteria, grayscale and texture characteristics of liver ultrasound images, a set of features are extracted. The significance of these features are analyzed statistically using feature selection methods. This method helps to show a good ability to distinguish between fatty liver and normal liver. This study uses classification rate to evaluate and compare fatty liver classification methods. For normal liver and fatty liver, two classification accuracy rates are defined. In this study, all ROIs within B-scan ultrasound images area chosen by experienced clinical doctors, and features for ROIs are analyzed and calculated. In the calculation of near-field light spot density, the threshold is chosen as 200[1]. For calculation of the angular second order moment and contrast in grayscale co-occurrence matrix, 256 grayscales were divided equally into 16 levels, and d = 1, θ = 0. In the calculation of complexity of NGTDM, window size was chosen to be 5. All these 5 features are normalized to make them equally weighted. Combining fatty liver diagnostic criteria, grayscale and texture characteristics of liver ultrasound images, a set of features are extracted. The significance of these features are analyzed statistically using feature selection methods. Clinical image data are collected and satisfactory classification results are obtained, which are consistent with diagnostic results by clinical doctors and is shown in the Table 1. Table 1: Quantitative results FEATURES Normal liver Fatty liver NFLSD >0.5 < 0.5 NFFGR ~1 <0.6 CONTRAST >0.1 0.01-0.1 ASM 0.1-0.5 > 0.5 COMPLEXITY >3e4 <3e4 IV. CONCLUSION In this paper based on the characteristics of liver ultrasound images and clinical diagnostic criteria of fatty liver, five features which are able to distinguish between normal liver and fatty liver are extracted and selected. Satisfactory classification results are obtained, which are consistent with diagnostic results by clinical doctors. This method establishes a more objective diagnostic means to improve the clinical diagnostic accuracy, efficiency and repeatability, analyze fatty liver disease quantitatively, and thus reduces misdiagnosis caused by the subjective judgment difference. This method shows a good ability to distinguish between fatty liver and normal liver. More objective diagnostic means to improve the clinical diagnostic accuracy, efficiency and repeatability.hence provides a feasible means for the analysis of fatty liver disease quantitatively, and thus reduces the misdiagnosis caused by the subjective judgment difference. ROI selection is an important factor in the fatty liver diagnosis. Different ROI regions may lead to totally different diagnostic results. Future work will include how to get consistent diagnosis and quantitative analysis results from ultrasound images acquired on different B-scan devices, how to quantify ultrasound image characteristics and divide fatty liver into finer categories, how to select the most effective features from a large selection of ultrasound image features, and how to establish an effective man-machine interface, to improve the diagnostic efficiency and operability. ACKNOWLEDGMENT Sincere thanks to Dr. Rajesh, JSS Hospital Mysore for giving us necessary medical data and lending an excellent medical guidance, this work is possible only because of the suitable help, and meticulous encouragement of Shailaja K and Nanda.S, Associate professor, Dept. of IT, SJCE Mysore. REFERENCES [1] Computer aided diagnosis of fatty liver ultrasonic images based on support vector machine, Engineering In Medicine & Biology 2008,EMBS 2008 30 TH INTERNATIONAL CONFERENCE OF IEEE [2] W. C. Yeh, S.W. Huang, and P.C. Li. Liver fibrosis grade classification with B-mode ultrasound. Ultrasound in Med &Biol, vol. 9, pp. 1229-1235, 2003. [3] M.H. Horng, Y.N. Sun, and X.Z. Lin. Texture feature coding metho for classification of liver sonography, Com. Med. Imaging & Graphics, vol. 26, pp. 33-42, 2002. [4] M.H. Horng, Y.N. Sun, and X.Z. Lin. A diagnostic image system for assessing the severity of chronic liver disease. Proceedings of the
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