The system can be used as a second opinion system to aid the diagnosis of liver diseases. Key words-Ultrasound imaging, automatic liver diagnoses,.
Automatic Diagnosis of Liver Diseases from Ultrasound Images Abou zaid Sayed Abou zaid, Mohamed Waleed Fakhr, Ahmed Farag Ali Mohamed
The quantitative tissue characterization steps first, are to extract the features (parameters) from the returned signal, (pulse-echo data) and then analyze these features and correlating it to different Pathologies [3], [4]. Quantitative tissue characterization technique (QTCT) is gaining more acceptance and appreciation from the ultrasound diagnosis community. It has the potential to significantly assist sonographers to achieve better diagnostic rates. QTCT is based on extracting parameters from the returned ultrasound echoes for the purpose of identifying the type of tissue present in ultrasound scan plane. These parameters can be divided into two main categories according to their origin: 1) RF Signals parameters: extracted from the returned RF echoes prior to machine processing, attenuation and backscattering parameters. 2) Image texture parameters: extracted from the video image after the echo processing is performed in machine, such parameters include the statistical characteristics of the gray level distribution in certain region of interest (ROI) in the image. RF Signal parameter has the advantage of being free from any machine processing distortions, while the second has the advantage of being easier to implement [5], [6]. The major advantage of using computerized B-mode ultrasound is the possibility of obtaining tissue-specific parameters which are unattainable by visual assessment. These characteristics apply to changes of the tissue texture due to diffuse diseases of organs (e.g., cirrhosis) or caused by focal lesions. In both cases the changes are expressed relative to some standard display of the texture e.g. the normal echogram of the healthy organ. Brightness scale depends on echo strength [7]. The mean gray level physical meaning is the brightness or echogenicity of the texture, which most of the Sonographers write it in their ultrasound report. It is well established that in fatty (Steatosis) and cirrhosis liver classes, the echogenicity is higher, and sometimes they called this group as "bright liver" [8]. Figure 1 shows the ultrasound image for fatty (bright) liver which shows the higher echogenicity, but brightness is not sufficient to diagnose subjectively fatty liver from cirrhosis. Most of the fatty and Cirrhotic livers are Hyper echoic. Figure 2 shows the ultrasound image for the cirrhotic liver. Figure 3 shows the ultrasound image for cancer liver.
Abstract- Ultrasound is a widely used medical imaging technique. Tissue characterization with ultrasound has become important topic since computer facilities have been available for the analysis of ultrasound signals. Automatic liver tissue characterizations from ultrasonic scans have been long the concern of many researchers. Different techniques has been used ranging from processing the RF signals received by the transducer to using neural networks to analyze images based on image texture. In this paper, an automatic liver diseases diagnostic system is implemented for early detection of liver diseases. The proposed system classification accuracy is 96.125%. The system advantage is its high accuracy and its computation simplicity. The system can be used as a second opinion system to aid the diagnosis of liver diseases. Key words-Ultrasound imaging, automatic liver diagnoses, neural networks, quantitative tissue characterization.
I.
INTRODUCTION
Tissue characterization is a term that usually refers to the quantitative estimation of tissue or image features leading to a more accurate distinction of normal and abnormal tissues, the results of tissue characterization are quantitatively interpreted using numerical values. It aims to provide additional information about tissues not available by viewing ordinary images of the ultrasound B-scan. Thus, gained information are quantitative and is far less operator dependent than the usual B-scan images. Changes in tissue elasticity are generally correlated with pathological phenomena. Many cancers appear as extremely hard nodules which are a result of increased stormal density (collagen content). Other diseases involve fatty and/or collagenous deposits which increase or decrease tissue elasticity. Complicated fluid filled cysts could be invisible in standard ultrasound examination. Diffuse diseases such as cirrhosis are known to significantly reduce the elasticity of the liver, yet they appear normal in conventional ultrasound examinations. The Visual criteria provide low diagnostic accuracy around 70%. Therefore the physicians may have to use further invasive methods such as the pathology investigation of ultrasonically guided needle biopsy [1], [2]. Although this technique is considered to be the best test for diagnosis, it has the disadvantage of being invasive and risky, it may cause a great risk of cancer spread if it cuts through a localized cancer area.
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Figure 4 shows the ultrasound image for the normal liver, it is isoechoic (normal brightness). Figure 5 shows ultrasound images for some samples for the four liver cases. The gray-scale ultrasound images generated in the clinical environment provide significant contributions to the diagnosis of liver diseases. However, at the resolution capabilities practical for abdominal scanning, common diffuse diseases of the liver, such as hepatitis, fatty infiltrations, and early cirrhosis, are difficult to diagnose from normal by visual inspection of the B-mode [9]. Fatty liver containing very little fibrous tissue would produce a similar sonogram to that cirrhotic liver containing similar quantity of fibrous tissue making it difficult for a clinician to differentiate fatty from cirrhotic livers, so the specific texture on B-scan images is believed to be related to tissue properties, i.e., the pathological state of the soft tissue. Therefore, for classification, features can be extracted with the use of image texture analysis techniques. We propose the gray scale statistics for texture characterization. Neural networks [10, 11] provide computational techniques that are able to deal with our problem. The fundamental objective for neural networks pattern recognition systems is classifying an input to provide a meaningful categorization of its data content. A Pattern recognition system can be considered as a two stages device, the first stage is feature extraction, and the second is classification. Features are the measurements taken on the input pattern that is to be classified; typically we are looking for minimum features that will provide a definite characteristic of that input type.
Figure 3. Ultrasound image for cancer liver.
Figure 4. Ultrasound image for normal liver.
II. DATA AcQuisITION
The most spread and known diseases in our country such as fatty liver (steatosis), cirrhoses and carcinoma (cancer) are chosen to be our cases in addition to the normal case to be easy to compare the disorder ones with the normal case. Ultrasound images used in our research were obtained on [Toshiba ECCO CEE. and Toshiba SSA-100] with 3.5MHz.transducer frequency; Images were captured with 512 x 512 pixels and 256 gray-level resolution. Fasting Condition of the patients is substantial, it has been suggested that patients should be fasting for eight hours before any scan to avoid the effect of changing liver glycogen and water storage on ultrasound attenuation. Ultra sound images for different liver cases taken from patients with known histology and accurately diagnosed by specialized sonographer from various hospitals such as, department of tropical medicine and hepatology Cairo
Figure 1. Ultrasound image for bright liver.
University (Kaser El-aini Hospital), Al Moalemeen Hospital Gezira, and Charity Center for Liver Diseases and Researches - Nasser City, Altyseer Medical center and other clinical centers are the assistant to our database. Four sets of images have been taken: Normal, Fatty, Cirrhoses and cancer, for each case we have 160 samples from 80 subjects, 80 samples to find out the learning data for classification and 80 samples for testing each liver case, Except for Normal case we have 200 samples from 100 subjects, after discarding the false negative and false positive samples. From each image, two blocks of 64X64 pixels approximately 2cm x2cm in actual dimension have been selected. Blocks were chosen to include only liver tissue, without blood vessels, acoustic shadowing, or any type of distortion. We select 320 samples from 340 for training set
Figure 2. Ultrasound image for cirrhotic liver.
314
Normal liver
mage.
images [14]. Choosing the size of this region is not yet as simple. It should be chosen such that it will encompass all the ROI (e.g. lesion) and at the same time have enough number of pixels to provide a good statistical population. In the case of small lesions, this fixed size window allows pixels from the normal tissue, as well, to be considered in the calculations which will influence the measured features of the region.
Cirrhoses
Carcinoma Figure 5. Ultrasound image samples for liver cases.
B. Quantifying the Features of ROI The second step in lesion classification, is quantifying features of the region studied. Features used to characterize tissue include gray scale. In ultrasonography, Texture analyses are based on gray scale statistics. First- and secondorder statistics of the histogram [15], [16] as well as spectral analysis are computed and used to characterize tissue. In addition, attenuation estimates and scatter separation distance are used to calculate texture measures of the liver tissue. After the texture measures are obtained, a decision should be made on whether this tissue is normal or diseased and what kind of a disease it is.
and 320 samples from 340 for test set were composed out of all blocks from independent images. An important initial step is to divide the data set into two independent subsets, Train and Test sets. This preliminary step effectively avoids introducing false-negative or falsepositive. False-negative rate is the probability that the classification result indicates a normal liver while the true diagnosis is indeed is liver diseases i.e., positive. This case should be completely avoided. False-positive rate is the probability that the classification result indicates liver diseases while the true diagnosis is indeed a normal liver.
C. Making Decisions about the Nature of ROI Classifying objects or regions is based on neural network analysis; Samples from ultra sound images are taken, grouped and numbered according to each disease case. The following image features are extracted from each image: Mean gray level, Variance of graylevel, Skewness of graylevel and kurtosis. Neural network trained on these features to be ready for testing operation. The following subsections will discuss these features. 1) The Mean Gray Level: Most significant parameter used to build in tissue characterization is the mean gray level. It is the brightness or echogenicity of the texture, and is expressed mathematically as the average of the brightness of all Pixels of the sampled Ultrasound photo. Equation (1) is used to calculate the mean gray level.
III. PROPOSED SYSTEM
The proposed work is to determine the minimal set of features required to uniquely characterize each texture class, in image processing. Many techniques used to characterize tissue; our approach is divided into three steps: A. Defining the Region of Interest (ROI) Defining the ROI is one of the most important steps in characterizing the tissue since it is the basis for all the following steps. In order to accurately identify and quantify regions, they should be as free as possible from the effects of the imaging system. For example, their depth and location in the beam should be such that the effects of the side lobes and the beam diffraction, acoustic shadowing be a minimum [12], [13]. Blocks were chosen to include only liver tissue, without blood vessels. Several techniques were used in defining the ROI, the simplest being which we used, is the square shaped region that have a fixed size and shape and would be applied to all
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IV. RESULTS
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CLASSIFIER NETWORK
The utilized neural network as shown in figure 7 is a multi layer back propagation neural network with 4 inputs, 2 hidden layers. The first hidden layer contains 4 neurons. The second hidden layer contains 8 neurons, and the output layer contains 4 neurons [17-20]. The proposed network contains fewer neurons than a network with a single hidden layer that solve the same classification problem. The 4 inputs are the main features used to distinguish between the liver cases.
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TABLE I A COMPARISON BETWEEN THE MAIN FEATURES IN THE FOUR LIVER CASES
Features Mean gray level
from
to
Bright (fatty Liver) from to
from
to
from
to
0.2459
0.6357
0.5046
0.6719
0.3183
0.5066
0.2936
0.4986
Variance. Skewness Kurtoses
0.0003 -1.1154 2.5151
0.0108 1.6952 8.5028
0.0049 -0.4134 1.8692
0.0307 0.8338 3.3589
0.0023 0.9398 1.8752
0.0284 2.6815 11.3442
0.0027 -0.0857 2.5611
0.0188 1.7592 6.2869
Liver Cases
Normal Liver
Figure 12. Features for all liver cases.
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Cirrhoses
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TABLE IV CLASSIFICATION RESULTS AFTER DISCARD THE MISCLASSIFIED CASES
V. CLASSIFATION RESULTS
In data acquisition stage, the misclassified % cases are the False-negative and false-positive, false-negative means that the case is liver disease but the test statement is normal and vice versa in false-positive. Some of normal cases come out bright and some of cirrhoses cases come out carcinoma and vice versa, also some of bright cases come out normal. This results due to incorrect selection for the ROI. After discard the misclassified cases (false-negative and false-positive), another misclassification present due to some likeness and convergence between some features. TABLE II
Samples
Misclassified cases
%
Normal
100
4
Bright
80
Four cases comes out Bright Four cases comes out
Cirrhoses
80
Cancer. Two cases come out Bright.
7.5
Cancer
Normal Three cases come out
One case comes out normal Three cases come out Cirrhoses. One case comes out
80
5
Normal
100
Bright
80
Three cases comes out Bright Two cases comes out Normal
Cirrhoses
80
Cancer.
80
One case comes out Cancer. One case comes out Bright. One case comes out Normal Three cases come out Cirrhoses. One case comes out Normal One case comes out Bright
Overall Classification accuracy %
Train
100%
100%
98.75%
96.25
98. 25%
Test
97.0%
97.5%
96.25%
93.75
96.125%
D. Gray Level Concurrence (GLC) The GLC matrices were formed for all combination of
TABLE III
MISCLASSIFICATION / TOTAL RESULTS IN TESTING GROUP
misclassified cases
Overall%
C. Fractal Feature Vector Based on M- Band Wavelet Transform in distinction between two liver cases Ultrasonic liver tissues classifications by Fractal Feature Vector based on M-band wavelet transform to classify ultrasonic liver images. The proposed feature extraction algorithm is based on the spatial-frequency decomposition and fractal geometry [23].
Table 2 shows the misclassification results in data acquisition stage, before discarding the false-negative and false-positive). Table 3 shows the misclassification results in the Test stage, the overall Classification accuracy is about 96.25% Carcinoma Liver having the minimum for gray level and middle values between bright and cirrhoses liver for both skewness and kurtosis.
Total
Cancer
B. Wavelet Transform Another approach is using separable or nonseparable wavelet transform in visually similar diffuse diseases from B-Scan for characterization of noisy data [21,22]. Wavelet Transform Systems having the better and excellent diagnosis results if it works on large images liver samples.
6.25
Overall Classification accuracy =94.3 125 %
Number
Cirrhosis
A. Region Growing Another approach is processing the RF signals received by the transducer by neural networks to analyze images based on image texture and proposed models for attenuation and scattering of ultrasound waves. The ROI is determined by a region growing procedure based on gray level difference between a seed point and other points in ROI [14]. In region growing figure 13, a pixel in the region is used as a seed point and then all the similar pixels (spatially close pixels sharing the same features, i.e. neighbors) are gathered together in the same region.
Normal. One case comes out Bright.
Liver case
Bright
VI. COMPARISON WITH OTHER APPROACHES USED IN AUTOMATIC DIAGNOSIS OF LIVER DISEASES
MISCLASSIFICATION RESULTS IN DATA ACQUISITION STAGE
Liver cases
Normal
displacements angles.
Overall% 3% 2.5%
3.75%
6.25 %
ng.
96.125
318
Characterization of Different liver pathologies preliminiary Results", Kaser Elaini Midical Congress Conf.1995. [4] Ultrasonic tissue characterization. IEEE.Trans. Ultrasound Ferroelectric. Freq. Control. 2001 Jul; 48 (4):1139-46.PMID: 11477773[PubMed-indexed for MEDLINE]. [5] Liver Health Information. The American Liver- Foundation. Online: [www.liver foundation.org/cgi- bin/dbs/articles. cgi? db=articles&uid=default], 2005 [6] Liver diseases & CellBiology, Online: [http://www.otterbein.edu/home/fac/wndnjhns/NOTES3.HTML], 2005. [7] Diseases of the Liver. [www.ability.org.uk/Liver_Diseases.html], 2005. Online: [www.cancerindex.org/clinks2m.htm] ,2005. [www.cancerindex.org/geneweb/X070601.htm], 2005. [8] Diffuse Liver Diseases Online. [www.asianradiology.com/sample-page/sampl-chap25.pdf], 2005. [9] C.Wu, Y. Chen, and K. Hsieh, "Texture features for classification of ultrasonic liver images, "IEEE Trans. Med. Imag., vol. 11, pp. 141152, june 1992. [10] Training an Artificial Neural Network http://www.dacs.dtic.mil/techs/neural/neural3.html [11] "An automatic diagnostic system for liver image classification in Artificial Neural Network in Biomedicine". Online:
TABLE V COMPARISON WITH OTHER APPROACHES USED IN AUTOMATIC DIAGNOSIS OF LIVER DISEASES
Classification
Overall%
I
Gray scale statistics.
96.125%
2
Fractal Feature vector based on M- Band Wavelet Transform in distinction between two liver cases.
3
2-a. Between Normal and abnormal. 2-b. Between Cirrhoses and Hepatoma. Wavelet Transform using Separable or Non separable Wavelet Transform.
96.7% 93.6% 92%
4
Fuzzy logic algorithm.
90%
5 6
Gray-level Concurrence (GLC). Region growing techniques.
87% 86%
7
Fourier measure.
82%
[www.medinfo.cs.ucy.ac.cy/documents/medinf 03_NeuralNetworksM edicallmaging.Pdf.], 2005. [12] David J. Dowsett, Patrick A. Kenny and R. Eugene Johnston. The Physics of Diagnostic Imaging. Published by Chapman & Hall, an imprint of Thomson Science, 1998. [13] Rafael C. Gonzalez and Richard E. Woods. Digital Image Processing. Chapman & Hall Medical, New York Don Mills, 1993. [14] Liver diseases characterization, pubMed. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retriev&db=pub Med&list_uids ...25/02/1427 [15] Yasser M. Kadah, Aly A. Farag, Jacek M. Zurada, Ahmed M. Badawi, and Abu-Bakr M. Youssef, Classification Algorithms for Quantitative Tissue Characterization of Diffused Liver Disease from Ultrasound Images, IEEE Transactions on Medical Imaging, volume 15, No. 4, August 1996, pp. 466-478. [16] Y.M. Kadah, A.A. Farag, M. Zurada, A. M. Badawi,and A.M. Youssef, "Classification algorithms for quantitative tissue characterization of diffuse liver diseases from ultrasound images," IEEE Trans. Med. Image., col. 15, no. 4, pp. 466-477, aug. 1996. [17] The Back Propagation Network Learning by Example. http://www2.psy.uq.edu.au/-brainwav/Manual/BackProp.html# Learning Problem. [18] H. Sujana, S. Swarnamani, and S. Suresh, Application of Artificial Neural Networks for the Classification of Liver Lesions by Image Texture parameters, Ultrasound in Medicine and Biology, volume 22, No. 9, 1996, pp. 1177-1181. [19] Jacek M. Zurada. Artifical Neural Systeems. PWS Publishing Company, United States of America, 1999. [20] An Introduction to Neural Networks. Online: [www.cs.stir.ac.uk/!lss/NNIntro/InvSlides.html],2005. [21] M. Unser, "Texture classification and segmentation using wavelet fraMES, 'IEEE Tran. Image Processing. Vol. 4, pp. 1549-1569, Nov. 1995. [22] Wavelet-Based Texture Classification of Tissues in Computed Tomography [http://doi.ieeecomputersociety.org/10.1109/CBMS]. 2005.105 [23] Characterization, based on nonseparable wavelet decomposition and its application for the discrimination of visually similar. Diffuse diseases of liver. www.research.ibm.com/people/a/aleksand/pdf/00730399.pdf
E. Fourier Measure In which Fourier power spectrum based features are used to classify types of liver tissues.
F. Fuzzy Logic Algorithm for Quantitative Tissue Characterization of Diffuse Liver Diseases from Ultra Sound Images Fuzzy logic has emerged as one of the most active area in classification using numerical quantitative features measured from the ultrasound images. Fuzzy rules were generated from liver cases representing different liver diseases. The output of the fuzzy system is one of the categories, the step done for differentiating the pathologies are data acquiring and feature extraction, dividing the input space of the measured quantitative data into fuzzy sets. Based on expert knowledge. The fuzzy rules are generated and applied using the fuzzy inference producers to determine the
pathology.
VII. CONCLUSION
An automatic liver diseases diagnosis system in our approach was proposed. The accuracy of the system is 96.125 %. The system can be used in diagnoses process, together with history information, laboratory, and clinical examinations as a second opinion system to aid the diagnosis of liver diseases. Wavelet Transform Systems having the better and excellent diagnosis results if it works on large images liver samples for different liver cases. REFERENCES [1] Detect Liver Disease Early -Online: [www.vitalimaging.co.uk], 2005. [2] Karl Wernecke, Pierre Vassallo, Ulrich Bick, Stefan Diederich, Peter E. Peters, The Distinction between Benign and Malignant Liver Tumors on Sonography: Value of a Hypoechoic Halo, American Journal of Roentgenology, volume 159(5), Nov.1992, pp. 1005-9 [3] A-M. Badawi, A. M. Hashem, Ali El Hendawi, Nabil El Kady, AbouBakr M. Youssef, M. F. Abdelwahab, "Ultrasonographic Tissue
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