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50120130405018

  1. 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 5, September – October (2013), pp. 155-164 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com IJCET ©IAEME SUBJECTIVE QUALITY ASSESSMENT OF NEW MEDICAL IMAGE DATABASE Asst. Prof. Jameelah H. Suad1, Wurood A. Jbara2 1 2 Computer Science, College of Science, AL-Mustansiriyah University, Iraq Computer Science, College of Science, AL-Mustansiriyah University, Iraq ABSTRACT A new medical image database is created in order to perform the subjective evaluation to obtain the Mean Opinion Score (MOS). Our database contains 100 test medical images (20 reference images and 5 types of distortions for each reference image). The MOS value for this database has been obtained as a result of 15 doctors. Observers doctors are specialists in electronic medical diagnosis carried out about 1650 individual human quality judgments. The collected MOS can be used for test the effectiveness performance of different visual quality metrics as well as for the design of new metrics. Also, the designed medical image database provides a lot of samples that can be used in the tested the efficiency of the numerical observer model to evaluate the medical image quality in the same way that consistent with human perception significantly. Keywords: Subjective evaluation, Mean opinion score, Visual quality, Numerical observer. I. INTRODUCTION The evaluation of image quality is important for many image processing systems, such as those for acquisition, compression, restoration, enhancement, reproduction etc. For instance, the goal of image compression is to reduce the amount of data required to store an image while at the same time it is ensured the results are of good quality enough; in image enhancement systems, final images should be of better visual quality than the originals; and taking into account current communication networks, their images are transported by channels that introduce errors, thus they should be evaluated to ensure can be worked with the final images they have transported [1]. The goal of image quality assessment (IQA) research is to design algorithms for objective evaluation of quality in a way that is consistent with subjective human evaluation. By “consistent” the algorithm’s assessments of quality should be in close agreement with human judgements, 155
  2. 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME regardless of the type of distortion corrupting the image, the content of the image, or strength of the distortion [2]. The evaluation of image quality may be divided into two classes, subjective and objective methods. Intuitively one can say that the best judge of quality is the human himself. That is why subjective methods are said to be the most precise measures of perceptual quality and to date subjective experiments are the only widely recognized method of judging perceived quality [3]. In these experiments humans are involved who have to vote for the quality of an image in a controlled test environment. This can be done by simply providing a distorted image of which the quality has to be evaluated by the subject. Another way is to additionally provide a reference image which the subject can use to determine the relative quality of the distorted image[4]. Mostly commonly, the mean opinion scores (MOS) is used where the individual participant’s scores are averaged to level out individual factors. Popular subjective evaluation methods useful for image quality evaluation are Double Stimulus Continuous Quality Scale (DSCQS),Double Stimulus Impairment Scale (DSIS), Single Stimulus Method (SSM) and Two Alternative Forced Choice (TAFC). However, the subjective methods are normally difficult to implement, expensive, timeconsuming, and impractical in many cases [5]. Another class is objective method, these are automatic algorithms for quality assessment that could analyses images and report their quality without human involvement. Such methods could eliminate the need for expensive subjective studies. Objective image quality assessment research aims to design quality measures that can automatically predict perceived image quality. The most widely used metrics are mean squared error (MSE), Signal-to-Noise Ratio (SNR), and Peak Signalto-Noise Ratio (PSNR), which exhibit weak performances that has not been in agreement with perceived quality assessment based on subjective test [6]. In this paper, we present a new medical image database, and then invite a group of specialist doctors in the e-diagnosis to assess the quality of medical images in the database that was created. The paper is structured as follows. Section 2 presents the benefits of designed medical image database. The design of new medical image database present in Section 3. Section 4 shows our experimental results. And finally Section 5 provides conclusions. II. BENEFITS OF NEW MEDICAL IMAGE DATABASE Recently the scientific community has done great efforts to develop and test image and video quality assessment methods incorporating perceptual measures. Many of the quality metrics proposed were based on properties of human vision system (HVS) [7]. However, till now there are no such quality metrics that are able to take into account all peculiarities of HVS. There are several reasons for that. First, HVS is not well understood yet. Second, it is not clear how to model all possible image distortion types and levels. Third, people use different image databases to carry out testing of existing and new quality metrics. Fourth, experiments with appropriate number of volunteers are needed for assessment of image visual quality and reliable testing of the image visual quality metrics [8]. Conditions to carry out experiments, methodology to process the results of these experiments, what is a necessary number of participants, etc., are other questions yet to be answered [7]. Moreover, assessing the quality of medical images is a great challenge because of the lack of available databases dedicated to medical images in order to use in the design of quality model to assess the quality of medical images. So we have been forced to created a medical image database that allows to alleviate some of shortcomings mentioned above and to make a comparison of different quality metrics integrally for particular groups (subsets) of distortion types. Also, test the new numerical observer model to evaluate the medical image quality. 156
  3. 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME This database provides, among others, the following opportunities: 1) It allows testing new quality metrics (if their implementation codes are available); 2) The database provides already MOS for medical images; 3) It is possible to consider applicability of quality metrics for particular applications by grouping experiment data for a given type of distortion(s) and analyzing correlation coefficients for the tested quality metrics; 4) The database allows determining types of distortions for which a given quality metric performs poorly, thus showing its drawbacks and, probably, indicating what should be taken into account for improvement of metric’s performance. III. DESIGN OF NEW MEDICAL IMAGE DATABASE The main goal of medical image database is to provide collection of reference medical images and their distorted images in order to conduct the subjective evaluation procedure intended for quality assessment of medical image and then use the results in design new numerical observer model to evaluate the medical image quality. Also, the obtained results can be used in the future to test and verification of the performance efficiency for the numerical observer model. The main steps of subjective evaluation are shown in Fig.1: Medical Reference image Images DB Different Types of Distortions and Noise Distorted images Subjective Evaluation by Averaging Opinions of many Observers(Doctors) on Image Visual Quality Compute the MOS Values Array of MOS values Fig.1: Steps of subjective evaluation 3.1 Medical Image Database The quality of any image database strictly depends on the reference images that are used. The main strategy is to select the medical images that represent a wide variety of cases of disease. That is, the images in the database should present different textural characteristics, various percentage of homogeneous regions, edges, and details. Therefore, we borrowed a large number of reference medical images from Baghdad Central Hospital. These images are captured using the magnetic 157
  4. 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME resonance technique. The Fig.2.shown the 20 reference medical images which are used in this database. These images has different cases and objects with various characteristics. Fig.2: Reference medical images of our database The size of each reference image is 512×512 pixels. The motivation of including these images into our database was to provide adequate testing for numerical model intended to assess the medical image quality. 3.2Distortions and Noise We aim to have the medical image database contain a wide variety of distorted images that are generated by using different types of distortions and noise which are based on various fields. In this database we consider five types of distortions and noise that are important for the most intensively used and studied image processing applications. Table(1) presents the distortions and noise which are modeled in our image database. 158
  5. 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME No. Type 1 Type 2 Type 3 Type 4 Type 5 Table (1): Distortion and noise types. Correspondence to practical Type of distortion situation Additive Gaussian Image acquisition, Image noise transmission Blurring Filtering JPEG compression Image compression Image acquisition, Image Salt and pepper transmission Sharpness Image Enhancement As mentioned in Table 1 there are five different types of distortions are: 1. Additive noise is often present in images [9] and it is commonly modeled as a white Gaussian noise. This type of distortion is included in most of studies of quality metric effectiveness. This type of distortion is, probably, one of few cases when metrics MSE and PSNR present a good match with the HVS. 2. blurring is also considered in the proposed database since it is an important type of distortions often met in practical applications and frequently included in studies dealing with visual quality metrics. This type produced due to filtering process [2]. 3. Image compression is an application widely used in medical image, because medical image usually compressed before being transferred to the other side during e-diagnosis process. Therefore, images distorted with lossy compression (JPEG) have been included into our database. The tasks of evaluating distortions for lossy image compression techniques are of great interest. Besides, we have included into our database the images compressed by JPEG [10]. 4. Impulse noise (Salt and pepper ), we have used a typically used model of uniformly distributed impulse noise arises, in particular, due to coding/decoding errors in data transmission [11].The presence of images affected by impulse noise in the database is necessary. This might assist to adequately evaluate effectiveness of proposed quality model. 5. Finally, we have added into our database the sharpness images. Sharpness is arguably the most important medical image quality factor because it determines the amount of detail an imaging system can reproduce. Sharpness is defined by the boundaries between zones of different pattern [11]. 3.3 Subjective Evaluation In this section, the main goal to use subjective evaluation procedure in order to assess the quality of medical images in our database, and then obtain the MOS scores that are produced by Doctor observers. The subjective evaluation procedure is a widely used for the assessment of image or video quality, but it has several obvious disadvantages. It is very tedious, time consume, expensive and impossible to be executed automatically. The subjective evaluation methods are divided into three primary categories: the first is Single Stimulus Impairment Scale (SSIS); the second is the Double Stimulus Impairment Scale (DSIS) and finally the Double Stimulus Continuous Quality Scale (DSCQS). All of these methods were based on ITU-R Recommendation (International Telecommunications Union) [12]. The DSIS method is better suited for assessing clearly visible impairments, such as distortions caused by compression or transmission errors. Therefore, we use a DSIS procedure to assess the quality of the medical images within our database. 159
  6. 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME The DSIS operates on the five level impairment grading. The reference image is always shown with the distorted one. Assessment of the images quality refers to the distortion level, not the absolute image quality. This scale is commonly known as the 5-dgree scale, where 5 equals the imperceptible level of impairment and 1 equals the very annoying level as explained in Table (2). Table (2):The DSIS Scale Quality 5 Excellent 4 Good 3 Fair 2 Poor 1 Bad Five-degree scale Impairment 5 Imperceptible 4 Perceptible, but not annoying 3 Slightly annoying 2 Annoying 1 Very annoying A. Setting of Subjective Test At the starting of subjective test, we must tack in consideration the ITU-T recommendations in order to doing the subjective test in suitable environment [13].Here, we summarize the recommendations of ITU regarding the medical image evaluation: 1. Subjects Number: As stated in ITU recommendations, the number of subjects required to perform the subjective quality experiment can vary from (5 to 35). The typically number of subjects is about 15. They should have normal vision, and should preferably be expert in diagnosis of medical image. 2. Information for Observers: Before carrying out the experiments some information should be given to the observers. These information consist of type of evaluation approach, the types of distortion, the ranking scale, duration of test, ..etc. This information should be explained and given to the observers in document form. The observer should be trained with the training trials to familiarize them with the mission they will carry out. 3. Environment: Specifications have been established for the room environment, ambient lighting conditions and viewing distance. 4. Images Display: Randomness in images display is preferred. Our subjective test was performed in suitable medical environment that meets all the standards of subjective evaluation at medical laboratory of Baghdad Central Hospital with fifteen of specialist Doctors in e-diagnosis. B. Conduct DSIS Protocol The DSIS protocol is used to produce the MOS values for all distorted medical images in created database. Thus, a group of Doctor observers are invited to judge the quality of the distorted medical image. The DSIS needs that number of (typically 15) observers with good experiences in ediagnosis for medical image and the observers must take training part in order to visual adaptation. In the training part, observers spend about 10-15 minutes in order to adapt with the specialized lighting conditions. Later, they are by showed the reference and distorted medical images. In order to that the observer do not suffer from tiredness during the test period, we have designed a graphical user interface in order to make the evaluation process easy and require no computer skills from observers. The graphical user interface was designed on a PC running Windows 7, using the Visual Basic 6.0 with Microsoft Access. The graphical user interface during the subjective testis shown in Fig.3. 160
  7. 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME At the starting of the experiment, the observers enters his/her name and then the multiple pairs of reference and distorted medical images are showed with the question "what is the impairment average of reference and distorted medical image". Fig.3: The graphical user interface for DSIS test. C. MOS Computation After completing the subjective test, the observers provided their opinions in the linguistic terms such as (excellent, good...etc.). These terms must be collected and converted to a numerical style in order to be analyzed and give accurate estimation for the quality of distorted medical images. ~ The MOS ukj is computed for each distorted medical image as shown below: 1 ~ u kj = N N ∑u ... (1) ikj i =1 Where N represents the observer’s number, uikj is the score of the observer i of the distorted image k for the distortion type j. IV. RESULTS AND DISCUSSION The subjective experiments using DSIS protocol have been conducted in order to obtain the MOS scores from the observers (Doctors)for all distorted medical images in our database. After the observer gives his/her opinion, the resultant value is stored in the data file of database. Table (3) shows the data file database containing values that produced by observers for two reference medical image and their distorted versions. Table (3): The data file Images 1_bluer 1_Gauss 1_JPEG com 1_Sharp 1_S&P 2_bluer 2_Gauss 2_JPEG com 2_Sharp 2_S&P Distortion Bluer Gaussian JPEG comp. Sharpness Salt & pepper Bluer Gaussian JPEG comp. Sharpness Salt & pepper Dr Ali Dr Mayada M Dr Ayad Dr Mayada J Dr Aymen Dr Ayam Dr Alia W. Dr Wasan Dr Salim Dr Ahmed Dr Sura Dr Mohamed Dr Marwan Dr Alaa K. Dr Rsual 3 3 4 4 4 3 3 3 2 3 4 4 3 3 3 1 1 2 2 1 2 1 1 1 1 2 1 1 1 1 3 4 5 5 4 4 3 4 3 4 5 4 4 4 3 4 4 5 5 5 5 4 5 4 4 5 5 4 5 4 1 1 2 1 2 1 1 2 1 2 2 1 1 1 1 2 3 3 4 3 3 3 3 3 3 4 3 3 3 3 1 1 1 1 2 1 2 2 1 1 1 1 1 1 1 4 4 4 4 5 3 3 3 4 3 5 5 4 5 4 5 5 5 5 5 4 5 5 4 4 5 5 5 5 5 1 2 2 2 2 1 1 1 1 2 2 2 1 2 1 161
  8. 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME The Fig. 3 explained the summary of results for all distorted medical images with five different distortions and noise. 5 4 Salt & pepper Sharpness 3 JPEG comp. Gaussian 2 Bluer 1 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Fig.4: Summary of results After a subjective evaluation is completed, the MOS scores will be computed. Then, the computed MOS scores for two examples are stored in the new table which is showed in Table (4). Table (4):MOS scores Images 1_bluer 1_Gauss 1_JPEG com 1_Sharp 1_S&P 2_bluer 2_Gauss 2_JPEG com 2_Sharp 2_S&P Distortion MOS Bluer 3.2666667 Gaussian 1.2666667 JPEG comp. 3.9333333 Sharpness 4.5333333 Salt & pepper 1.3333333 Bluer 3.0666667 Gaussian 1.2 JPEG comp. 4 Sharpness 4.8 Salt & pepper 1.5333333 MOS scores for all distorted medical image are completed, we analyze the effect of each distortion type on each reference medical image. Then, the overall effect of each distortion type is obtained. The Table (5) presented the of each distortion type on each reference medical image with the overall average. 162
  9. 9. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME Table (5): The effect of each distortion type for all medical image im 1 im 2 im 3 im 4 im 5 im 6 im 7 im 8 im 9 im 10 im 11 im 12 im 13 im 14 im 15 im 16 im 17 im 18 im 19 im 20 Ratio= Bluer 3.26667 3.06667 3.13333 3 2.93333 2.8 3.33333 3 3.06667 2.46667 2.6 2.73333 2.26667 2.73333 2.26667 2.6 2.73333 3.2 2.2 2.2 2.78 Gaussian 1.266667 1.2 1.133333 1.4 1.466667 1.6 1.5333 1.266667 1.6 1.266667 1.33333 1.266667 1.2 1.133333 1.466667 1.466667 1.533333 1.6 1.466667 1.333333 1.376665 JPEG comp. Sharpness 3.93333333 4.5333333 4 4.8666667 4.13333333 4.4666667 4.13333333 4.7333333 4.2 4.5333333 3.93333333 4.4 4.06666667 4.2 3.86666667 4.333 3.66666667 4.2666667 3.86666667 3.9333333 3.46666667 4.2 3.53333333 4.2666667 3.53333333 4.4666667 3.6 4.4 4 4.73 3.73333333 4.477 3.66666667 4.4 4.06666667 4.466 3.6 4.2666667 3.33333333 4.2 3.81666667 4.4069667 Salt & pepper 1.333333333 1.533333333 1.466666667 1.6 1.533333333 2 2.066666667 1.733333333 1.533333333 1.4 1.333333333 1.8 1.466666667 1.733333333 1.533333333 1.866666667 1.8 1.933333333 1.533333333 1.4 1.63 As noted results of effect in Table 5, clear the Sharpness have the highest quality ratio. The second best quality is Compression followed by the Blurring, and Salt& pepper respectively. The Gaussian noise have poorest quality. V. CONCLUSIONS In this paper, we have created the new medical image database which is contains many of a distorted images with different types of distortion and perform subjective evaluation to obtain the MOS values for each distorted image. This database represents an encouraging step towards the design and testing of dedicated numerical models to assess the quality of medical images. Also, this database provide wide variety of distorted medical images in order to analyze the suitability of many known image visual quality metrics for the measure of medical image quality. For future work, we plan to extended the database with provide the different types of distortion and noise. VI. REFERENCES [1] Mireia C. C., Channelized hotelling observer optimization for medical image quality assessment in lesion detection tasks, Technical Report for the degree of Superior Telecommunication Engineering, Illinois Institute of Technology, Chicago-USA, July 2011. [2] Sheikh H.R., Sabir M.F., Bovik A.C., A statistical evaluation of recent full reference image quality assessment algorithms, IEEE Transactions on Image Processing, vol.15, 2006, pp. 3440 – 3451. [3] B. Alexandre, P. Le Callet, C. Patrizio, and C. Romain, Quality assessment of stereoscopic images, EURASIP Journal on Image and Video Processing, 2009. 163
  10. 10. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME [4] M. Carnec, P. Le Callet, and D. Barba, Objective quality assessment of color images based on a generic perceptual reduced reference, Signal Processing: Image Communication, vol. 23(4), 2008, pp. 239–256. [5] Sheikh, H.R.; Bovik A.C., Information theoretic approaches to image quality assessment, In: Bovik, A.C. Handbook of Image and Video Processing. Elsevier, 2005. [6] Damon M. Chandler, Seven challenges in image quality assessment: past, present, and future research, Journal of ISRN Signal Processing, vol. 2013, Article ID 905685, 2013, pp. 53-68. [7] N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, F. Battisti, TID2008 - A database for evaluation of full-reference visual quality assessment metrics, Advances of Modern Radio electronics, vol. 10, 2009, pp. 30-45. [8] M. S. Landy and N. Graham, Visual perception of texture, in The Visual Neurosciences, MIT Press, 2004, pp. 1106–1118. [9] K. Barner and G. Arce, Nonlinear signal and image processing: theory, methods, and applications, Electrical Engineering & Applied Signal Processing Series, CRC Press, ISBN10: 0849314275, 2003. [10] P.Gastaldo, G.Parodi, J.Redi and R.Zunino, No-reference quality assessment of JPEG images by using CBP neural networks, Artificial Neural Networks – ICANN, vol. 4669, 2007, pp.564-572. [11] K.N. Plataniotis and A.N. Venetsanopoulos, Color image processing and applications, Springer Verlag, ISBN 3-540-66953-1, 2000. [12] ITU-T Recommendation (P.910), Subjective video quality assessment methods for multimedia applications, 2000. [13] AmelaSadagic, Ben Teitelbaum, Dr. Jason Leigh, Prof. Magda El Zarki, Haining Liu, Measuring the quality of video, Computer Science Department, University College London, 2002. [14] R. Edbert Rajan and Dr.K.Prasadh, “Spatial and Hierarchical Feature Extraction Based on Sift for Medical Images”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 2, 2012, pp. 308 - 322, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [15] Shipra Gupta and Chirag Sharma, “A Novel Technique in Spiht for Medical Image Compression”, International Journal of Graphics and Multimedia (IJGM), Volume 4, Issue 1, 2013, pp. 1 - 8, ISSN Print: 0976 – 6448, ISSN Online: 0976 –6456. [16] John Blesswin, Rema and Jenifer Joselin, “A Self Recovery Approach using Halftone Images for Medical Imagery System”, International Journal of Computer Engineering & Technology (IJCET), Volume 1, Issue 2, 2010, pp. 133 - 146, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 164

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