ANALYSIS OF TRABECULAR BONE OF FEMUR RADIOGRAPHS FOR DETECTING OSTEOPOROSIS USING MWT AND DWT

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1 Volume 119 No , ISSN: (on-line version) url: ANALYSIS OF TRABECULAR BONE OF FEMUR RADIOGRAPHS FOR DETECTING OSTEOPOROSIS USING MWT AND DWT Shankar N 1, Dr Sathish Babu 2, Dr RajMohan V 3, Allwyn Gnanadas A 4 1 Assistant Professor, Department of Biomedical Engineering, Saveetha Engineering College,Chennai 2 Assistant Professor, Department of Electronics and Instrumentation, Annamalai University,Chidabaram 3 Professor, Department of Electronics and Communication Engineering, Saveetha Engineering College,Chennai. 4 Assistant Professor, Department of Biomedical Engineering, KPR Institute of Engineering and Technology. Corresponding Author : Bmeshankar@gmail.com, Phone Number ABSTRACT: With minimum force the bone will get fracture due to osteoporosis. Osteoporosis occurs due to loss of minerals in the bone. It affects irrespective of genders i.e women and elderly peoples. T-score and Z-score and BMD (Bone Mineral Density) is measured through Dual Energy X-ray absorptiometry (DXA). Anisotropic Morlet Wavelet transform and Discrete wavelet Transform is used to calculate energy at various regions of femur bone especially in neck, head and trochanter region.a Camp was conducted at private centre. This study was performed on 50 Indian womens. A sum of 50 Indian women (n=50), 18 young women with an average age of 36.3 ± 8.7 years and 32 postmenopausal women with an average age of 58 ± 9.1 years their age varies from years was incorporated. The vitality(energy) processed from trabecular patterns of ordinary bone by trabecular improved radiographic images had all the earmarks of being extent than the vitality from tests of the affected groups in more greater trochanter and neck regions. Limitation of study is no womens has osteoporotic fracture. Keywords/ Index Term proximal femur, DEXA, women,osteoporosis,anisotropic Filter, Morlet Wavelet Transform, Wavelet Transform, BMD. INTRODUCTION: With minimum force the bone will get fracture due to osteoporosis. Osteoporosis occurs due to loss of minerals in the bone. It affects irrespective of genders i.e women and elderly peoples [1]. In general population the quantity of individuals suffering by osteoporotic is increasing day by day. In India, Currently 6 million(5.5%) people are suffering from osteoporosis and every year it is get increasing by 2.3 million (0.2%) of total population. In India, the ratio of women and men is suffering from osteoporosis is 3:8, (1 women out of 3 and 1 men out of 8) [2]. Osteoporosis is loss of minerals in the bone, results in poor mechanical support of normal activity. Due to this non-traumatic fracture may occur[3]. Risk 2129

2 of bone fracture can be predicted through BMD (Bone Mineral Density). The bone strength is correlated by BMD. Its prescient ability is tantamount in its size to that of circulatory pressure for stroke and superior to that of serum cholesterol for coronary diseases. [4]. The prevision of osteoporotic cracks with resulting decreases in medicinal services expenses and excess morbidity and mortality is an important clinical goal. The significance of identifying patients with osteoporosis before the break is all around perceived, as there are a few medications for anticipation [5]. There are several methods (quantitative and semiquantitative) available to measure BMD or bone mass from simple digital radiographs to latest photon techniques. The difference in these is techniques is not only from source of energy, it also differ from skeletal site and type of bone were BMD is going to measure[6]. Dual Energy X-ray absorptiometry (DXA) is considered as gold standard for measuring BMD. DEXA will produce low precision error and high accuracy, the major disadvantage of DXA is highly expensive and availability of scan is less in scan centres [7]. Fig 1 shows the complex three dimensional Trabecular structure of proximal femur. Fig 1.trabecular Structure of Proximal Femur. MATERIALS AND METHODS: In this paper, In digital radiograph of 50 Indian womens, four different Trabecular pattern was recorded. The recorded image is enhanced after removing the noise in the images. Morlet anisotropic wavelet transform is used to extract the features from enhanced images. The texture feature is extracted from Morlet wavelet by energy computation and is then compared with predetermined trabecular energy computation. Sapthagirivasan et al calculated the 2130

3 energy from the same region of interest. The energy calculated from Morlet wavelet transform and Gabor wavelet transform is compared. Fig 2 shows the results of Energy calculation of femur in the Region of Interest (ROI) in digital radiograph using Morlet wavelet transform. The following are the region of interest Neck, Head, Ward triangle and trochanter. Fig 3 shows the results of Energy calculation of femur in the Region of Interest (ROI) in digital radiograph using wavelet transform. Fig 4 shows the original ROI and Enhanced ROI. Fig 2 Marked region of Interest (ROI) of 30 years old Premenopausal women using Morlet wavelet Transform. 2131

4 Fig 3 Right proximal femur with various ROl using Gabor wavelet Transform. 3.1 Preprocessing using Morlet Wavelet Transform: For enhancing the picture nature of surface ansiotropic channel is used.formally, anisotropic dissemination might be a strong picture enhancer. Anisotropic dissemination is thought to smooth the flag, protect tough edges, and upgrade the complexity of edges. anisotropic dissemination, likewise alluded to as Perona Malik diffusion[8]-[10] is a procedure going for decreasing picture commotion without evacuating essential components of the picture content, generally edges, lines or diverse subtle elements that are key for the translation of the image [9].anisotropic diffusion[11] is delineated as =div(c(x,y,t) I=) c. ) I+c(x,y,t) ) I (1) The diffusion coefficientis given by: C ( І ) =exp [-( /k) 2] (2) 2132

5 Fig 4 a) Original right proximal femur b) marked ROI c) Enhanced ROI d) ROI of femur head e) ROI Enhanced of femur head. Table 1. Calculated Energy using Morlet Wavelet Transform of Original ROI Classification Energy (Vitality) calculated in proximal femur of original images Normal Subjects Femur Neck Femur Head Lower Trochanter Low (A1) High (A4) Low High Low High (A1) (A4) (A1) (A4) 0.65± ± ± ± 0.64 ± 0.55 ± Higher trochanter Low High (A4) (A1) 0.51 ± 0.56 ± Osteopenia 0.6 ± ± ± 0.56 ± 0.51 ± 0.58 ± 0.54 ± 0.58 ± Osteoporosis 0.6± ± ± 0.5± 0.6 ± 0.51 ± 0.6± ± Table 1 gives the details about the calculated energy using Morlet wavelet for original region of interest for all classified groups.. Table 2. Calculated Energy using Morlet Wavelet of Enhanced ROI Classificati on Normal Osteopenia Osteoporosi s Energy calculated in proximal femur of enhanced images Femur Neck Femur Head Lower Trochanter Higher trochanter Low (A1) High (A4) Low (A1) High (A4) Low (A1) High (A4) Low (A1) High (A4) 0.39 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

6 Subjects Table 2 gives the details about the calculated energy using Morlet wavelet for enhanced region of interest for all classified groups. DISCRETE WAVELET TRANSFORM: In the work [12], the authors used radiograph of 41 patients to record four different trabecular pattern. The texture feature is extracted from wavelet transform by energy computation and is then compared with predetermined trabecular energy computation. After pre-processing the digital radiograph,the Gabor and wavelet (4- level decomposition) was performed to calculate the energy of trabecular patterns in order to detect osteoporosis. The energy of trabecular pattern was calculated using the formula given below Table 3. Calculated Energy using Wavelet for original ROI Classificatio n Normal Energy calculated in proximal femur for original images Neck Head Less Trochanter High trochanter Low (A1) High (A4) Low (A1) High (A4) Low (A1) High (A4) Low (A1) High (A4) 0.66 ± ± ± ± 0.64 ± 0.55 ± 0.51 ± ± Osteopenia 0.6 ± ± ± ± ± ± ± ± 0.03 Osteoporosis 0.64 ± ± ± ± ± ± ± ± 0.01 The Table 3 gives the details regarding energy calculated using wavelet transform for original images for all three groups Table 4. Calculated Energy using Wavelet for Enhanced ROI Classificati on Energy calculated in proximal femur for enhanced images Neck Head Less Trochanter High trochanter 2134

7 Normal Low (A1) High (A4) Low (A1) High (A4) Low (A1) High (A4) Low (A1) High (A4) 0.39 ± ± ± ± ± ± ± ± 0.17 Osteopenia 0.36 ± ± ± ± ± ± ± ± 0.13 Subjects Osteoporosi s 0.32 ± ± ± ± ± ± ± ± 0.09 Table 4 gives the details regarding energy calculated using wavelet transform for enhanced images for all three groups. Fig 5 comparison of energy calculated between normal women and osteopenic women. Fig 6 comparison of energy calculated between original and enhanced images of osteoporotic women

8 Fig 7 Graph is plot between approximation coefficient and energy for all the three groups. The energy calculated using DWT and Morlet Wavelet Transform, Fig 5 show the energy between normal women and osteopenia women using Morlet wavelet Transform. Fig 6 show the Energy between enhanced and original image of osteoporotic women. Fig 7 shows the energy between normal, Osteopenia and Osteoportic women using Gabor wavelet Transform. CONCLUSION Texture analysis of a Trabecular pattern recorded in the radiograph of proximal femur is successfully analysed by Gabor filter and Wavelet transform, Anisotropic filter and Morlet wavelet transforms. The comparison is made between Gabor wavelet transform and anisotropic wavelet transform. The accuracy is more in anisotropic wavelet transform than Gabor wavelet transform. The energy can be calculated from various features extracted from Trabecular patterns, through which the quality of bone assessment can be do for detecting osteoporosis. Through this research work it is concluded that 20% of the Indian women were suffering from osteoporosis. REFERENCES [1] Shankar N,Sathish Babu S,Viswanathan C, Bone trabecular analysis of proximal femur radiographs for the detection of osteoporosis using anisotropic Morlet wavelet transform, cluster Computing., March 2018, volume 16.ISSN , pp [2] Shankar N,Sathish Babu S,Viswanathan C, Morphometric analysis of X-ray and CT images for evaluating osteoporosis, cluster Computing., March 2018, volume 16.ISSN pp

9 [3] Hui SL, Slemenda CW, Johnston CJ, Age and bone mass as predictors of fracture in a prospective study, J. Clin. Invest., 1988, 81, pp: [4] Chapuy MC, Arlot ME, Dubocuf F, Brun J, Crouzet B, Arnaud S, Delmas PD, Meunier PJ, Vitamin D3 and calcium to prevent hip fractures in elderly women, N. Engl. J. Med., 1992, 327, pp: [5] Tothil P, Methods of bone mineral measurement-review article, Phys Med Biol., 1989, 34, pp: [6] Anburajan M, Evaluation of osteoporosis using conventional radiographic methods and dualenergy x-ray absorptiometry, Ph.D. Thesis, Anna University, Chennai, [7]. C. Chappard, V. Bousson, C. Bergot, D. Mitton, A. Marchadier, T. Moser, C. L. Benhamou, and J. D. Laredo, Prediction of femoral fracture load: Cross-sectional study of texture analysis and geometric measurements on plain radiographs versus bone mineral density, Radiology, vol. 2, pp , [8].Yongjian Yu and Scott T. Acton, Speckle Reducing Anisotropic Diffusion, IEEE Transaction on Image Processing Vol. 11, No. 11, November [9]. Francesco Voci, Shigeru Eiho, Naozo Sugimoto, and Hiroyuki Sekiguchi, Estimating the GradientThreshold in the Perona-Malik Equation, Applications of segmentation and noise reduction using a scale-space technique, IEEE Signal Processing Magazine, /04/$ IEEE. [10]. Nadia SOUAG, SPECKLE REDUCTION IN ECHOCARDIOGRAPHIC IMAGES, Florence, Italy, September 4-8, [11]. R C Gonzalez, and R E Woods, Digital Image Processing, Pearson Education, India, [12]. Sapthagirivasan, V, and M Anburajan. "Analysis of trabecular proximal femur bone in diagnosing osteoporosis using digital x-ray: A comparison with DXA", 2011 International Conference on Recent Trends in Information Technology (ICRTIT), AUTHORS: 2137

10 N SHANKAR is an active researcher, currently pursuing his research in Osteoporosis (image Processing). He did his master s degree (M.tech Biomedical Instrumentation) at SRM University bachelor s degree Biomedical Engineering at Pondicherry University, 2011 and 2009 respectively. He has over 7.7 years of experience in teaching. He has published many papers in various International Journals, International Conferences, National Journals and National Conferences. His research interests are Image processing, Signal Processing, Biomedical Instrumentation, and Biometrics. He is currently working as an Assistant Professor in Department of Biomedical Engineering, Saveetha Engineering College, Thandalam, Chennai. Dr. S.Sathishbabu had his graduation in the year 1996 in Electronics and Instrumentation Engineering, Master of Business Administration in the year 1998 and Master of Engineering (Process Control and Instrumentation) in the Annamalai University in He received Doctor of Philosophy in 2013 from Annamalai University in the field of Instrumentation. He has nearly 16 years of teaching experience. His research interests are in the areas of Process Control, Bio medical and Image processing. He has published many papers in various International Journals, International Conferences, National Journals and National Conferences. He has conducted and participated in many National Level Conferences, Technical Symposium, workshops and seminars.he is currently working as an Assistant Professor in Department of Electronics and Instrumentation Engineering, Annamalai University. Dr. V. Rajmohanreceived his B.E. degree in Electronics and Communication Engineering from Thiagarajar College of Engineering, Madurai, Tamilnadu, India in 1998, M.E. degree in VLSI from Regional Engineering College, Trichy, Tamilnadu, India in 2000 and PhD in Low Power Reversible Multipliers from Anna University, Chennai in His research interests include Digital Circuits and logic design, Reversible logic and synthesis, Low power VLSI and DSP VLSI. He is a member of IETE, IACSIT, IAENG. Currently he is working as Professor in Saveetha Engineering College, Chennai 2138

11 Allwyn Gnanadas received his B.E. degree in Electronics and Communication Engineering from DMI college of Engineering and Technology, India in 2013, M.Tech. degree in Biomedical Engineering from VIT University, Vellore, Tamilnadu, India in 2015.His research interests include Medical Imaging, Microprocessor and Microcontroller and Biomedical Instrumentation. Currently he is working as Assistant Professor in KPR institute of Engineering and Technology, Coimabtore. 2139

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