IJCSIS invites authors to submit their original and unpublished work that communicates current research on information assurance and security regarding both the theoretical and methodological aspects, as well as various applications in solving real world information security problems.
(IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 5, August 2010 IEEM PROGRAMMING PROCEDURE FOR DETECTING BOUNDARY OF CAROTID ARTERY V.Savithri Dr.S.Purushothaman Professor,Department of Computer Science Principal, Sun College of Engineering and Mother Teresa Women’s University Technology Kodaikanal, INDIA Kanyakumari, INDIA Savi3_8@yahoo.co.in email@example.com Abstract--This paper presents an IEEM programming Abstract-- represents, in fact, the most powerful instrument today procedure for use on noisy B-mode ultrasound images B- available for predictingcoronary disorders in people over of the carotid artery. This programming procedure is fifty .Images are taken by ultrasound equipment working at based on Image Enhancement, Edge detection and frequencies ranging from 1 to 20 MHz, and are obtained via Morphological operations in Boundary detection. This a probe that has to be positioned on the patient’s neck. procedure may simplify the job of the practitioner of There are two phases to the measurement process: i) for analyzing accuracy and variability of segmentation carotid image capturingand ii) thickness measurement. results. Possible plaque regions are also highlighted. A Ultrasound plays an important role in diagnosis and thorough evaluation of the method in the clinical illness and injury. The noninvasive imaging of different environment shows that inter observer variability is parts of the body has other applications in medical evidently evidently decreased and so is the overall analysis diagnosis, such as in tissue characterization and time. The results demonstrate that it has the measurement of tissue motion.The measurement of tissue potential to perform qualitatively better than motion is a broad category. It can include the analysis of applying existing methods in intima and the motion of physically active organs such as the heart and adventitial layer detection on B-mode images. adventitial B- discrete structures such as cardiac valves and arterial walls. It can refer to the analysis of motion induced in passive tissues, such as liver or lung, due to active organs such as Keywords- Artery, boundary detection , imaging, Keywords- the heart or by external sources such as low frequency Ultrasonic, parallel programming. vibration or compression. The response of the tissue is a function its elasticity, which is directly related to the healthiness of the tissue. The measurement of blood I. Introduction flow velocity is an important parameter in diagnosing vascular diseases such as venous thrombosis. Regardless of Over the last few years, image processing has the particular medical application of ultrasound, all been playing an increasingly important role in many applications require the transmission and capture of radio- scientific areas. This is due, among other reasons, to the frequency (RF) ultrasonic signals. The signals must be ever-improving performance of computers that are now processed in some way to extract the desired information. In capable of quickly processing the characteristically large the case of assessing tissue motion, Doppler ultrasound has amounts of data produced by images. This processing is been very popular, particularly in the measurement of blood mostly oriented toward extracting either qualitative or flow. Practically all commercial ultrasound blood flow quantitative information from object images. In particular, measurement systems utilize the frequency-domain Doppler precise dimensional characterization of objects technique. Doppler techniques have been around for a long through contact-less measurement techniques is a very time, and have been extensively covered in the literature. In important task in several environments such as industrial addition to Doppler, however, time-domain methods of quality and process control and medical diagnosis. A measuring tissue motion also exits, which have not been typical application field of medical image processing is in comprehensively reviewed.Time-domain methods have the diagnosis of atherosclerosis.The atherosclerosis potential advantages over Doppler techniques in many process is strongly linked to carotid thickening and applications, and the use of time-domain based methods is plaques, whose presence can be clearly detected in becoming more and more widespread. artery longitudinal section images provided by ultrasound Due to the huge amount of information ( intravascular(IVUS) images are increasing their role in the techniques.The analysisof the carotid ultrasound diagnosis and treatment of several diseases. Manual 85 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 5, August 2010 segmentation is slow and lacks of objectivity. Image brightness may be improved by modifying Consequently, automatic segmentation and tracking of the the histogram of the image. vessel inner wall in IVUS images has been approached in several recent works. The poor quality of the images Image Denoising: suggests the use of techniques such as probabilities or fuzzy logic guiding an active contour to adjust the inner wall. An image is often corrupted by noise in its acquisition or transmission. Noise is any undesired information that II. Materials and Methods contaminates an image. Noise appears in images from a Problem Definition variety of sources. The goal of denoising is to remove the noise while retaining as much as possible the important signal features. Denoising can be done through filtering. The noise created during ultrasound scanning Filters reduces noise. Gaussian highpass filter helps to leads to difficulty in defining the boundary of the vessel. reduces a noise. The image is further deteriorated by the occurrence of lipid rich plaque a poorly angled transducer during image % H = guassian_filter(co,ro,fo); acquisition. Difficult in highlighting plaque region. H = 1 – H; Out = Zeros(co,ro); BOUNDARY DETECTION Outf = imf. * H; Ultrasonic Artery Images Out = abs ( ifft2(outf)); Imshow(im), title (‘Original image’), figure, Imshow((out)), title (‘Filtered Image’) figure, title(‘2D Imshow (H), title(‘2D view of B’), figure, surf(H), Title (‘3D view of H’) Edge Detection: The IEEM defines edges as Zero-crossings of second 1(a) Figure 1(a) Carotid artery image. (b) Definition of echo zones and derivatives in the direction of the greatest first derivate. This interfaces. works in multistage process (i) image is smoothed by Gaussian convolution (ii) 2D first derivate operator is applied to the smoothed image to highlight region of the Figure. 1(a) shows a representative image of a carotid artery. image with high spatial derivatives. The effectiveness of The femoral artery has a similar appearance. The echoes in this algorithm is determined by three parameters (i) width of the region of interest can be schematically grouped into the Gaussian kernel (ii) upper threshold (iii) lower threshold seven echo zones Z1–Z7 [Figure. 1(b)]. Previous studies used by tracker. ,  have shown that the leading edge (upper side) of Z3, Z5, and Z7, denoted as I3, I5, and I7, can be mapped to Morphological operations for Boundary detection: the near-wall intima–lumen interface, the far-wall lumen– intima interface, and the far-wall media–adventitia interface, Morphological operations are very effective in the detection respectively. Consequently, the distancebetween I3 and I5 of boundaries in a binary image X. The following boundary represents the LD and the distance between I5 and I7 is the detectors are widely used: far-wall IMT.With this understanding, the determination of ultrasonic measurement of the artery becomes equivalent to Y=X–(X B) accurately detecting the echo boundaries I3, I5, and I7. Y=(X B ) = X or The femoral artery has a similar appearance. Y = (X B) - ( X B) The echoes in the region of interest can be schematically where Y is the boundary image, operator denotes erosion grouped into seven echo zones Z1–Z7 . Previous studies have shown that the leading edge (upper side) of Z3, operator denotes dilation ‘ – ‘ denotes set theoretical Z5, and Z7, denoted as I3, I5, and I7, can be mapped to the subtraction. near-wall intima–lumen interface, the far-wall lumen–intima interface, and the far-wall media–adventitia interface, %Boundary detector respectively. Consequently, the distance between I3 and I5 Close all; represents the LD and the distance between I5 and I7 is the Clear all; far-wall IMT. With this understanding,[14 the determination Clc; of ultrasonic measurement of the artery becomes equivalent a=imread(‘carotid.jpg’); to accurately detecting the echo boundaries. b=[010;111;010]; a1=imdilate(a,b); a2=imerode(a,b); a3=a-a2; III. IEEM programming procedure a4=a1-a; Image Enhancement: a5=a1-a2; imshow(a) Histogram equalization provides more visually figure,imshow(a1),title(‘Dilated Image’) pleasing results across a wider range of images. figure, imshow(a2),title(‘Eroded Image’) 86 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 5, August 2010 Segmentation of the intima-media region: intima- We introduce a new method for the segmentation of the imtima-media region in ultrasound images, which combines splines(for the adventitia detection), dynamic programming(dp), smooth intensity thresholding surfaces and a successful geometric active contour model and known for its accuracy, G-canny flexibility and robustness. Several image features are used in the segmentation. Human interaction is minimal. It is able to segment both near-end and far- end carotid walls; it supports to detect plaques of different sizes, shapes and classes. IV. Results and discussion TABLE 1 READING VARIABILITY(%) WHEN MEASUREMENTS WERE PERFORMED BY THREE READERS BEFORE APPLYING IEEM PROCEDURE. morphed READER1 READER2 READER3 AVERAGE (accuracy) (accuracy) (accuracy) (accuracy %) 82% 80% 84% 82% TABLE 2 READING VARIABILITY(%) WHEN MEASUREMENTS WERE PERFORMED BY THREE READERS AFTER APPLYING IEEM Original image PROCEDURE. READER1 READER2 READER3 AVERAGE (accuracy) (accuracy) (accuracy) (accuracy %) 94% 94.5% 96% 94.5% V. Conclusion gray In conclusion, We have proposed a method based on IEEM programming procedure to automatically measure ultrasonic artery images. The human knowledge of the artery image is incorporated in the system, which makes the system capable in processing images of different quality. Human factors in the determination of the boundaries are reduced. Evaluation of the system shows reduced inter observer variability as well as overall analysis time. The automated artery boundary detection system can replace the old manual system in a clinical application environment. indexed 87 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 5, August 2010 Acknowledgement principles and description of a computerized analyzing system,” Clin. Physiol., vol. 6, no. 11, pp. 565–577, 1991. We would like to thank members of  P. Pignoli, E. Tremoli, A. Poli, and R. Paoletti, “Intimal Madras Medical College and New hope Scan center plus medial thickness of the arterial wall: A direct And Hospital , in the Department of Radiologist measurement with ultrasound imaging,” Circulation, and Neurologist ,and Dr.P.Kamalakannan, offered vol. 74, pp. 1399–1406, 1986. valuable comments advices and for providing  R. E. Bellman and S. Dreyfus, Appled Dynamic patients personal reports Programming. Princeton, NJ: Princeton University Press, 1962. References  A. A. Amini, T. E.Weymouth, and R. C. Jain, “Using dynamic programming for solving variational problems  R. Chelappa et al., “The past, present, and future of in vision,” IEEE Trans. Pattern Anal. Machine Intell., image and multidimensional signal processing,” IEEE vol. 12, pp. 855–867, Sept. 1990. Signal Processing Mag., vol. 15, pp. 21–58, Mar. 1998.  R. J. Kozick, “Detectinh interfaces on ultrasound  D. H. Ballard and C. M. Brown, Computer V ision. images of the carotid artery by dynamic programming,” Englewood Cliffs, NJ: Prentice-Hall, 1982.  L. Angrisani, P. Daponte, C. Liguori, and A. Pietrosanto, SPIE, vol. 2666, pp. 233–241, 1996.  W. Liang, R. Briwning, R. Lauer, and M. Sonka, “An image based measurement system for the “Automated analysis of brachial ultrasound time characterization of automotive gaskets,” Measurement, series,” in Proc. SPIE Conf. Physiol. Function vol. 25, pp. 169–181, 1999.  M. G. Bond and S. K. Wilmoth et al., “Detection and Multidimensional Images, vol. SPIE 3337, San Diego, CA, Feb. 1998, pp. 108–118. Monitoring of Asymptomatic Atherosclerosisi in Clinical Trials,” Amer. J. Med., vol.86, (suppl 4A), pp. 33–36, 1989.  N. M. El-Barghouty, T. Levine, S. Ladva, A. Flanagan, and A. Nicoladeis, “Histological verification of Savithri Vedachalam , working computerized carotid plaque characterization,” Eur. J. as professor in Department of V ascular Endovascular Surg., vol. 11, pp. 414–416, Computer Application in Hindu 1996. College, Pattabiram, Chennai,.  F. De Man, I. De Scheerder, M.C. Herregods, J. Piessens She received her degrees, and H. De Geest Role of Intravascular Ultrasound in M.Phil.(C.S.) from Alagappa Coronary Artery Disease: A new gold standart? University and M.C.A. from Beyond Angiography. Intravascular Ultrasound State- Annamalai University, India.Her area of research includes Of-The-Art XX Congres of the ESC, Vol 1 (August Medical Imaging, Image Processing, Object tracking, 3-D 1998) Image analysis. She published more than 10 papers in  D. Hausmann, Andre J.S. Lundkvist, Guy Friedrich, national , International Conferences and Journals. Krishnankutty Sudhir, Peter J. Fitzgerald and Paul G. Yock Lumen and Plaque Shape in Atherosclerotic Coronary Arteries Assesed by In EVO Intracoronary . Ultrasound Beyond Angiography. Intravascular Ultrasound: State-Of-The-Art XX Congres of the ESC, Vol 1 (August 1998) . Dr. S. Purushothaman is working as  F. Escolano, M. Cazorla, D. Gallardo and R. Rizo Principal and professor in Sun College Deformable Templates for Plaque Thickness of Engineering and Technology, Estimation of Intravascular Ultrasound Sequences Nagerkoil, India. He received his Ph.D Pattern Recognition and Image Analysis. Preprints of from IIT Madras, M.E from Anna the VI1 National Symp. On Patt. Recog. and Im. University Chennai and B.E from PSG An.Vol 1 (April 1997) College of Technology, Coimbatore  M.A.Bottalico, A.Starita, “EcoStudio:A computer tool His area of research includes Artificial to support carotid ultrasound images Neural Networks, Image Processing and signal processing. analysis,Engineering in Medicine and Biology He published more than 50 research papers in national and Soc.,IEEE,pp.2428-2430,2000. international journals.  Song Chun Zhu, Alan Yuille, Region Competition:Unzfiing Snakes, Region Growing, and BayesIMDL for Multiband Image Segmentation. IEEE Trans. Pattern An. Mach. Intelligence, Vol. 18, No 9 , ( September 1996).  Nobuyuki Otsu A Threshold Selection Method from Gray-Level Histograms. IEEE Trans. on Sys. Man and Cybernetics, Vol. SMC-9,Na 1, pp 62-65, (January 1979)  I. Wendelhag, T. Gustavsson, M. Suurkula, G. Berglund, and J. Wikstrand, “Ultrasound measurement of wall thickness in the carotid artery. Fundamental 88 http://sites.google.com/site/ijcsis/ ISSN 1947-5500
Pages to are hidden for
"IEEM Programming Procedure For Detecting Boundary Of Carotid Artery"Please download to view full document