Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD
CELL: +91 9894917187 | ...
OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD
CELL: +91 9894917187 | ...
OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD
CELL: +91 9894917187 | ...
OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD
CELL: +91 9894917187 | ...
OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD
CELL: +91 9894917187 | ...
OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD
CELL: +91 9894917187 | ...
OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD
CELL: +91 9894917187 | ...
OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD
CELL: +91 9894917187 | ...
OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD
CELL: +91 9894917187 | ...
OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD
CELL: +91 9894917187 | ...
OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD
CELL: +91 9894917187 | ...
Upcoming SlideShare
Loading in …5
×

Automated 3 d retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments

23 views

Published on

Final Year IEEE Projects for BE, B.Tech, ME, M.Tech,M.Sc, MCA & Diploma Students latest Java, .Net, Matlab, NS2, Android, Embedded,Mechanical, Robtics, VLSI, Power Electronics, IEEE projects are given absolutely complete working product and document providing with real time Software & Embedded training......

JAVA, .NET, NS2, MATLAB PROJECTS:

Networking, Network Security, Data Mining, Cloud Computing, Grid Computing, Web Services, Mobile Computing, Software Engineering, Image Processing, E-Commerce, Games App, Multimedia, etc.,

EMBEDDED SYSTEMS:

Embedded Systems,Micro Controllers, DSC & DSP, VLSI Design, Biometrics, RFID, Finger Print, Smart Cards, IRIS, Bar Code, Bluetooth, Zigbee, GPS, Voice Control, Remote System, Power Electronics, etc.,

ROBOTICS PROJECTS:

Mobile Robots, Service Robots, Industrial Robots, Defence Robots, Spy Robot, Artificial Robots, Automated Machine Control, Stair Climbing, Cleaning, Painting, Industry Security Robots, etc.,

MOBILE APPLICATION (ANDROID & J2ME):

Android Application, Web Services, Wireless Application, Bluetooth Application, WiFi Application, Mobile Security, Multimedia Projects, Multi Media, E-Commerce, Games Application, etc.,

MECHANICAL PROJECTS:

Auto Mobiles, Hydraulics, Robotics, Air Assisted Exhaust Breaking System, Automatic Trolley for Material Handling System in Industry, Hydraulics And Pneumatics, CAD/CAM/CAE Projects, Special Purpose Hydraulics And Pneumatics, CATIA, ANSYS, 3D Model Animations, etc.,

CONTACT US:

ECWAY TECHNOLOGIES
23/A, 2nd Floor, SKS Complex,
OPP. Bus Stand, Karur-639 001.
TamilNadu , India.Cell: +91 9894917187.
Website: www.ecwayprojects.com | www.ecwaytechnologies.com
Mail to: ecwaytechnologies@gmail.com.

Published in: Engineering
  • Be the first to comment

  • Be the first to like this

Automated 3 d retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments

  1. 1. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com AUTOMATED 3-D RETINAL LAYER SEGMENTATION OF MACULAR OPTICAL COHERENCE TOMOGRAPHY IMAGES WITH SEROUS PIGMENT EPITHELIAL DETACHMENTS By A PROJECT REPORT Submitted to the Department of electronics &communication Engineering in the FACULTY OF ENGINEERING & TECHNOLOGY In partial fulfillment of the requirements for the award of the degree Of MASTER OF TECHNOLOGY IN ELECTRONICS &COMMUNICATION ENGINEERING APRIL 2016
  2. 2. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com CERTIFICATE Certified that this project report titled “Automated 3-D Retinal Layer Segmentation of Macular Optical Coherence Tomography Images With Serous Pigment Epithelial Detachments” is the bonafide work of Mr. _____________Who carried out the research under my supervision Certified further, that to the best of my knowledge the work reported herein does not form part of any other project report or dissertation on the basis of which a degree or award was conferred on an earlier occasion on this or any other candidate. Signature of the Guide Signature of the H.O.D Name Name
  3. 3. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com DECLARATION I hereby declare that the project work entitled “Automated 3-D Retinal Layer Segmentation of Macular Optical Coherence Tomography Images With Serous Pigment Epithelial Detachments” Submitted to BHARATHIDASAN UNIVERSITY in partial fulfillment of the requirement for the award of the Degree of MASTER OF APPLIED ELECTRONICS is a record of original work done by me the guidance of Prof.A.Vinayagam M.Sc., M.Phil., M.E., to the best of my knowledge, the work reported here is not a part of any other thesis or work on the basis of which a degree or award was conferred on an earlier occasion to me or any other candidate. (Student Name) (Reg.No) Place: Date:
  4. 4. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com ACKNOWLEDGEMENT I am extremely glad to present my project “Automated 3-D Retinal Layer Segmentation of Macular Optical Coherence Tomography Images With Serous Pigment Epithelial Detachments” which is a part of my curriculum of third semester Master of Science in Computer science. I take this opportunity to express my sincere gratitude to those who helped me in bringing out this project work. I would like to express my Director,Dr. K. ANANDAN, M.A.(Eco.), M.Ed., M.Phil.,(Edn.), PGDCA., CGT., M.A.(Psy.)of who had given me an opportunity to undertake this project. I am highly indebted to Co-OrdinatorProf. Muniappan Department of Physics and thank from my deep heart for her valuable comments I received through my project. I wish to express my deep sense of gratitude to my guide Prof. A.Vinayagam M.Sc., M.Phil., M.E., for her immense help and encouragement for successful completion of this project. I also express my sincere thanks to the all the staff members of Computer science for their kind advice. And last, but not the least, I express my deep gratitude to my parents and friends for their encouragement and support throughout the project.
  5. 5. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com ABSTRACT: Automated retinal layer segmentation of optical coherence tomography (OCT) images has been successful for normal eyes but becomes challenging for eyes with retinal diseases if the retinal morphology experiences critical changes. We propose a method to automatically segment the retinal layers in 3-D OCT data with serous retinal pigment epithelial detachments (PED), which is a prominent feature of many chorioretinal disease processes. The proposed framework consists of the following steps: fast denoising and B-scan alignment, multi-resolution graph search based surface detection, PED region detection and surface correction above the PED region. The proposed technique was evaluated on a dataset with OCTimages from 20 subjects diagnosed with PED. The experimental results showed the following. 1) The overall mean unsigned border positioning error for layer segmentation is , and is comparable to the mean inter-observer variability ( ). 2) The true positive volume fraction (TPVF), false positive volume fraction (FPVF) and positive predicative value (PPV) for PED volume segmentation are 87.1%, 0.37%, and 81.2%, respectively. 3) The average running time is 220 s for OCT data of 512 64 480 voxels.
  6. 6. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com INTRODUCTION: Optical coherence tomography (OCT), a noninvasive, non-contact scan of the retina that shows its cross-sectional profile, has been used clinically for assessment of a variety of ocular diseases, such as glaucoma, diabetic macular edema (DME), and age-related macular degeneration (AMD). Recently introduced spectral domain (SD) OCT produces highresolution real 3-D volumetric scan of the retina, and most of the anatomical layers of the retina can be visualized. Many methods have been proposed for automated retinal layer segmentation of SD-OCT images of normal eyes, and have obtained satisfactory results. Fig. 1 shows a macular centered OCT B- scan (axial view) of a normal eye and the 11 surfaces that define 10 retinal layers, segmented using the Iowa Reference Algorithm. The surfaces are numbered 1–11 from top to bottom. The retinal layers thus defined are nerve fiber layer (NFL), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer and inner segment layer ( ), connecting cilia (CL), outer segment layer (OSL), Verhoeff's membrane (VM), and retinal pigment epithelium (RPE). Layer segmentation methods designed for normal retinas have also been successfully applied to retinas with certain types of diseases, such as glaucoma, and multiple sclerosis or other diseases at an early stage, when no dramatic change in the layer structure happens. However, they usually experience difficulty when additional structures exist, such as intraretinal cysts, subretinal, or sub-RPE fluid in DME and wet AMD. In these cases, layer segmentation becomes challenging due to the following two reasons. First, the morphological features of each layer may vary greatly, and some constraints such as layer smoothness and thickness may not apply as in the normal case. Secondly, the degradation of image quality caused by abnormalities may affect the segmentation performance. Therefore, new methods that can segment retinas with abnormalities are needed for quantitative analysis of these diseases. The significance of layer segmentation in pathological study and clinical practice lies in the following two aspects. First, with the segmentation information, the morphological and optical features of each individual layer and their difference from normal ones can be analyzed, which can improve the understanding of the disease progression and also can facilitate diagnosis. Secondly, layer segmentation can localize the abnormal regions and serve as a preprocessing step for automated detection and analysis of the abnormalities.
  7. 7. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com In this paper, we focus on segmentation for retinas with serous pigment epithelium detachments (PEDs), which is associated with sub-RPE fluid and RPE deformation. We report a fully automated, unsupervised 3-D layer segmentation method for macular-centered OCT images with serous PEDs. In this work, layer segmentation and abnormal region segmentation are effectively integrated, where the position of layers and regions serve as constraints for each other. PED is a prominent feature of many chorioretinal disease processes, including AMD, polypoidal choroidal vasculopathy, central serous chorioretinopathy, and uveitis. PEDs can be classified as serous, fibrovascular, or drusenoid. Study shows that patients diagnosed with serous PED associated with AMD frequently have co-existing choroidal neovascularization (CNV), or have a higher risk of developing CNV, which can eventually cause severe visual acuity loss. PED is routinely diagnosed by 2-D imaging techniques such as fluorescein angiography (FA) and indocyanine green angiography (ICGV). More recently, SD-OCToffers a means to show the cross- sectional morphologic characteristics of PED and to provide more detailed anatomic assessment. In OCTimages, the RPE appears as a bright layer, and serous PED appears as a localized, relatively pronounced dome-shaped elevation of the RPE layer, as shown There were several reported methods related to segmentation of OCT images with PEDs or other abnormalities. Penha et al. utilized the software on the commercially available Cirrus SD- OCT to detect the RPE and a method proposed by Gregori et al. To create a virtual RPE floor free of any deformations. The combination of these algorithms permitted the detection of PEDs. The same algorithm was also used to study drusen associated with AMD ding et al. Detected the top and bottom surfaces of the retina as constraints for subretinal and sub-RPE fluid detection. Chen et al. segmented the fluid-associated abnormalities associated with AMD using a combined graph- search–graph-cut (GS-GC) method. The abnormal region was detected together with two auxiliary surfaces. Dufour et al. Detected six surfaces using graph-search based method with soft constraints in OCT images with drusen. Quellec et al. segmented 11 surfaces in OCT images with fluid- associated abnormalities. However, all these works focused on region segmentation only. In only two or three surfaces were detected and served as constraints for the region segmentation purpose. In and more surfaces were detected and their position information was utilized to indicate or detect retinal abnormalities. For all works reported in no evaluation of layer segmentation accuracy was given.
  8. 8. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com In comparison with the existing methods, the proposed method achieves the following goals. • The retinal OCT image with PEDs is segmented into all discernible layers. • Both layer segmentation and abnormal region segmentation are performed and high accuracy is achieved. • The method is designed for retinas with serous PEDs, but it also maintains good performance for normal retinas.
  9. 9. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com CONCLUSION: We proposed an unsupervised method for automated segmentation of retinal layers on SD- OCT scans of eyes with serous PED. After denoising using fast bilateral filtering, the B-scans are aligned using the upper boundary of the retina. This alignment improves the smoothness of the surfaces to be detected and enhances the accuracy of the segmentation. Then the surfaces defining the boundaries between consecutive layers are detected based on multi-resolution single surface graph search. Surfaces 1–6, and a surface combined by surfaces 7 and 10 are detected on the denoised and aligned data. Then surfaces 11 and 12 corresponding to the elevated RPE and the estimated normal RPE floor are also detected, whose difference is used to find the PED footprints. Surface 11 is used as the reference surface for flattening of the retina. Then surface 7 is corrected based on its smoothness on the flattened image. Surfaces 8–10 are also detected on the flattened image with necessary corrections. For the tested PED dataset, the overall layer segmentation errors are statistically indistinguishable from the inter-observer variability, and statistically significantly smaller than errors obtained from employing the general Iowa Reference Algorithm . Though the proposed algorithm is designed for retinas with serous PED's, it also works well for normal retinas. For the tested normal dataset, the overall layer segmentation errors are statistically smaller than the inter- observer difference, and statistically indistinguishable from errors obtained from employing the Iowa Reference Algorithm . Although the method is not the most efficient for normal retina segmentation, it represents a major advancement of the field allowing segmentation of the retinal layers in both normal and diseased retinal images, thus bypassing a need for disease-specific diagnosis prior to retinal analysis Simultaneous detection and segmentation of the PED volume is also achieved. The PED volume segmentation is of high true positive ratio and low false positive ratio, which is statistically comparable to the results obtained by the GS-GC method . The proposed algorithm is 3-D, but some of the calculations, including denoising, gradient calculation for cost function and smoothing of the detected surfaces, are constrained in 2-D B-scans. This allows big difference in adjacent B- scans caused by low resolution in the direction of the available data. For OCT data with higher resolution in the direction, their 3-D counterparts should be used to fully utilize the contextual information. The proposed algorithm can be extended to other pathological cases where RPE deformation occurs. It provides a means to remove the sub-RPE fluid region and to approximately
  10. 10. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com restore the original shape of the retinal layers. This method can be extended to other pathological cases where intraretinal or subretinal fluids are present. In the future, we will consider detection of these regions and using the information to improve the layer segmentation results. In summary, as an accurate and efficient replacement of manual segmentation, the proposed algorithm can be utilized for quantitative analysis of features of individual retinal layers for both eyes with serous PED's and normal eyes. The algorithm also detects the PED volume, providing its size, shape, and position information. With the current efficiency, the reported work can be used in offline clinical or pathology studies. However, with further optimization in implementation, additional speed-up will be accomplished and the reported approach will become suitable for clinical practice.
  11. 11. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com REFERENCES: [1] J. Novosel, K. A. Vermeer, G. Thepass, H. G. Lemij, and L. J. van Vliet, “Loosely coupled level sets for retinal layer segmentation in optical coherence tomography,” in Proc. IEEE Int. Symp. Biomed. Imag., 2013, pp. 1010–1013. [2] M. K. Garvin, M. D. Abràmoff, X. Wu, S. R. Russell, T. L. Burns, and M. Sonka, “Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images,” IEEE Trans. Med. Imag., vol. 28, no. 9, pp. 1436–1447, Sep. 2009. [3] S. Lu, C. Y. Cheung, J. Liu, J. H. Lim, C. K. Leung, and T. Y. Wong, “Automated layer segmentation of optical coherence tomography images,” IEEE Trans. Biomed. Eng., vol. 57, no. 10, pp. 2605–2608, Oct. 2010. [4] A. Yazdanpanah, G. Hamarneh, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomography images using an active contour approach,” IEEE Trans. Med. Imag., vol. 30, no. 2, pp. 484–496, Feb. 2011. [5] Q. Song, J. Bai, M. K. Garvin, M. Sonka, J. M. Buatti, and X. Wu, “Optimal multiple surface segmentation with shape and context priors,” IEEE Trans. Med. Imag., vol. 32, no. 2, pp. 376– 386, Feb. 2013. [6] P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schröder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of OCT data using trained hard and soft constraints,” IEEE Trans. Med. Imag., vol. 32, no. 3, pp. 531–543, Mar. 2013. [7] Q. Yang, C. A. Reisman, Z. Wang, Y. Fukuma, M. Hangai, N. Yoshimura, A. Tomidokoro, M. Araie, A. S. Raza, D. C. Hood, and K. Chan, “Automated layer segmentation of macular OCT images using dual-scale gradient information,” Opt. Exp., vol. 18, pp. 21 293–307, 2010. [8] S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Exp., vol. 18, no. 18, pp. 19413–28, 2010.

×