Al-Diri, B., Hunter, A., & Steel, D. (2009). An active contour model for segmenting and measuring retinal vessels. IEEE transactions on medical imaging, 28(9), 1488-1497.
Blinn, J. F. (1978). Simulation of wrinkled surfaces. Paper presented at the ACM SIGGRAPH computer graphics.
Bruder, V., Frey, S., & Ertl, T. (2016). Real-time performance prediction and tuning for interactive volume raycasting. Paper presented at the SIGGRAPH ASIA 2016 Symposium on Visualization.
Candiago, A., & Kawamoto Júnior, L. T. (2014). Virtual Multimedia Environment to Teach Safety Procedures in Laboratories. Paper presented at the Advanced Materials Research.
Conegliano, A., & Schulze, J. P. (2016). Realistic 3D Modeling of the Liver from MRI Images. Paper presented at the International Symposium on Visual Computing.
González, C., Pérez, M., & Orduña, J. M. (2017). Combining displacement mapping methods on the GPU for real-time terrain visualization. The Journal of Supercomputing, 73(1), 402-413.
Hammond, S., Wells, J. R., Marcus, D. M., & Prisant, L. M. (2006). Ophthalmoscopic findings in malignant hypertension. The Journal of Clinical Hypertension, 8(3), 221-223.
Harpe, S. E. (2015). How to analyze Likert and other rating scale data. Currents in Pharmacy Teaching and Learning, 7(6), 836-850.
Innamorati, C., Ritschel, T., Weyrich, T., & Mitra, N. J. (2017). Decomposing single images for layered photo retouching. Paper presented at the Computer Graphics Forum.
Joshi, A., Kale, S., Chandel, S., & Pal, D. (2015). Likert scale: Explored and explained. British Journal of Applied Science & Technology, 7(4), 396.
Khoshkhou, D., Mostafavi, M., Reinhard, C., Taylor, M., Rickerby, D., Edmonds, I., . . . Connolly, B. (2016). Three-dimensional displacement mapping of diffused Pt thermal barrier coatings via synchrotron X-ray computed tomography and digital volume correlation. Scripta Materialia, 115, 100-103.
Lai, C.-F. W., Yeung, S.-K., Yan, X., Fu, C.-W., & Tang, C.-K. (2016). 3D navigation on impossible figures via dynamically reconfigurable maze. IEEE transactions on visualization and computer graphics, 22(10), 2275-2288.
Liu, B., Clapworthy, G. J., & Dong, F. (2015). IsoBAS: a binary accelerating structure for fast isosurface rendering on GPUs. Computers & Graphics, 48, 60-70.
Luetkemeyer, C. M., Cai, L., Neu, C. P., & Arruda, E. M. (2018). Full-volume displacement mapping of anterior cruciate ligament bundles with dualmri. Extreme Mechanics Letters, 19, 7-14.
Mendonca, A. M., & Campilho, A. (2006). Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE transactions on medical imaging, 25(9), 1200-1213.
Preim, B., & Botha, C. P. (2013). Visual computing for medicine: theory, algorithms, and applications: Newnes.
Series, B. (2012). Methodology for the subjective assessment of the quality of television pictures. Recommendation ITU-R BT, 500-513.
Sparavigna, A. C. (2014). GIMP and wavelets for medical image processing: Enhancing images of the fundus of the eye. arXiv preprint arXiv:1408.4703.
Sparavigna, A. C. (2015). An image processing approach based on Gnu Image Manipulation Program Gimp to the panoramic radiography. International Journal of Sciences, 4(5), 57-67.
Staal, J., Abràmoff, M. D., Niemeijer, M., Viergever, M. A., & Van Ginneken, B. (2004). Ridge-based vessel segmentation in color images of the retina. IEEE transactions on medical imaging, 23(4), 501-509.
Sulaiman, P. S., Rahmat, R. W., Mahmod, R., & Rashid, A. (2008). A liver level set (LLS) algorithm for extracting liver’s volume containing disconnected regions automatically. IJCSNS, 8(12), 246.
Team, G. (2015). GIMP-The GNU Image Manipulation Program. Online. URL https://www. gimp.org.
Teng, T., Lefley, M., & Claremont, D. (2002). Use of two-dimensional matched filters for estimating a length of blood vessels newly created in angiogenesis process. Medical & Biological Engineering & Computing, 40, 2-13.
Verhoeven, G. J. (2017). Computer graphics meets image fusion: the power of texture baking to simultaneously visualise 3d surface features and colour. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 4.
Wakid, M., Kirmizibayrak, C., & Hahn, J. K. (2011). Texture mapping volumes using GPU-based polygon-assisted raycasting. Paper presented at the 2011 16th International Conference on Computer Games (CGAMES).
Wikipedia. (2019). Image Segmentation Retrieved from https://en.wikipedia.org/wiki/Image_segmentation
Wu, K., Knoll, A., Isaac, B. J., Carr, H., & Pascucci, V. (2017). Direct Multifield Volume Ray Casting of Fiber Surfaces. IEEE transactions on visualization and computer graphics, 23(1), 941-949.
Yin, X., Ng, B. W., He, J., Zhang, Y., & Abbott, D. (2014). Accurate image analysis of the retina using hessian matrix and binarisation of thresholded entropy with application of texture mapping. PLoS One, 9(4), e95943.
You, X., Peng, Q., Yuan, Y., Cheung, Y.-m., & Lei, J. (2011). Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recognition, 44(10-11), 2314-2324.
Zeng, Y., Rao, B., Chapman, W. C., Nandy, S., Rais, R., González, I., Chatterjee, D., Mutch, M., & Zhu, Q. (2019). The Angular Spectrum of the Scattering Coefficient Map Reveals Subsurface Colorectal Cancer. Scientific reports, 9(1), 2998.
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Sarah Qahtan Mohammed Salih
middle technical university
Abdul Sattar Arif Khammas
middle technical university
How to Cite
Using GIMP and ShaderMap Tools as an Alternative Segmentation Method for Retinal Image Enhancement
Vol 2 No 1 (2019): October
Submitted: Sep 20, 2019
Published: Oct 12, 2019
Retinal image segmentation plays an important role in monitoring and diagnosing retinal diseases. It is considered as one of the most challenging tasks for computer graphics researchers. Many researchers used complex approach to obtain the segmentation of retinal images for medical diagnoses. However, the main goal of this study was to propose an alternative method for segmenting retinal image by employing two tools. ShaderMap and GIMP, the GNU Image Manipulation Program used in this paper for creating normal map that can improve the retinal image and give an alternative of those acquired from segmentation. The performance of the proposed method was evaluated on one high resolution retinal image datasets (REVIEW) and one low resolution image datasets (DRIVE). According to the pre-designed questionnaire, the proposed method improved the retinal image and GIMP gave better result compared to ShaderMap. However, when high resolution retinal image used there was no significant differences between both tools.