Abstract
Diabetic Maculopathy is damage to the macula. Scientifically also known as a pathological disorder. It’s a very serious upshot of diabetes. Maculopathy early detection is very important as it causes blindness and is irreversible if proper treatment is taken. The present study deals with the design of a novel detection technique for early diagnosis of the diabetic maculopathy. For that Digital image processing have been used and for extracting the feature wavelet Filter has been used. Weighted KNN classification technique for grading of image i.e. mild, moderate and sever on the standard fundus images. The blood vessel extraction of the retina is first preferred because when macula starts getting affected by diabetes at the same time some abnormal blood vessel is created which is known as neovascularization. This Neovascularization cause’s blindness because the retina gets nourishes with the blood vessels that’s why blood vessel extraction is very important. Diabetic Maculopathy is one of the complications of diabetes mellitus that is considered as the major cause of vision loss among people around the world. It results from the leakage of fluid rich in fat and cholesterol from the damaged retinal vasculature. Accumulation of these fluids called exudates near the center of the retina. Development of diabetic Maculopathy is slow and silent, very frequently without any symptoms in the early stages. If Maculopathy is not detected in the early stage then the damage of the macula or visual field is irreversible and can lead to blindness. Therefore, compulsory regular screening of diabetic eye will help to identify the Maculopathy at the initial stage and reduce the risk of severe vision loss. Digital screening of Maculopathy results in the generation of a large number of retinal images to be Manually analyzed by an expert [2]. This often leads to observer fatigue and increase in the time taken for diagnosis. Non clinically significant (NCSME) and clinically significant (CSME) are two types of maculopathy stages.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
American Diabetes Association: American Diabetes Association Copyright 1995–2018 [Internet]. http://www.diabetes.org/diabetes-basics/type-1/
Shodhganga. http://shodhganga.inflibnet.ac.in/bitstream/10603/3095/14/14_chapter%206.pdf
Noronha, K., Nayak, K.P.: Automated diagnosis of diabetes maculopathy: a survey. J. Med. Imaging Health Inform. 3, 1–8 (2013)
Structured Analysis of the Retina. http://cecas.clemson.edu/~ahoover/stare/
Staal, J.J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23, 501–509 (2004)
DIARETDB0 - Standard Diabetic Retinopathy Database. http://www.it.lut.fi/project/imageret/diaretdb0/
DIARETDB1 - IMAGERET. http://www.it.lut.fi/project/imageret/diaretdb1/
CS425 Lab: Intensity Transformations and Spatial Filtering. http://www.cs.uregina.ca/Links/class-info/425/Lab3/
Kushwaha, S., Rabindra, K.S.: Study and analysis of various image enhancement method using MATLAB. Int. J. Comput. Sci. Eng. (IJCSE) 3(1), 15–20 (2015)
Dixit, M., Singh, R.P.: Histogram equalization a strong technique for image enhancement. Int. J. Signal Process. Image Process. Pattern Recogn. 8(8), 345–352 (2015)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. (2008). ISBN 9789332570320. OCLC 979415531
Garg, P.K., Verma, P., Bhardwaj, A.: A survey paper on various median filtering techniques for noise removal from digital images. Am. Int. J. Res. Formal Appl. Nat. Sci. (2014). ISSN (Print) 2328-3777, ISSN (Online) 2328-3785, ISSN (CD-ROM) 2328-3793
Mathworks. https://in.mathworks.com/discovery/image-thresholding.html
Kozak, K., Kozak, M., Stapor, K.: Weighted k-nearest-neighbor techniques for high throughput screening data. Int. J. Chem. Mol. Eng. 1(12), 155 (2007). World Academy of Science, Engineering and Technology
Ruikar, D.D., Santosh, K.C., Hegadi, R.S.: Automated fractured bone segmentation and labeling from CT images. J. Med. Syst. 43(3), 60 (2019). https://doi.org/10.1007/s10916-019-1176-x
Ruikar, D.D., Santosh, K.C., Hegadi, R.S.: Segmentation and analysis of CT images for bone fracture detection and labeling. In: Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques, Chap. 7. CRC Press (2019). ISBN 9780367139612
Hegadi, R.S., Navale, D.I., Pawar, T.D., Ruikar, D.D.: Multi feature-based classification of osteoarthritis in knee joint X-ray images. In: Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques, Chap. 5. CRC Press (2019). ISBN 9780367139612
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pattebahadur, C., Manza, R., Kamble, A. (2019). Design a Novel Detection for Maculopathy Using Weightage KNN Classification. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1036. Springer, Singapore. https://doi.org/10.1007/978-981-13-9184-2_32
Download citation
DOI: https://doi.org/10.1007/978-981-13-9184-2_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9183-5
Online ISBN: 978-981-13-9184-2
eBook Packages: Computer ScienceComputer Science (R0)