A new expert system based on hybrid colour and structure descriptor and machine learning algorithms for early glaucoma diagnosis
Medical image classification system is widely used by the radiologists to segment the medical images into meaningful regions. Glaucoma is an optic neuropathy defined by characteristic damage to the optic nerve and accompanying visual field deficits. Early diagnosis and treatment are critical to prevent irreversible vision loss and ultimate blindness. Automatic detection of diabetic retinopathy in retinal image is vital as it delivers data about unusual tissues which is essential for planning treatment. Automating, this method is challenging due to the high variabiity in the appearance of tissue among dissimilar patients and in many circumstances, the comparison between abnormal and normal tissue. This paper presents a new methodology and a computerized diagnostic system for diabetic retinopathy. In this article, adaptive histogram equalization is used to convert colour images to gray scale images followed by significant features are selected using hybrid colour and structure descriptor (HCSD). Finally, various classifiers are used for classification of images into normal and glaucomatous classes. The overall classification accuracy of HCSD with Hybrid Radial Basis Kernel based Support vector Machine (HRKSVM) is 97.55%, HCSD with Support vector Machine (SVM) is 94.77% and HCSD with Hybrid Kernel Support Vector Machine (HKSVM) is 95.71%.
KeywordsClassification Feature extraction Texture Segmentation Retinal images
- 3.Cremers D (2003) A multiphase level set framework for variation motion segmentation. In Proc Scale Space Meth Comput Vis, Isle of Skye, U.K. pp 599–614Google Scholar
- 8.Jurie F, Triggs B (2005) Creating efficient codebooks for visual recognition, in: Proceedings of the Tenth IEEE International Conference on Computer Vision, Los Alamitos, 17th-21st October 2005, vol. 1, IEEE, pp 604–610Google Scholar
- 10.Kharmegasundaraj G, Jayachandran A (2016) Abnormality segmentation and classification of multi model brain tumor in MR images using fuzzy based hybrid kernel SVM. Int J Fuzzy Syst 17(3):434–443Google Scholar
- 12.Kolar R, Jan J (2008) Detection of glaucomatous eye via color fundus images using fractal dimensions. Radio Eng 17(3):109–114Google Scholar
- 16.Larose DT (2004) Discovering knowledge in data: an introduction to data mining, chapter 5: KNN, New Jersey Willey Interscience, USA, pp 90–106Google Scholar