Food Calories Detection using Deep Learning OpenCV | Python

Description

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The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to variable size of vessels, relatively low contrast, and potential presence of pathologies like micro aneurysms and haemorrhages. The Project proposes the Retinal image analysis through efficient detection of vessels and exudates for retinal vasculature disorder analysis. It plays important roles in detection of some diseases in early stages, such as diabetes, which can be performed by comparison of the states of retinal blood vessels. Intrinsic characteristics of retinal images make the blood vessel detection process difficult. Here, we proposed a new algorithm to detect the retinal blood vessels effectively. The green channel will be selected for image analysis to extract vessels accurately. The duabachies wavelet transform is used to enhance the image contrast for effective vessels detection. The directionality feature of the multistructure elements method makes it an effective tool in edge detection. Hence, morphology operators using multistructure elements are applied to the enhanced image in order to find the retinal image ridges. Afterward, morphological operators by reconstruction eliminate the ridges not belonging to the vessel tree while trying to preserve the thin vessels unchanged. In order to increase the efficiency of the morphological operators by reconstruction, they were applied using multistructure elements. A simple thresholding method along opening and closing indicates the remained ridges belonging to vessels. Experimental result proves that the blood vessels and exudates can be effectively detected by applying this method on the retinal images.

Existing Method:

  • Edge detection methods
  • Segmentation like simple and global thresholding algorithm

Drawbacks:

  • In this method intrinsic characteristics of retinal images make the blood vessel detection process difficult.
  • Poor Edge detection.
  • Not possible to cluster a fundus Exudates.

Proposed System:

  • The combination of multi structure morphological process and Segmentation technique used effectively for retinal vessel and exudates detection.

Modules:

  • Daubachies Wavelet
  • GLCM Features
  • NN Classifier
  • Morphological Process

Advantages:

  • Accurate retina vessel and execute detection
  • It is useful in diabetic diagnosis

Applications:

  • Bio medical application for retinal image analysis and fundus, exudates detection.
  • Image Fusion of CT Machine.

Software Requirements:

  • MATLAB 2018a and above versions

REFERENCES:

[1] T. Y. Wong, R. Klein, A. R. Sharrett, B. B. Duncan, D. J. Couper, J. M. Tielsch, B. E. Klein, and L. D. Hubbard, ?Retinal arteriolar narrowing and risk of coronary heart disease in men and women: The atherosclerosis risk in communities study,? The Journal of the American Medical Association, vol. 287(9), pp. 1153?9, 2002.

[2] M. D. knudtson, K. E. Lee, L. D. Hubbard, T. Y. Wong, R. Klein, and B. E. Klein, ?Is retinal photography useful in the measurement of stroke risk?? Lancet Neurol, vol. 3(3), pp. 179?83, 2004.

[3] W. H. O. Fact Sheet on Cardiovascular Diseases, http://www.who.int/ cardiovascular diseases/en/. (last accessed on 12 July, 2010), 2010.

[4] M. Saeza, S. Gonzalez-Vazquezc, M. Gonzalez-Penedoc, M. A. Barcelo, M. Pena-Seijod, G. C. de Tuerob, and A. Pose-Reinod, ?Development of an automated system to classify retinal vessels into arteries and veins,? Computer methods and programs in biomedicine, vol. PMID: 22424729, pp. 1?10, 2012.

[5] H. Azegrouz and E. Trucco, ?Max-min central vein detection in retinal fundus images,? In the proceedings of IEEE International Conference on Image Processing, 2006, vol. 10.1109/ICIP.2006.313145, pp. 1925 ? 1928, 2006.

[6] A. Ruggeri, E. Grisan, and M. D. Luca, ?An automatic system for the estimation of generalized arteriolar narrowing in retinal images,? In the Proceedings of the 29th Annual International Conference of the IEEE EMBS, pp. 6462?6, 2007.

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