Title

Authors

Affiliation

¹Department of IT, GITAM University, Patancheru, Medak Dist-502329, India; ²Pydah College of Engineering And Technology, Gambheeram, Anandapuram, Visakhapatnam-531163, India; ³Jawaharlal Nehru Technological University Kakinada, Kakinada, Andhra Pradesh-533003, India; ⁴C R Rao Advanced Institute Of Mathematics Statistics And Computer Science, University Of Hyderabad Campus, Gachibowli, Hyderabad-500046, India

Email

susmitagv@gmail.com; *Corresponding author

Article Type

Hypothesis

Date

Received December 22, 2013; Revised January 11, 2014; Accepted January 11, 2014; Published January 29, 2014

The paper presents a new approach for medical image segmentation. Exudates are a visible sign of diabetic retinopathy that is the major reason of vision loss in patients with diabetes. If the exudates extend into the macular area, blindness may occur. Automated detection of exudates will assist ophthalmologists in early diagnosis. This segmentation process includes a new mechanism for clustering the elements of high-resolution images in order to improve precision and reduce computation time. The system applies K-means clustering to the image segmentation after getting optimized by Pillar algorithm; pillars are constructed in such a way that they can withstand the pressure. Improved pillar algorithm can optimize the K-means clustering for image segmentation in aspects of precision and computation time. This evaluates the proposed approach for image segmentation by comparing with K-means and Fuzzy C-means in a medical image. Using this method, identification of dark spot in the retina becomes easier and the proposed algorithm is applied on diabetic retinal images of all stages to identify hard and soft exudates, where the existing pillar K-means is more appropriate for brain MRI images. This proposed system help the doctors to identify the problem in the early stage and can suggest a better drug for preventing further retinal damage.

Keywords

Diabetic Retinopathy, K-Means, Fuzzy C-means, Pillar k-Means, Dark Spots, Hard exudates, Soft exudates.

Citation

Gogula  et al. Bioinformation 10(1): 028-032 (2014)
 

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.