Proposal of a CBIR Framework for Retinal Images Using Fusion Edge Detection and Zernike Moments

Sivakamasundari, J; Natarajan, V.

doi:10.1007/978-3-319-20294-5_63

Search citation statements

Order By: Relevance

Paper Sections

Select...

Background Exclusion1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2017

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Modified valley emphasized automatic thresholding and morphology for color distorted background exclusion was adopted in [53]. Undesired background separation through morphology and edge operator as preprocessing was proposed in [54]. Median filtering, CLAHE, Bottom hat and contrast stretching for background removal [55], median filtering and bottom hat for background separation [56], CLAHE, thresholding, background exclusion and post filtration [57] have reported.…”

Section: Background Exclusionmentioning

confidence: 99%

Review of Preprocessing Techniques for Fundus Image Analysis

Joshi¹,

Karule²

2017

AMA_B

View full text Add to dashboard Cite

The principal target of preprocessing is to get more appropriate resultant image than its original for further additional analysis. Enhancement of retinal images creates several challenges. The main obstacle is to develop a technique to accommodate the wide variation in contrast inside the image. Necessity of preprocessing methods are for image normalization and to increase the contrast for achieving accurate analysis. This work examined literature in the prior process of digital imaging, in the field of the analysis of fundus image to extract normal and pathologic retinal traits within the context of diabetic retinopathy (DR).

show abstract

Section: Background Exclusionmentioning

confidence: 99%