Fr. 47.50

Lesion Detection Using Segmented Structure of Retina

English · Paperback / Softback

Shipping usually within 2 to 3 weeks (title will be printed to order)

Description

Read more

Morphology of retina indicates the diseases like diabetic retinopathy, glaucoma and hypertension. Automatic extraction of lesions from retinal images can assist in early diagnosis and screening of common disease such as Diabetic Retinopathy. Automated identification of exudates pathologies in retinopathy fundus images based on fuzzy c means clustering. This approach employs a unique sequential execution of morphological operators to extract fundus image features like vessels, red lesions, and white lesions together with texture feature analysis .Finally features selected are passed into the well-known support vector machine (SVM) classifier which classifies the images into normal and abnormal classes and abnormal regions can be extracted.

About the author










Prof.B.K Anoop A trabalhar como Professor Assistente, Departamento de Electrónica e Comunicação da Faculdade de Engenharia Vimal Jyothi, Chemperi Kannur. A sua área de investigação inclui Processamento de Sinal, Processamento Biomédico de Imagem. Actualmente, está a tirar o doutoramento na APJAKTU Kerala. Tem mais de 30 publicações.

Product details

Authors Anoo Balakrishnan Kadan, Anoop Balakrishnan Kadan, Dr Perumal Sankar, Dr.Perumal Sankar, Perumal Sankar
Publisher LAP Lambert Academic Publishing
 
Languages English
Product format Paperback / Softback
Released 04.12.2017
 
EAN 9786202069571
ISBN 9786202069571
No. of pages 68
Subjects Humanities, art, music > Philosophy > Miscellaneous
Non-fiction book > Philosophy, religion > Miscellaneous

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.