Analysis of polycystic kidney disease in medical ultrasound images
by Prema T. Akkasaligar; Sunanda Biradar
International Journal of Medical Engineering and Informatics (IJMEI), Vol. 10, No. 1, 2018

Abstract: The growth of kidney diseases has gradually increased in recent years. Ultrasound imaging provides the internal structure of the body to detect eventually diseases or abnormal tissues non-invasively. Segmentation of required region in ultrasound images is one of the challenging tasks. The proposed method focuses on classification of medical ultrasound images of kidney as cystic and polycystic types. Segmentation is performed using gradient vector force (GVF) snakes. Before segmentation, speckle noise is removed using Gaussian filter and contrast is enhanced. We have segmented normal, cystic and polycystic kidney ultrasound images effectively using GVF snakes. We have also carried out segmentation using morphological operations which requires a user intervention during the process of segmentation. Geometrical features are used with k-NN for classifying medical US images of kidney as normal, single cystic and polycystic types for segmented regions .The proposed method has applications in analysis of organ morphology and realising quantitative measurements.

Online publication date: Wed, 28-Feb-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Medical Engineering and Informatics (IJMEI):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com