Online ISSN: 2515-8260

Keywords : DCT


An Analysis of Different Watermarking Schemes for Medical Image Authentication

Lalan Kumar; Kamred Udham Singh

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2250-2259

These days enormous amounts of information are at almost everyone’s disposal with a single click of a button, and that too on a hand-held device. Data can be present in various forms like still images and slides of pictures like a video or GIF, over various websites present on the Internet. Because of the excessive use of this data, it also becomes important to secure it as it can be duplicated, transformed, stolen, tampered or misused pretty easily. Recently, there has been a spike increase in the use of medical images in various E-health applications. In order to counter these potential threats, a number of watermarking techniques are being developed. A watermark is embedded in an image in the form of some pattern that can be used to authenticate the integrity of the image. This paper deals with the various algorithms that have been developed, proposed and utilized in recent years to solve the highly complex problems that have been faced while trying to secure the medical images from different kinds of threats. Alongside the survey of the techniques, this paper also goes through the concept of watermarking, properties of watermarking, various challenges faced by watermarking for medical images and the summary of different techniques for watermarking.

Detection of Microcalcifications in Digital Mammogram Using Curvelet Fractal Texture Features

Dr. Vimal Kumar M N; Divya M; Ilakkia M M; Jayanthi S; Dr Gomathi V

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 251-256

In this work, an attempt is made to Segment and find features of the segmented
mammogram Images and finally mammogram images are classified as normal and
abnormal. The mammogram images used for this work are considered from MIAS
Database. The database includes 322 digitized films and all the images are of size
1024x1024. It consists of 322 images (208 normal images and 114 abnormal images).
Initially, mammogram images are subjected to pre-processing using Discrete Cosine
Transform to enhance the edges of the mammograms. Then, sharpened images are cl
using the Fuzzy C-means Clustering algorithm. After segmentation, Curvelet coefficient
and fractal Dimension values are obtained using Discrete Curvelet Transformand Fractal
textures respectively. The average values of obtained curvelet coefficient and fractal
dimension values for both normal and abnormal mammogram images are compared.
Finally, The mammogram images are classified using an Ensemble Fully Complex-
Valued Relaxation Network Classifier. The Classifier is used foe the classification of the
mammogram images as normal and abnormal.