Evolution of Big Data in Medical Imaging Modalities to Extract Features Using Region Growing Segmentation, GLCM, and Discrete Wavelet Transform

2022 ◽  
pp. 455-482
Author(s):  
Yogesh Kumar Gupta
Keyword(s):  
Big Data ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Medical Images ◽  
Human Life ◽  
Image Data ◽  
Region Growing ◽  
Discrete Wavelet ◽  
Promising Area ◽  
Region Growing Segmentation

Big data refers to the massive amount of data from sundry sources (gregarious media, healthcare, different sensor, etc.) with very high velocity. Due to expeditious growth, the multimedia or image data has rapidly incremented due to the expansion of convivial networking, surveillance cameras, satellite images, and medical images. Healthcare is the most promising area where big data can be applied to make a vicissitude in human life. The process for analyzing the intricate data is mundanely concerned with the disclosing of hidden patterns. In healthcare fields capturing the visual context of any medical images, extraction is a well introduced word in digital image processing. The motive of this research is to present a detailed overview of big data in healthcare and processing of non-invasive medical images with the avail of feature extraction techniques such as region growing segmentation, GLCM, and discrete wavelet transform.

Evolution of Big Data in Medical Imaging Modalities to Extract Features Using Region Growing Segmentation, GLCM, and Discrete Wavelet Transform

2021 ◽  
pp. 41-78
Author(s):  
Yogesh Kumar Gupta
Keyword(s):  
Big Data ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Medical Images ◽  
Human Life ◽  
Image Data ◽  
Region Growing ◽  
Discrete Wavelet ◽  
Promising Area ◽  
Region Growing Segmentation

Big data refers to the massive amount of data from sundry sources (gregarious media, healthcare, different sensor, etc.) with very high velocity. Due to expeditious growth, the multimedia or image data has rapidly incremented due to the expansion of convivial networking, surveillance cameras, satellite images, and medical images. Healthcare is the most promising area where big data can be applied to make a vicissitude in human life. The process for analyzing the intricate data is mundanely concerned with the disclosing of hidden patterns. In healthcare fields capturing the visual context of any medical images, extraction is a well introduced word in digital image processing. The motive of this research is to present a detailed overview of big data in healthcare and processing of non-invasive medical images with the avail of feature extraction techniques such as region growing segmentation, GLCM, and discrete wavelet transform.

Shift-invariant discrete wavelet transform-based sparse fusion of medical images

2021 ◽  
Author(s):  
M. Munawwar Iqbal Ch ◽  
M. Mohsin Riaz ◽  
Naima Iltaf ◽  
Abdul Ghafoor ◽  
Nuwayrah Jawaid Saghir
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Medical Images ◽  
Discrete Wavelet

scholarly journals Design and Implementation of Lifting Based Wavelet and Adaptive LSB Steganography to Secret Data Sharing Through Image on FPGA

2018 ◽  
Vol 8 (03) ◽  
Author(s):  
Kokila B. Padeppagol ◽  
Sandhya Rani M H
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Image Data ◽  
Image Steganography ◽  
Cover Image ◽  
Discrete Wavelet ◽  
Least Significant Bit ◽  
Secret Data ◽  
Secret Image ◽  
Design And Implementation

Image steganography is an art of hiding images secretly within another image. There are several ways of performing image steganography; one among them is the spatial approach.The most popular spatial domain approach of image steganography is the Least Significant Bit (LSB) method, which hides the secret image pixel information in the LSB of the cover image pixel information. In this paper a LSB based steganography approach is used to design hardware architecture for the Image steganography. The Discrete Wavelet Transform (DWT) is used here to transform the cover image into higher and lower wavelet coefficients and use these coefficients in hiding the secret image. the design also includes encryption of secret image data, to provide a higher level of security to the secret image. The steganography system involving the stegno module and a decode module is designed here. The design was simulated, synthesized and implemented on Artix -7 FPGA. The operation hiding and retrieving images was successfully verified through simulations.

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