Quality optimized medical image steganography based on edge detection and hamming code

2015 ◽  
Author(s):  
Hayat Al-Dmour ◽  
Ahmed Al-Ani
Keyword(s):  
Edge Detection ◽  
Medical Image ◽  
Image Steganography ◽  
Hamming Code

scholarly journals Enhancing Ant-Based Algorithms for Medical Image Edge Detection by Admissible Perturbations of Demicontractive Mappings

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 885
Author(s):  
Vasile Berinde ◽  
Cristina Ţicală
Keyword(s):  
Edge Detection ◽  
Medical Image ◽  
Test Functions ◽  
Head Ct ◽  
X Ray ◽  
Numerical Tests ◽  
Image Edge Detection ◽  
Evaluation Of Parameters ◽  
Image Edge ◽  
Demicontractive Mappings

The aim of this paper is to show analytically and empirically how ant-based algorithms for medical image edge detection can be enhanced by using an admissible perturbation of demicontractive operators. We thus complement the results reported in a recent paper by the second author and her collaborators, where they used admissible perturbations of demicontractive mappings as test functions. To illustrate this fact, we first consider some typical properties of demicontractive mappings and of their admissible perturbations and then present some appropriate numerical tests to illustrate the improvement brought by the admissible perturbations of demicontractive mappings when they are taken as test functions in ant-based algorithms for medical image edge detection. The edge detection process reported in our study considers both symmetric (Head CT and Brain CT) and asymmetric (Hand X-ray) medical images. The performance of the algorithm was tested visually with various images and empirically with evaluation of parameters.

Adaptive Edge Detection Method Towards Features Extraction From Diverse Medical Imaging Technologies

2018 ◽  
pp. 1245-1278
Author(s):  
Indra Kanta Maitra ◽  
Samir Kumar Bandhyopadhyaay
Keyword(s):  
Edge Detection ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Detection Techniques ◽  
Robust Segmentation ◽  
Image Characteristics ◽  
Edge Detection Method ◽  
Medical Image Enhancement

The CAD is a relatively young interdisciplinary technology, has had a tremendous impact on medical diagnosis specifically cancer detection. The accuracy of CAD to detect abnormalities on medical image analysis requires a robust segmentation algorithm. To achieve accurate segmentation, an efficient edge-detection algorithm is essential. Medical images like USG, X-Ray, CT and MRI exhibit diverse image characteristics but are essentially collection of intensity variations from which specific abnormalities are needed to be isolated. In this chapter a robust medical image enhancement and edge detection algorithm is proposed, using tree-based adaptive thresholding technique. It has been compared with different classical edge-detection techniques using one sample two tail t-test to exam whether the null hypothesis can be supported. The proposed edge-detection algorithm showing 0.07 p-values and 2.411 t-stat where α = 0.025. Moreover the proposed edge is single pixeled and connected which is very significant for medical edge detection.

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