A Statistical Dominance Algorithm for Edge Detection and Segmentation of Medical Images

2016 ◽  
pp. 3-14 ◽  
Author(s):  
Adam Piórkowski
Keyword(s):  
Edge Detection ◽  
Medical Images

Algorithms for segmentation and recognition of objects on medical images based on chiarlet transformation and neural networks

2020 ◽  
pp. 35-45
Author(s):  
Y.A. Hamad ◽  
K.V. Simonov ◽  
A.S. Kents
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Edge Detection ◽  
Medical Images ◽  
Classification Algorithms ◽  
Visual Data ◽  
Recognition Of Objects

The paper considers general approaches to image processing, analysis of visual data and computer vision. The main methods for detecting features and edges associated with these approaches are presented. A brief description of modern edge detection and classification algorithms suitable for isolating and characterizing the type of pathology in the lungs in medical images is also given.

scholarly journals Effect of morphological algorithms on medical imaging

2021 ◽  
Vol 8 (3) ◽  
pp. 1-8
Author(s):  
Cuong Phan Viet ◽  
Thao Ho Thi ◽  
Anh Le Tuan ◽  
Ha Nguyen Hong ◽  
Thanh Ha Quang
Keyword(s):  
Medical Imaging ◽  
Edge Detection ◽  
Medical Images ◽  
Computer Software ◽  
Diagnosis And Treatment ◽  
Detection Methods ◽  
Morphological Operators

Handling and improving the quality of medical images with the help of computer software is one of the important stages in the diagnosis and treatment. In this article, we focus on describing the new morphological algorithms by ITK (Insight Segmentation and Registration Toolkit). These morphological operators eliminate noise, detect good edges, and overcome the drawback of traditional edge detection methods.

A new quantum edge detection algorithm for medical images

2009 ◽  
Author(s):  
Xiaowei Fu ◽  
Mingyue Ding ◽  
Yangguang Sun ◽  
Shaobin Chen
Keyword(s):  
Edge Detection ◽  
Medical Images ◽  
Detection Algorithm ◽  
Edge Detection Algorithm

Non-Subsampled Contourlet Transform-Based Effective Denoising of Medical Images

2019 ◽  
pp. 155-183
Author(s):  
Karthikeyan P. ◽  
Vasuki S. ◽  
Karthik K.
Keyword(s):  
Image Analysis ◽  
Edge Detection ◽  
Image Enhancement ◽  
Image Denoising ◽  
Medical Images ◽  
Noise Removal ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Shift Invariance ◽  
Image Edges

Noise removal in medical images remains a challenge for the researchers because noise removal introduces artifacts and blurring of the image. Developing medical image denoising algorithm is a difficult operation because a tradeoff between noise reduction and the preservation of actual features of image has to be made in a way that enhances and preserves the diagnostically relevant image content. A special member of the emerging family of multiscale geometric transforms is the contourlet transform which effectively captures the image edges and contours. This overcomes the limitations of the existing method of denoising using wavelet and curvelet. But due to down sampling and up sampling, the contourlet transform is shift-variant. However, shift-invariance is desirable in image analysis applications such as edge detection, contour characterization, and image enhancement. In this chapter, nonsubsampled contourlet transform (shift-invariance transform)-based denoising is presented which more effectively represents edges than contourlet transform.

Edge Detection on Medical Images Using Intuitionistic Fuzzy Logic

2021 ◽  
pp. 236-246
Author(s):  
Nidhi Tripathi ◽  
Deepak Kumrawat ◽  
Venkata Keerthi Gottimukkala ◽  
S. Jeevaraj ◽  
W. Wilfred Godfrey
Keyword(s):  
Fuzzy Logic ◽  
Edge Detection ◽  
Medical Images ◽  
Intuitionistic Fuzzy ◽  
Intuitionistic Fuzzy Logic

Integrating Morphological Edge Detection and Mutual Information for Nonrigid Registration of Medical Images

2019 ◽  
Vol 15 (3) ◽  
pp. 292-300 ◽  
Author(s):  
Vivek Aggarwal ◽  
Anupama Gupta
Keyword(s):  
Mutual Information ◽  
Edge Detection ◽  
Visual Information ◽  
Medical Images ◽  
Similarity Index ◽  
Structural Similarity ◽  
Magnetic Resonance Images ◽  
Registration Accuracy ◽  
Measure Function ◽  
Improved Performance

Background: Medical images are widely used within healthcare and medical research. There is an increased interest in precisely correlating information in these images through registration techniques for investigative and therapeutic purposes. This work proposes and evaluates an improved measure function for registration of carotid ultrasound and magnetic resonance images (MRI) taken at different times. Methods: To achieve this, a morphological edge detection operator has been designed to extract the vital edge information from images which is integrated with the Mutual Information (MI) to carry out the registration process. The improved performance of proposed registration measure function is demonstrated using four quality metrics: Correlation Coefficient (CC), Structural Similarity Index (SSIM), Visual Information Fidelity (VIF) and Gradient Magnitude Similarity Deviation (GMSD). The qualitative validation has also been done through visual inspection of the registered image pairs by clinical radiologists. Results: The experimental results showed that the proposed method outperformed the existing method (based on integrated MI and standard edge detection) for both ultrasound and MR images in terms of CC by about 4.67%, SSIM by 3.21%, VIF by 18.5%, and decreased GMSD by 37.01%. Whereas, in comparison to the standard MI based method, the proposed method has increased CC by 16.29%, SSIM by 16.13%, VIF by 52.56% and decreased GMSD by 66.06%, approximately. Conclusion: Thus, the proposed method improves the registration accuracy when the original images are corrupted by noise, have low intensity values or missing data.

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