Active contour based segmentation of low-contrast medical images

2000 ◽  
Author(s):  
M. Piotrowski
Keyword(s):  
Active Contour ◽  
Medical Images ◽  
Low Contrast

Enhancement Algorithm Combining Pseudocolor Processing and Marker-Based Watershed Transform for Medical Image

2013 ◽  
Vol 655-657 ◽  
pp. 1953-1956
Author(s):  
Chun Yu Ning ◽  
Shu Fen Liu
Keyword(s):  
Medical Image ◽  
Clinical Medicine ◽  
Medical Images ◽  
Fusion System ◽  
Watershed Transform ◽  
Low Contrast ◽  
Different Types ◽  
Two Parameters ◽  
Medical Image Enhancement ◽  
Enhancement Algorithm

Medical images play a very important role in clinical medicine. They often have the features of low contrast, narrow gray scale and edge blurring between tissues. Aimed at the disadvantages, the paper presents a new medical image enhancement algorithm combining pseudocolor processing and marker-based watershed transform. The new algorithm and traditional pseudocolor processing in frequency domain are implemented using MATLAB, and their effectivity is evaluated by two parameters, mean and entropy. Results show that the proposed enhancement algorithm can improve the visual effect of different types of medical images, especially for the tissues in the low contrast regions in the image. The output image is more suitable for doctor’s diagnosing. The algorithm has been used in the medical image fusion system we developed, and the performance is very satisfactory.

scholarly journals Extraction and Visualization of Ocular Blood Vessels in 3D Medical Images Based on Geometric Transformation Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhike Zhang ◽  
Shuixin Zhang ◽  
Hongyu Feng
Keyword(s):  
Blood Vessels ◽  
Input Signal ◽  
Medical Images ◽  
Vessel Segmentation ◽  
New Method ◽  
Geometric Transformation ◽  
High Contrast ◽  
Low Contrast ◽  
Resonance Response ◽  
Local Image

Data extraction and visualization of 3D medical images of ocular blood vessels are performed by geometric transformation algorithm, which first performs random resonance response in a global sense to achieve detection of high-contrast coarse blood vessels and then redefines the input signal as a local image shielding the global detection result to achieve enhanced detection of low-contrast microfine vessels and complete multilevel random resonance segmentation detection. Finally, a random resonance detection method for fundus vessels based on scale decomposition is proposed, in which the images are scale decomposed, the high-frequency signals containing detailed information are randomly resonantly enhanced to achieve microfine vessel segmentation detection, and the final vessel segmentation detection results are obtained after fusing the low-frequency image signals. The optimal stochastic resonance response of the nonlinear model of neurons in the global sense is obtained to detect the high-grade intensity signal; then, the input signal is defined as a local image with high-contrast blood vessels removed, and the parameters are optimized before the detection of the low-grade intensity signal. Finally, the multilevel random resonance response is fused to obtain the segmentation results of the fundus retinal vessels. The sensitivity of the multilevel segmentation method proposed in this paper is significantly improved compared with the global random resonance results, indicating that the method proposed in this paper has obvious advantages in the segmentation of vessels with low-intensity levels. The image library was tested, and the experimental results showed that the new method has a better segmentation effect on low-contrast microscopic blood vessels. The new method not only makes full use of the noise for weak signal detection and segmentation but also provides a new idea of how to achieve multilevel segmentation and recognition of medical images.

An active contour model for medical image segmentation using a quaternion framework

2020 ◽  
Vol 2020 (10) ◽  
pp. 62-1-62-6
Author(s):  
V. Voronin ◽  
M. Zhdanova ◽  
E. Semenishchev ◽  
A. Zelensky ◽  
S. Agaian
Keyword(s):  
Active Contour ◽  
Color Image ◽  
Active Contour Model ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Local Polynomial ◽  
Current State ◽  
Contour Model ◽  
Hamiltonian Quaternions ◽  
Algebra Method

This paper presents a new method for segmenting medical images is based on Hamiltonian quaternions and the associative algebra, method of the active contour model and LPA-ICI (local polynomial approximation - the intersection of confidence intervals) anisotropic gradient. Since for segmentation tasks, the image is usually converted to grayscale, this leads to the loss of important information about color, saturation, and other important information associated color. To solve this problem, we use the quaternion framework to represent a color image to consider all three channels simultaneously when segmenting the RGB image. As a method of noise reduction, adaptive filtering based on local polynomial estimates using the ICI rule is used. The presented new approach allows obtaining clearer and more detailed boundaries of objects of interest. The experiments performed on real medical images (Z-line detection) show that our segmentation method of more efficient compared with the current state-of-art methods.

scholarly journals Nonlocal Means-Based Denoising for Medical Images

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Ke Lu ◽  
Ning He ◽  
Liang Li
Keyword(s):  
Medical Imaging ◽  
Medical Images ◽  
Random Noise ◽  
Imaging Analysis ◽  
Low Contrast ◽  
Nonlocal Means ◽  
Noisy Pixel ◽  
Improved Performance ◽  
Adaptive Denoising ◽  
State Of Art

Medical images often consist of low-contrast objects corrupted by random noise arising in the image acquisition process. Thus, image denoising is one of the fundamental tasks required by medical imaging analysis. Nonlocal means (NL-means) method provides a powerful framework for denoising. In this work, we investigate an adaptive denoising scheme based on the patch NL-means algorithm for medical imaging denoising. In contrast with the traditional NL-means algorithm, the proposed adaptive NL-means denoising scheme has three unique features. First, we use a restricted local neighbourhood where the true intensity for each noisy pixel is estimated from a set of selected neighbouring pixels to perform the denoising process. Second, the weights used are calculated thanks to the similarity between the patch to denoise and the other patches candidates. Finally, we apply the steering kernel to preserve the details of the images. The proposed method has been compared with similar state-of-art methods over synthetic and real clinical medical images showing an improved performance in all cases analyzed.

Sign in / Sign up

Export Citation Format

Share Document