scholarly journals ANALYSIS MEDICAL AND STEREOSCOPIC IMAGES BY E-MEDICUS SYSTEM

2018 ◽  
Vol 8 (2) ◽  
pp. 54-57
Author(s):  
Tomasz Rymarczyk
Keyword(s):  
Image Segmentation ◽  
Level Set Method ◽  
Level Set ◽  
Arbitrary Number ◽  
Medical Images ◽  
Stereoscopic Images ◽  
Piecewise Constant ◽  
Segmentation Problem

In this work, there were implemented methods to analyze and segmentation medical images by using different kind of algorithms. The solution shows the architecture of the system collecting and analyzing data. There was tried to develop an algorithm for level set method applied to piecewise constant image segmentation. These algorithms are needed to identify arbitrary number of phases for the segmentation problem. With the use of modern algorithms, it can obtain a quicker diagnosis and automatically marking areas of the interest region in medical images.

scholarly journals NEURAL NETWORK AND CONVOLUTIONAL ALGORITH TO EXTRACT SHAPES BY E-MEDICUS APPLICATION

2018 ◽  
Vol 8 (3) ◽  
pp. 39-42
Author(s):  
Tomasz Rymarczyk ◽  
Barbara Stefaniak ◽  
Przemysław Adamkiewicz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Segmentation ◽  
Deep Learning ◽  
Level Set Method ◽  
Level Set ◽  
Arbitrary Number ◽  
Medical Images ◽  
Learning Methods ◽  
Segmentation Problem

The solution shows the architecture of the system collecting and analyzing data. There was tried to develop algorithms to image segmentation. These algorithms are needed to identify arbitrary number of phases for the segmentation problem. With the use of algorithms such as the level set method, neural networks and deep learning methods, it can obtain a quicker diagnosis and automatically marking areas of the interest region in medical images.

Two-Step Modified Nash Equilibrium Method for Medical Image Segmentation Based on an Improved C-V Model

2018 ◽  
Vol 8 (9) ◽  
pp. 1826-1834
Author(s):  
Tian Chi Zhang ◽  
Jian Pei Zhang ◽  
Jing Zhang ◽  
Melvyn L. Smith
Keyword(s):  
Image Segmentation ◽  
Nash Equilibrium ◽  
Level Set ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Clustering Problem ◽  
Object Contour ◽  
Segmentation Methods ◽  
Maximum Similarity ◽  
Segmentation Problem

One of the most established region-based segmentation methods is the region based C-V model. This method formulates the image segmentation problem as a level set or improved level set clustering problem. However, the existing level set C-V model fails to perform well in the presence of noisy and incomplete data or when there is similarity between the objects and background, especially for clustering or segmentation tasks in medical images where objects appear vague and poorly contrasted in greyscale. In this paper, we modify the level set C-V model using a two-step modified Nash equilibrium approach. Firstly, a standard deviation using an entropy payoff approach is employed and secondly a two-step similarity clustering based approach is applied to the modified Nash equilibrium. One represents a maximum similarity within the clustered regions and the other the minimum similarity between the clusters. Finally, an improved C-V model based on a two-step modified Nash equilibrium is proposed to smooth the object contour during the image segmentation. Experiments demonstrate that the proposed method has good performance for segmenting noisy and poorly contrasting regions within medical images.

scholarly journals LEVEL SETS AND COMPUTATIONAL INTELLIGENCE ALGORITHMS TO MEDICAL IMAGE ANALYSIS IN E-MEDICUS SYSTEM

2017 ◽  
Vol 7 (1) ◽  
pp. 63-67
Author(s):  
Tomasz Rymarczyk
Keyword(s):  
Image Segmentation ◽  
Computational Intelligence ◽  
Level Sets ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Piecewise Constant ◽  
Multiple Regions ◽  
Statistical Algorithms ◽  
Segmentation Problem

In this work, there were implemented methods to analyze and segmentation medical images by using topological, statistical algorithms and artificial intelligence techniques. The solution shows the architecture of the system collecting and analyzing data. There was tried to develop an algorithm for level set method (LSM) applied to piecewise constant image segmentation. These algorithms are needed to identify arbitrary number of phases for the segmentation problem. The image segmentation refers to the process of partitioning a digital image into multiple regions. There is typically used to locate objects and boundaries in images. There was also shown an algorithm for analyzing medical images using a neural network MLP.

New normalized nonlocal hybrid level set method for image segmentation

2017 ◽  
Vol 32 (4) ◽  
pp. 407-421
Author(s):  
Qiong Lou ◽  
Jia-lin Peng ◽  
De-xing Kong ◽  
Chun-lin Wang
Keyword(s):  
Image Segmentation ◽  
Level Set Method ◽  
Level Set

A New Image Segmentation Method Based on Improved Fast Implicit Level Set Scheme in X/γ-Ray Inspection System

2011 ◽  
Vol 103 ◽  
pp. 705-710 ◽  
Author(s):  
Yu Jie Li ◽  
Hui Min Lu ◽  
Li Feng Zhang ◽  
Shi Yuan Yang ◽  
Serikawa Seiichi
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Active Contours ◽  
Operator Splitting ◽  
Inspection System ◽  
Segmentation Method ◽  
Piecewise Constant ◽  
Γ Ray ◽  
Luggage Inspection ◽  
Segmentation Of Images

Digital X/γ-ray imaging technology has been widely used to help people deliver effective and reliable security in airports, train stations, and public buildings. Nowadays, luggage inspection system with digital radiographic/computed tomography (DR/CT) represents a most advanced nondestructive inspection technology in aviation system, which is capable of automatically discerning interesting regions in the luggage objects with CT subsystem. In this paper, we propose a new model for active contours to detect luggage objects in the system, in order to facilitate people to identify the things in luggage. The proposed method is based on techniques of piecewise constant and piecewise smooths Chan-Vese Model, semi-implicit additive operator splitting (AOS) scheme for image segmentation. Different from traditional models, the fast implicit level set scheme (FILS) is ordinary differential equation (ODE). Characterized by no need of any pre-information of topology of images and efficient segmentation of images with complex topology, the FILS scheme is fast more than traditional level set scheme 30 times. At the same time, it performs well in image segmentation of DR images in our experiments.

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