A Competitive Study of Graph Reduction Methods for Min S-T Cut Image Segmentation

2014 ◽  
Vol 19 (2-3) ◽  
pp. 97-105
Author(s):  
Tomasz Węgliński ◽  
Anna Fabijańska ◽  
Jarosław Goclawski
Keyword(s):  
Graph Cut ◽  
Size Reduction ◽  
Practical Application ◽  
Graph Reduction ◽  
Segmentation Algorithms ◽  
Flow Algorithm ◽  
Graph Size ◽  
Reduction Methods ◽  
Graph Cut Segmentation ◽  
Min Cut

Abstract When applied to the segmentation of 3D medical images, graph-cut segmentation algorithms require an extreme amount of memory and time resources in order to represent the image graph and to perform the necessary processing on the graph. These requirements actually exclude the graph-cut based approaches from their practical application. Hence, there is a need to develop the dedicated graph size reduction methods. In this paper, several techniques for the graph size reduction are proposed. These apply the idea of superpixels. In particular, two methods for superpixel creation are introduced. The results of applying the proposed methods to the segmentation of CT datasets using min-cut/max-flow algorithm are presented, compared and discussed.

GRAPH CUT SEGMENTATION METHOD AND ITS APPLICATION IN MEDICAL IMAGES

2015 ◽  
Vol 75 (4) ◽  
Author(s):  
Alireza Norouzi ◽  
Ismail Mat Amin ◽  
Mohd Shafry Mohd Rahim ◽  
Abdolvahab Ehsani Rad
Keyword(s):  
Cost Function ◽  
Medical Images ◽  
Graph Cut ◽  
Segmentation Method ◽  
Interactive Segmentation ◽  
Optimum Result ◽  
Image Pixels ◽  
The Cost ◽  
Graph Cut Segmentation ◽  
Min Cut

Graph cut is an interactive segmentation method. It works based on preparing graph from image and finds the minimum cut for the graph. The edges value is calculated based on belonging a pixel to object or background. The advantage of this method is using the cost function. If the cost function is clearly described, graph cut is presents a generally optimum result. In this paper graph concepts and preparing graph according to image pixels is described. Preparing different edges and performing min cut/max flow is explained. Finally, the method is applied on some medical images.  

Comparison of fuzzy connectedness and graph cut segmentation algorithms

2011 ◽  
Author(s):  
Krzysztof C. Ciesielski ◽  
Jayaram K. Udupa ◽  
A. X. Falcão ◽  
P. A. V. Miranda
Keyword(s):  
Graph Cut ◽  
Fuzzy Connectedness ◽  
Segmentation Algorithms ◽  
Graph Cut Segmentation

Integrating Improved U-Net and Continuous Maximum Flow Algorithm for 3D Brain Tumor Image Segmentation

2020 ◽  
Vol 64 (4) ◽  
pp. 40412-1-40412-11
Author(s):  
Kexin Bai ◽  
Qiang Li ◽  
Ching-Hsin Wang
Keyword(s):  
Brain Tumor ◽  
Data Augmentation ◽  
A Priori ◽  
Class Imbalance ◽  
Maximum Flow ◽  
Magnetic Resonance Images ◽  
Tumor Segmentation ◽  
Similarity Coefficients ◽  
Segmentation Algorithms ◽  
Flow Algorithm

Abstract To address the issues of the relatively small size of brain tumor image datasets, severe class imbalance, and low precision in existing segmentation algorithms for brain tumor images, this study proposes a two-stage segmentation algorithm integrating convolutional neural networks (CNNs) and conventional methods. Four modalities of the original magnetic resonance images were first preprocessed separately. Next, preliminary segmentation was performed using an improved U-Net CNN containing deep monitoring, residual structures, dense connection structures, and dense skip connections. The authors adopted a multiclass Dice loss function to deal with class imbalance and successfully prevented overfitting using data augmentation. The preliminary segmentation results subsequently served as the a priori knowledge for a continuous maximum flow algorithm for fine segmentation of target edges. Experiments revealed that the mean Dice similarity coefficients of the proposed algorithm in whole tumor, tumor core, and enhancing tumor segmentation were 0.9072, 0.8578, and 0.7837, respectively. The proposed algorithm presents higher accuracy and better stability in comparison with some of the more advanced segmentation algorithms for brain tumor images.

Survey of code-size reduction methods

2003 ◽  
Vol 35 (3) ◽  
pp. 223-267 ◽  
Author(s):  
Árpád Beszédes ◽  
Rudolf Ferenc ◽  
Tibor Gyimóthy ◽  
André Dolenc ◽  
Konsta Karsisto
Keyword(s):  
Size Reduction ◽  
Code Size ◽  
Reduction Methods

scholarly journals Pedestrian segmentation based on a spatio-temporally consistent graph-cut with optimal transport

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Yang Yu ◽  
Yasushi Makihara ◽  
Yasushi Yagi
Keyword(s):  
Optimal Transport ◽  
Graph Cut ◽  
Temporal Consistency ◽  
Semantic Level ◽  
Spatial Consistency ◽  
Segmentation Accuracy ◽  
Spatio Temporal ◽  
Accuracy Indices ◽  
Graph Cut Segmentation ◽  
Data Term

AbstractWe address a method of pedestrian segmentation in a video in a spatio-temporally consistent way. For this purpose, given a bounding box sequence of each pedestrian obtained by a conventional pedestrian detector and tracker, we construct a spatio-temporal graph on a video and segment each pedestrian on the basis of a well-established graph-cut segmentation framework. More specifically, we consider three terms as an energy function for the graph-cut segmentation: (1) a data term, (2) a spatial pairwise term, and (3) a temporal pairwise term. To maintain better temporal consistency of segmentation even under relatively large motions, we introduce a transportation minimization framework that provides a temporal correspondence. Moreover, we introduce the edge-sticky superpixel to maintain the spatial consistency of object boundaries. In experiments, we demonstrate that the proposed method improves segmentation accuracy indices, such as the average and weighted intersection of union on TUD datasets and the PETS2009 dataset at both the instance level and semantic level.

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