scholarly journals Gradient-based kernel selection technique for tumour detection and extraction of medical images using graph cut

2020 ◽  
Vol 14 (1) ◽  
pp. 84-93 ◽  
Author(s):  
Jyotsna Dogra ◽  
Shruti Jain ◽  
Meenakshi Sood
Keyword(s):  
Medical Images ◽  
Graph Cut ◽  
Selection Technique ◽  
Tumour Detection ◽  
Kernel Selection ◽  
Gradient Based

Brain Tumor Detection from MR Images Employing Fuzzy Graph Cut Technique

2020 ◽  
Vol 13 (3) ◽  
pp. 362-369 ◽  
Author(s):  
Jyotsna Dogra ◽  
Shruti Jain ◽  
Ashutosh Sharma ◽  
Rajiv Kumar ◽  
Meenakshi Sood
Keyword(s):  
Performance Metrics ◽  
Brain Mri ◽  
Tumor Detection ◽  
Graph Cut ◽  
Tumor Segmentation ◽  
Low Grade ◽  
Seed Selection ◽  
Selection Technique ◽  
Kernel Selection ◽  
Mri Image

Background: This research aims at the accurate selection of the seed points from the brain MRI image for the detection of the tumor region. Since, the conventional way of manual seed selection leads to inappropriate tumor extraction therefore, fuzzy clustering technique is employed for the accurate seed selection for performing the segmentation through graph cut method. Methods: In the proposed method Fuzzy Kernel Seed Selection technique is used to define the complete brain MRI image into different groups of similar intensity. Among these groups the most accurate kernels are selected empirically that show highest resemblance with the tumor. The concept of fuzziness helps making the selection even at the boundary regions. Results: The proposed Fuzzy kernel selection technique is applied on the BraTS dataset. Among the four modalities, the proposed technique is applied on Flair images. This dataset consists of Low Grade Glioma (LGG) and High Grade Glioma (HGG) tumor images. The experiment is conducted on more than 40 images and validated by evaluating the following performance metrics: 1. Disc Similarity Coefficient (DSC), 2. Jaccard Index (JI) and 3. Positive Predictive Value (PPV). The mean DSC and PPV values obtained for LGG images are 0.89 and 0.87 respectively; and for HGG images it is 0.92 and 0.90 respectively. Conclusion: On comparing the proposed Fuzzy kernel selection graph cut technique approach with the existing techniques it is observed that the former provides an automatic accurate tumor detection. It is highly efficient and can provide a better performance for HGG and LGG tumor segmentation in clinical application.

Glioma extraction from MR images employing Gradient Based Kernel Selection Graph Cut technique

2019 ◽  
Vol 36 (5) ◽  
pp. 875-891 ◽  
Author(s):  
Jyotsna Dogra ◽  
Shruti Jain ◽  
Meenakshi Sood
Keyword(s):  
Graph Cut ◽  
Mr Images ◽  
Kernel Selection ◽  
Gradient Based

Graph-Cut Energy Minimization for Object Extraction in MRCP Medical Images

2010 ◽  
Vol 36 (1) ◽  
pp. 311-320 ◽  
Author(s):  
Rajasvaran Logeswaran ◽  
Dongho Kim ◽  
Jungwhan Kim ◽  
Keechul Jung ◽  
Bundo Song
Keyword(s):  
Energy Minimization ◽  
Medical Images ◽  
Graph Cut ◽  
Object Extraction

scholarly journals Automatized Evaluation of the Left Ventricular Ejection Fraction from Echocardiographic Images Using Graph Cut

2014 ◽  
Author(s):  
Michael Bernier ◽  
Pierre-Marc Jodoin ◽  
Alain Lalande
Keyword(s):  
Ejection Fraction ◽  
Signal To Noise Ratio ◽  
Graph Cut ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction ◽  
Gradient Based ◽  
Echocardiographic Images ◽  
3D Volume ◽  
Infinite Energy

In this paper, we present a fast and interactive graph cut method for 3D segmentation of the endocardial wall of the left ventricle (LV) given 3D echocardiographic images. This is a challenging task due to the poor contrast and the low signal-to-noise ratio typical of echocardiographic images. The method is carried out in 3 steps. First, 3D sampling of the LV cavity is made in a spherical-cylindrical coordinate system. Then, a gradient-based energy term is assigned to each voxel, some of which being given an infinite energy to make sure the resulting volume passes through key anatomical points. Then, a graph-cut procedure provides delineation of the endocardial surface. Results obtained on 30 exams from the 2014 CETUS MICCAI challenge dataset reveal that our method takes between 5 and 10 seconds to segment a 3D volume with an overall mean surface distance lower than 2.3 mm and an ejection fraction error of less than 5% compared to a manual tracing by an expert.

scholarly journals Gradient-based enhancement of tubular structures in medical images

2015 ◽  
Vol 26 (1) ◽  
pp. 19-29 ◽  
Author(s):  
Rodrigo Moreno ◽  
Örjan Smedby
Keyword(s):  
Medical Images ◽  
Tubular Structures ◽  
Gradient Based
Sign in / Sign up

Export Citation Format

Share Document