Palm Print Recognition Using Oriented Hausdorff Distance Transform

2011 ◽  
Author(s):  
MianMahmood Ali ◽  
Mubeen Ghafoor ◽  
Imtiaz A. Taj ◽  
Khizar Hayat
Keyword(s):  
Hausdorff Distance ◽  
Distance Transform ◽  
Palm Print

A Review on Palm Print Recognition Techniques

2017 ◽  
Vol 7 (3) ◽  
pp. 196-201 ◽  
Author(s):  
Vivek kr. Sharma ◽  
◽  
Nisha Vasudeva ◽  
Keyword(s):  
Palm Print

Level Set Method Optimized with the Euclidean Distance Transform

2019 ◽  
Author(s):  
Luis Fernando Segalla ◽  
Alexandre Zabot ◽  
Diogo Nardelli Siebert ◽  
Fabiano Wolf
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Euclidean Distance ◽  
Distance Transform ◽  
Euclidean Distance Transform

A Hybrid Approach for the Lung(s) Nodule Detection using the Deformable Model and Distance Transform

2020 ◽  
pp. -1--1
Author(s):  
Ayyaz Hussain ◽  
, Mohammed Alawairdhi ◽  
Fayez Alazemi ◽  
Sajid Khan ◽  
Muhammad Ramzan
Keyword(s):  
Hybrid Approach ◽  
Deformable Model ◽  
Distance Transform ◽  
Nodule Detection

Efficient Hausdorff Distance Calculation Algorithm Based on Z-Order and Octree

2018 ◽  
Vol 30 (10) ◽  
pp. 1794
Author(s):  
Dejun Zhang ◽  
Fazhi He ◽  
Long Tian ◽  
Zhuyang Xie ◽  
Lu Zou
Keyword(s):  
Hausdorff Distance ◽  
Calculation Algorithm ◽  
Distance Calculation

3‐D texture characterized by accessibility measurements, based on the grey weighted distance transform

Bioimaging ◽  
1994 ◽  
Vol 2 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Karel C Strasters ◽  
Arnold W M Smeulders ◽  
Hans T M van der Voort
Keyword(s):  
Distance Transform ◽  
Weighted Distance

scholarly journals Fully automated preoperative segmentation of temporal bone structures from clinical CT scans

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
C. A. Neves ◽  
E. D. Tran ◽  
I. M. Kessler ◽  
N. H. Blevins
Keyword(s):  
Facial Nerve ◽  
Inner Ear ◽  
Temporal Bone ◽  
Hausdorff Distance ◽  
Sigmoid Sinus ◽  
Ct Scans ◽  
Automated Segmentation ◽  
Dice Coefficient ◽  
Lateral Skull Base ◽  
End To End

AbstractMiddle- and inner-ear surgery is a vital treatment option in hearing loss, infections, and tumors of the lateral skull base. Segmentation of otologic structures from computed tomography (CT) has many potential applications for improving surgical planning but can be an arduous and time-consuming task. We propose an end-to-end solution for the automated segmentation of temporal bone CT using convolutional neural networks (CNN). Using 150 manually segmented CT scans, a comparison of 3 CNN models (AH-Net, U-Net, ResNet) was conducted to compare Dice coefficient, Hausdorff distance, and speed of segmentation of the inner ear, ossicles, facial nerve and sigmoid sinus. Using AH-Net, the Dice coefficient was 0.91 for the inner ear; 0.85 for the ossicles; 0.75 for the facial nerve; and 0.86 for the sigmoid sinus. The average Hausdorff distance was 0.25, 0.21, 0.24 and 0.45 mm, respectively. Blinded experts assessed the accuracy of both techniques, and there was no statistical difference between the ratings for the two methods (p = 0.93). Objective and subjective assessment confirm good correlation between automated segmentation of otologic structures and manual segmentation performed by a specialist. This end-to-end automated segmentation pipeline can help to advance the systematic application of augmented reality, simulation, and automation in otologic procedures.

scholarly journals Retraining Convolutional Neural Networks for Specialized Cardiovascular Imaging Tasks: Lessons from Tetralogy of Fallot

2021 ◽  
Author(s):  
Animesh Tandon ◽  
Navina Mohan ◽  
Cory Jensen ◽  
Barbara E. U. Burkhardt ◽  
Vasu Gooty ◽  
...  
Keyword(s):  
Tetralogy Of Fallot ◽  
Hausdorff Distance ◽  
Volumetric Analysis ◽  
Dice Similarity Coefficient ◽  
Algorithm Performance ◽  
Comprehensive Method ◽  
Combined Use ◽  
Median Error ◽  
Structural Abnormalities ◽  
Testing Data

AbstractVentricular contouring of cardiac magnetic resonance imaging is the gold standard for volumetric analysis for repaired tetralogy of Fallot (rTOF), but can be time-consuming and subject to variability. A convolutional neural network (CNN) ventricular contouring algorithm was developed to generate contours for mostly structural normal hearts. We aimed to improve this algorithm for use in rTOF and propose a more comprehensive method of evaluating algorithm performance. We evaluated the performance of a ventricular contouring CNN, that was trained on mostly structurally normal hearts, on rTOF patients. We then created an updated CNN by adding rTOF training cases and evaluated the new algorithm’s performance generating contours for both the left and right ventricles (LV and RV) on new testing data. Algorithm performance was evaluated with spatial metrics (Dice Similarity Coefficient (DSC), Hausdorff distance, and average Hausdorff distance) and volumetric comparisons (e.g., differences in RV volumes). The original Mostly Structurally Normal (MSN) algorithm was better at contouring the LV than the RV in patients with rTOF. After retraining the algorithm, the new MSN + rTOF algorithm showed improvements for LV epicardial and RV endocardial contours on testing data to which it was naïve (N = 30; e.g., DSC 0.883 vs. 0.905 for LV epicardium at end diastole, p < 0.0001) and improvements in RV end-diastolic volumetrics (median %error 8.1 vs 11.4, p = 0.0022). Even with a small number of cases, CNN-based contouring for rTOF can be improved. This work should be extended to other forms of congenital heart disease with more extreme structural abnormalities. Aspects of this work have already been implemented in clinical practice, representing rapid clinical translation. The combined use of both spatial and volumetric comparisons yielded insights into algorithm errors.

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