Gray Hausdorff distance measure for medical image comparison in dermatology: Evaluation of treatment effectiveness by image similarity

2012 ◽  
Vol 19 (1) ◽  
pp. e498-e506 ◽  
Author(s):  
Panagiota Spyridonos ◽  
Georgios Gaitanis ◽  
Ioannis D. Bassukas ◽  
Margaret Tzaphlidou
Keyword(s):  
Hausdorff Distance ◽  
Medical Image ◽  
Distance Measure ◽  
Treatment Effectiveness ◽  
Image Similarity ◽  
Image Comparison

scholarly journals Hausdorff distance-based multiresolution maps applied to image similarity measure

2007 ◽  
Vol 55 (3) ◽  
pp. 164-174 ◽  
Author(s):  
E Baudrier ◽  
G Millon ◽  
F Nicolier ◽  
R Seulin ◽  
S Ruan
Keyword(s):  
Similarity Measure ◽  
Hausdorff Distance ◽  
Image Similarity

A Novel Model for Medical Image Similarity Retrieval

2013 ◽  
pp. 595-606 ◽  
Author(s):  
Pengyuan Li ◽  
Haiwei Pan ◽  
Jianzhong Li ◽  
Qilong Han ◽  
Xiaoqin Xie ◽  
...  
Keyword(s):  
Medical Image ◽  
Image Similarity ◽  
Similarity Retrieval ◽  
Novel Model

Image thresholding based on template matching with arctangent Hausdorff distance measure

2013 ◽  
Vol 51 (5) ◽  
pp. 600-609 ◽  
Author(s):  
Yaobin Zou ◽  
Fangmin Dong ◽  
Bangjun Lei ◽  
Lulu Fang ◽  
Shuifa Sun
Keyword(s):  
Hausdorff Distance ◽  
Template Matching ◽  
Distance Measure ◽  
Image Thresholding

scholarly journals Segmentation of Skull-infiltrated Tumors Using ITK: Methods and Validation

2005 ◽  
Author(s):  
Aleksandra Popovic ◽  
Martin Engelhardt ◽  
Klaus Radermacher
Keyword(s):  
Computed Tomography ◽  
Hausdorff Distance ◽  
Distance Measure ◽  
Ct Images ◽  
Similarity Coefficient ◽  
Simulated Dataset ◽  
Dice Similarity Coefficient ◽  
Validation Metrics ◽  
Chi Squared ◽  
Sensitivity Specificity

Methods for segmentation of skull infiltrated tumors in Computed Tomography (CT) images using Insight Segmentation and Registration Toolkit ITK (www.itk.org) are presented. Pipelines of filters and algorithms from ITK are validated on the basis of different criteria: sensitivity, specificity, dice similarity coefficient, Chi-squared, and Hausdorff distance measure. The method to rate segmentation results in relation to validation metrics is presented together with analysis of importance of different goodness measures. Results for one simulated dataset and three patient are presented.

IPFCM Clustering Algorithm Under Euclidean and Hausdorff Distance Measure for Symbolic Interval Data

2019 ◽  
Vol 21 (7) ◽  
pp. 2102-2119 ◽  
Author(s):  
Jin-Tsong Jeng ◽  
Chih-Ming Chen ◽  
Sheng-Chieh Chang ◽  
Chen-Chia Chuang
Keyword(s):  
Hausdorff Distance ◽  
Clustering Algorithm ◽  
Distance Measure ◽  
Interval Data ◽  
Symbolic Interval Data

scholarly journals Developing Image Processing Meta-Algorithms with Data Mining of Multiple Metrics

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Kelvin Leung ◽  
Alexandre Cunha ◽  
A. W. Toga ◽  
D. Stott Parker
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Distance Measure ◽  
Image Similarity ◽  
Processing Problem ◽  
Multiple Image ◽  
Novel Approach ◽  
Potential Benefits ◽  
Image Metric ◽  
Multiple Metrics

People often use multiple metrics in image processing, but here we take a novel approach of mining the values of batteries of metrics on image processing results. We present a case for extending image processing methods to incorporate automated mining of multiple image metric values. Here by a metric we mean any image similarity or distance measure, and in this paper we consider intensity-based and statistical image measures and focus on registration as an image processing problem. We show how it is possible to develop meta-algorithms that evaluate different image processing results with a number of different metrics and mine the results in an automated fashion so as to select the best results. We show that the mining of multiple metrics offers a variety of potential benefits for many image processing problems, including improved robustness and validation.

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