scholarly journals On the usage of average Hausdorff distance for segmentation performance assessment: hidden error when used for ranking

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Orhun Utku Aydin ◽  
Abdel Aziz Taha ◽  
Adam Hilbert ◽  
Ahmed A. Khalil ◽  
Ivana Galinovic ◽  
...  
Keyword(s):  
Performance Assessment ◽  
Hausdorff Distance ◽  
Rank Correlation ◽  
Ground Truth ◽  
Performance Measure ◽  
Medical Image Segmentation ◽  
Cerebral Vessel ◽  
Angiography Cerebral ◽  
Median Correlation ◽  
Kendall Rank Correlation

AbstractAverage Hausdorff distance is a widely used performance measure to calculate the distance between two point sets. In medical image segmentation, it is used to compare ground truth images with segmentations allowing their ranking. We identified, however, ranking errors of average Hausdorff distance making it less suitable for applications in segmentation performance assessment. To mitigate this error, we present a modified calculation of this performance measure that we have coined “balanced average Hausdorff distance”. To simulate segmentations for ranking, we manually created non-overlapping segmentation errors common in magnetic resonance angiography cerebral vessel segmentation as our use-case. Adding the created errors consecutively and randomly to the ground truth, we created sets of simulated segmentations with increasing number of errors. Each set of simulated segmentations was ranked using both performance measures. We calculated the Kendall rank correlation coefficient between the segmentation ranking and the number of errors in each simulated segmentation. The rankings produced by balanced average Hausdorff distance had a significantly higher median correlation (1.00) than those by average Hausdorff distance (0.89). In 200 total rankings, the former misranked 52 whilst the latter misranked 179 segmentations. Balanced average Hausdorff distance is more suitable for rankings and quality assessment of segmentations than average Hausdorff distance.

scholarly journals An evaluation of performance measures for arterial brain vessel segmentation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Orhun Utku Aydin ◽  
Abdel Aziz Taha ◽  
Adam Hilbert ◽  
Ahmed A. Khalil ◽  
Ivana Galinovic ◽  
...  
Keyword(s):  
Performance Measures ◽  
Hausdorff Distance ◽  
Ground Truth ◽  
Relevant Information ◽  
Vessel Segmentation ◽  
Performance Measure ◽  
Medical Image Segmentation ◽  
Cerebral Vessel ◽  
Angiography Cerebral ◽  
Brain Vessel

Abstract Background Arterial brain vessel segmentation allows utilising clinically relevant information contained within the cerebral vascular tree. Currently, however, no standardised performance measure is available to evaluate the quality of cerebral vessel segmentations. Thus, we developed a performance measure selection framework based on manual visual scoring of simulated segmentation variations to find the most suitable measure for cerebral vessel segmentation. Methods To simulate segmentation variations, we manually created non-overlapping segmentation errors common in magnetic resonance angiography cerebral vessel segmentation. In 10 patients, we generated a set of approximately 300 simulated segmentation variations for each ground truth image. Each segmentation was visually scored based on a predefined scoring system and segmentations were ranked based on 22 performance measures common in the literature. The correlation of visual scores with performance measure rankings was calculated using the Spearman correlation coefficient. Results The distance-based performance measures balanced average Hausdorff distance (rank = 1) and average Hausdorff distance (rank = 2) provided the segmentation rankings with the highest average correlation with manual rankings. They were followed by overlap-based measures such as Dice coefficient (rank = 7), a standard performance measure in medical image segmentation. Conclusions Average Hausdorff distance-based measures should be used as a standard performance measure in evaluating cerebral vessel segmentation quality. They can identify more relevant segmentation errors, especially in high-quality segmentations. Our findings have the potential to accelerate the validation and development of novel vessel segmentation approaches.

scholarly journals Leadership and Motivation as Important Aspects of the International Company’s Corporate Culture

2021 ◽  
Vol 13 (7) ◽  
pp. 3916
Author(s):  
Ingrida Košičiarová ◽  
Zdenka Kádeková ◽  
Peter Štarchoň
Keyword(s):  
Corporate Culture ◽  
Rank Correlation ◽  
Chi Square ◽  
Exact Test ◽  
Main Research ◽  
Leadership Motivation ◽  
Kendall Rank Correlation ◽  
Friedman's Test ◽  
Chi Square Test

Although the issue of corporate culture has been taken over and addressed in the literature from various perspectives, there are very few researchers about the role of leadership and motivation in it, respectively very few researchers have addressed them as important components of the international company’s corporate culture. The present paper aims to point out that leadership and motivation can be perceived as important aspects of the international company’s corporate culture. The object of the investigation was an international company (situated in Italy) and its five subsidiaries (situated in Italy, Czech Republic, Germany, and Turkey). As the main research method, there was chosen the method of the questionnaire survey, which was attempted by all the company’s employees (totally 270 respondents). The questionnaire was divided into three separate, but logically related parts—leadership, motivation, and corporate culture, and submitted to two groups of respondents—the company’s management and its employees. In total 11 hypotheses were formulated and further evaluated by the methods of Pearson Chi-square Test, Fisher’s Exact Test, Cramer’s V coefficient, Kendall rank correlation coefficient, Eta coefficient, Spearman coefficient, Mann–Whitney U test and Wilcoxon W statistics, Kruskal–Wallis test, and Friedman’s test. The results of the research have proven that leadership and motivation are important parts of the corporate culture.

scholarly journals Ultrasonological evaluation of acute scrotum

2018 ◽  
Vol 6 (1-2) ◽  
pp. 11-14
Author(s):  
Prakash Sharma ◽  
Merina Gyawali ◽  
Sristi Singh
Keyword(s):  
Statistical Analysis ◽  
Color Doppler ◽  
Descriptive Analysis ◽  
Power Doppler ◽  
Rank Correlation ◽  
Final Diagnosis ◽  
Acute Scrotum ◽  
Age Group ◽  
Scrotal Pain ◽  
Kendall Rank Correlation

Introduction: Acute scrotum is a common surgical condition. Ultrasound along with Doppler plays an important role in differentiating the various causes. Purpose of this study was to evaluate the role of ultrasound in identifying the various causes of acute scrotum.Methods: All patients with history of acute scrotum presenting to the Department of Radiology were included in the study. Scrotal ultrasound was performed with a linear 7.5 to 12-MHz transducer with abundant acoustic gel. Imaging was done in longitudinal and transverse planes with Power Doppler and Color Doppler. The testes, epididymis, spermatic cord, scrotal wall and inguinal region were evaluated. Kidney, Ureter and Bladder region was evaluated for possibility of calculus. Final diagnosis was made based on clinical outcome, follow up, intraoperative findings and histopathology when available. Statistical analysis was done using SPSS version 18 for windows. Descriptive analysis was done. Non parametric correlation between side and torsion was done using Kendall Rank Correlation Coefficient.Results: There were altogether 50 patients available for statistical analysis. Age of the patients ranged from 14 to 65 years with mean age of 34.7±14.7 years. Most common age group was 21 to 40 years. Inflammatory pathology was the most common pathology of actual scrotal pain. There was no significant correlation between side of pain and testicular torsion.Conclusion: Inflammatory pathology was the most common cause for actual scrotal pain. The most common age group was 21 to 40 years.

scholarly journals CRITICAL ASSESSMENT OF OBJECT SEGMENTATION IN AERIAL IMAGE USING GEO-HAUSDORFF DISTANCE

2016 ◽  
Vol XLI-B4 ◽  
pp. 187-194 ◽  
Author(s):  
H. Sun ◽  
Y. Ding ◽  
Y. Huang ◽  
G. Wang
Keyword(s):  
Hausdorff Distance ◽  
Object Segmentation ◽  
Signal To Noise Ratio ◽  
Limited Resource ◽  
Ground Truth ◽  
Aerial Images ◽  
Aerial Image ◽  
Spatial Distance ◽  
Valuable Insight ◽  
Spatial Correspondence

Aerial Image records the large-range earth objects with the ever-improving spatial and radiometric resolution. It becomes a powerful tool for earth observation, land-coverage survey, geographical census, etc., and helps delineating the boundary of different kinds of objects on the earth both manually and automatically. In light of the geo-spatial correspondence between the pixel locations of aerial image and the spatial coordinates of ground objects, there is an increasing need of super-pixel segmentation and high-accuracy positioning of objects in aerial image. Besides the commercial software package of eCognition and ENVI, many algorithms have also been developed in the literature to segment objects of aerial images. But how to evaluate the segmentation results remains a challenge, especially in the context of the geo-spatial correspondence. The Geo-Hausdorff Distance (GHD) is proposed to measure the geo-spatial distance between the results of various object segmentation that can be done with the manual ground truth or with the automatic algorithms.Based on the early-breaking and random-sampling design, the GHD calculates the geographical Hausdorff distance with nearly-linear complexity. Segmentation results of several state-of-the-art algorithms, including those of the commercial packages, are evaluated with a diverse set of aerial images. They have different signal-to-noise ratio around the object boundaries and are hard to trace correctly even for human operators. The GHD value is analyzed to comprehensively measure the suitability of different object segmentation methods for aerial images of different spatial resolution. By critically assessing the strengths and limitations of the existing algorithms, the paper provides valuable insight and guideline for extensive research in automating object detection and classification of aerial image in the nation-wide geographic census. It is also promising for the optimal design of operational specification of remote sensing interpretation under the constraints of limited resource.

Expanding the Capabilities of Radar-Based Vehicle Detection Systems: Noise Characterization and Removal Procedures

2019 ◽  
Vol 2673 (11) ◽  
pp. 150-160
Author(s):  
Kelvin R. Santiago-Chaparro ◽  
David A. Noyce
Keyword(s):  
Performance Measures ◽  
Vehicle Detection ◽  
Ground Truth ◽  
Noise Removal ◽  
Performance Measure ◽  
Signalized Intersections ◽  
Volume Data ◽  
Trajectory Data ◽  
Potential Accuracy ◽  
Detection Systems

The capabilities of radar-based vehicle detection (RVD) systems used at signalized intersections for stop bar and advanced detection are arguably underutilized. Underutilization happens because RVD systems can monitor the position and speed (i.e., trajectory) of multiple vehicles at the same time but these trajectories are only used to emulate the behavior of legacy detection systems such as inductive loop detectors. When full vehicle trajectories tracked by an RVD system are collected, detailed traffic operations and safety performance measures can be calculated for signalized intersections. Unfortunately, trajectory datasets obtained from RVD systems often contain significant noise which makes the computation of performance measures difficult. In this paper, a description of the type of trajectory datasets that can be obtained from RVD systems is presented along with a characterization of the noise expected in these datasets. Guidance on the noise removal procedures that can be applied to these datasets is also presented. This guidance can be applied to the use of data from commercially-available RVD systems to obtain advanced performance measures. To demonstrate the potential accuracy of the noise removal procedures, the procedures were applied to trajectory data obtained from an existing intersection, and data on a basic performance measure (vehicle volume) were extracted from the dataset. Volume data derived from the de-noised trajectory dataset was compared with ground truth volume and an absolute average difference of approximately one vehicle every 5 min was found, thus highlighting the potential accuracy of the noise removal procedures introduced.

Phantom-based ground-truth generation for cerebral vessel segmentation and pulsatile deformation analysis

2016 ◽  
Author(s):  
Daniel Schetelig ◽  
Dennis Säring ◽  
Till Illies ◽  
Jan Sedlacik ◽  
Fabian Kording ◽  
...  
Keyword(s):  
Ground Truth ◽  
Vessel Segmentation ◽  
Deformation Analysis ◽  
Cerebral Vessel ◽  
Ground Truth Generation

Benthic Invertebrate Fauna and Feeding Relationships of Brown Trout, Salmo trutta Linnaeus, and River Blackfish, Gadopsis marmoratus Richardson, in the Aberfeldy River, Victoria

1978 ◽  
Vol 29 (6) ◽  
pp. 725 ◽  
Author(s):  
PD Jackson
Keyword(s):  
Brown Trout ◽  
Salmo Trutta ◽  
Rank Correlation ◽  
Correlation Coefficients ◽  
Benthic Invertebrate ◽  
Invertebrate Fauna ◽  
Kendall Rank Correlation ◽  
Brown Trout Salmo Trutta

The benthic invertebrate fauna and the stomachs of brown trout and river blackfish in a section of the Aberfeldy River, Victoria, were sampled regularly from October 1971 to November 1972. The ranges of food organisms utilized by the two species were very similar as were the proportions of the different food categories. Kendall rank correlation coefficients indicated that the diets of the two fishes were similar on all but the July sampling date. Due to apparent differences in habitat prefer- ences this similarity in diets may be regarded as giving rise to indirect competition.

scholarly journals Reframing E-Government Development Indices with Respect to New Trends in ICT

2015 ◽  
Vol 15 (4) ◽  
pp. 383-412 ◽  
Author(s):  
Renáta Máchová ◽  
Martin Lněnička
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Rank Correlation ◽  
Important Indicator ◽  
The Public ◽  
Telecommunication Infrastructure ◽  
Kendall Rank Correlation ◽  
New Framework ◽  
Government Development

Abstract E-government readiness is an important indicator of the quality of a country’s technological and telecommunication infrastructure and the ability of its citizens, businesses and governments to adopt, use and benefit from modern technologies. To measure and compare selected countries, a lot of benchmarking and ranking indices have been introduced since the beginning of the century. With the increasing importance of trends such as cloud computing, open (big) data, participation tools or social media, new indicators and approaches need to be introduced in the measuring of the e-government development, and the existing indices should to be updated, redefined and restructured. Therefore, this article explores the structure of the existing e-government development indices to show the main indicators and trends. Then, it proposes and implements a new framework to evaluate e-government development using these new trends in ICT. It also examines and compares a basic background on the e-government development, benefits and risks of cloud computing, open (big) data and participation tools in the public sector. Based on the newly proposed framework, the e-government development index is calculated for each EU Member State to clearly identify the indicators to have an influence on the e-government development. In the last part, these results are compared to the already existing indices to validate the conformity of the rank methods using Kendall rank correlation coefficient.

Sign in / Sign up

Export Citation Format

Share Document