Exploiting reliability-guided aggregation for the assessment of curvilinear structure tortuosity

Su，Pan1,2; Zhao，Yitian1; Chen，Tianhua3; Xie，Jianyang1; Zhao，Yifan1,4; Qi，Hong5; Zheng，Yalin1,6; Liu，Jiang1,7

10.1007/978-3-030-32251-9_2

2019

0302-9743

1611-3349

LECT NOTES ARTIF INT

11767 LNCS

12-20

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

The study on tortuosity of curvilinear structures in medical images has been significant in support of the examination and diagnosis for a number of diseases. To avoid the bias that may arise from using one particular tortuosity measurement, the simultaneous use of multiple measurements may offer a promising approach to produce a more robust overall assessment. As such, this paper proposes a data-driven approach for the automated grading of curvilinear structures’ tortuosity, where multiple morphological measurements are aggregated on the basis of reliability to form a robust overall assessment. The proposed pipeline starts dealing with the imprecision and uncertainty inherently embedded in empirical tortuosity grades, whereby a fuzzy clustering method is applied on each available measurement. The reliability of each measurement is then assessed following a nearest neighbour guided approach before the final aggregation is made. Experimental results on two corneal nerve and one retinal vessel data sets demonstrate the superior performance of the proposed method over those where measurements are used independently or aggregated using conventional averaging operators.

Curvilinear structure Fuzzy clustering Reliability guided aggregation Tortuosity assessment

其他

英语

CPCI-S ; EI

China Postdoctoral Science Foundation[2019M652156]

Computer Science ; Engineering ; Neurosciences & Neurology ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging

Computer Science, Artificial Intelligence ; Computer Science, Software Engineering ; Engineering, Biomedical ; Neuroimaging ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging

WOS:000548735900002

20194807770145

Grading ; Medical imaging ; Diagnosis ; Fuzzy clustering ; Uncertainty analysis

Biomedical Engineering:461.1 ; Medicine and Pharmacology:461.6 ; Computer Software, Data Handling and Applications:723 ; Imaging Techniques:746 ; Probability Theory:922.1

2-s2.0-85075698027

Scopus

1.Cixi Institute of Biomedical Engineering,Ningbo Institute of Industrial Technology,Chinese Academy of Sciences,Ningbo,China
2.School of Control and Computer Engineering,North China Electric Power University,Baoding,China
3.School of Computing and Engineering,University of Huddersfield,Huddersfield,United Kingdom
4.School of Aerospace,Transport and Manufacturing,Cranfield University,Cranfield,United Kingdom
5.Department of Ophthalmology,Peking University Third Hospital,Beijing,China
6.Department of Eye and Visual Science,University of Liverpool,Liverpool,United Kingdom
7.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China

Su，Pan,Zhao，Yitian,Chen，Tianhua,et al. Exploiting reliability-guided aggregation for the assessment of curvilinear structure tortuosity[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2019:12-20.