Deep Vesselness Measure from Scale-Space Analysis of Hessian Matrix Eigenvalues

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Ricardo J. Araújo (Author) Other; Jaime S. Cardoso (Author) FEUP; Hélder P. Oliveira (Author) FCUP

Conference proceedings International

Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Pages: 473-484

9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019

1 July 2019 through 4 July 2019

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Other information

Authenticus ID: P-00R-2EF

DOI: 10.1007/978-3-030-31321-0_41

Abstract (EN): The enhancement of tubular structures such as vessels in medical images has been addressed in the past, aiming for easier extraction and or visualization of such structures by professionals. Some literature methodologies propose vesselness measures whose design is motivated by local properties of vascular networks and how these influence the eigenvalues of the Hessian matrix. However, past work fails to combine properly the scale-space and neighborhood information, thus leading to the proposal of suboptimal vesselness measures. In this paper, we show that a shallow convolutional neural network is able to learn more optimal embedding spaces from the eigenvalue analysis at different scales, thus leading to a stronger vessel enhancement. Additionally, we also show that such a system maintains one of the biggest advantages of Hessian-based vesselness measures, which is the robustness to data with varying statistics. © 2019, Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12