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Improved Hessian multiscale enhancement filter

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 3rd International Conference on Biomedical Engineering and Biotechnology, 25–28 September 2014, Beijing, China

Article type: Research Article

Authors: Yang, Jinzhu; ; | Ma, Shuang; | Sun, Qi | Tan, Wenjun; | Xu, Mengjia; | Chen, Nan | Zhao, Dazhe;

Affiliations: Key Laboratory of Medical Image Computing of Northeastern University, Ministry of Education, Shenyang 110819, China | College of Information Science and Engineering, Northeastern University, Shenyang 110004, China | Neusoft, Shenyang 110819, China | Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China

Note: [] Corresponding author: Jinzhu Yang, College of Information Science and Engineering, Northeastern University, Shenyang 110004, China. Tel.: +86 024-83665418; Fax: 83663446; E-mail: [email protected].

Keywords: Hessian filter, multiscale enhancement, grayscale, CT image, MRA image

DOI: 10.3233/BME-141149

Journal: Bio-Medical Materials and Engineering, vol. 24, no. 6, pp. 3267-3275, 2014

Published: 2014
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Abstract

Traditional Hessian multiscale filter consider only the local geometric feature but not the global grayscale information. In medical image analysis, Hessian filter is usually used to enhance the blood vessels. However, it also produces some pseudo vascular structures or some isolate noise points, such as the nasal soft tissues that have the similar shape with the vessels in MRA data, which will increase the difficulty of cerebrovascular segmentation. To resolve this issue, an improved Hessian multiscale filter is proposed in this paper. An image grayscale factor is added to the vascular similarity function computed by Hessian matrix eigenvalue. This method is experimented on brain MRA data and lung CTA data. Experimental results show that this method can enhance vascular structures, and simultaneously reduce the appearance of the pseudo vascular structures and the isolated noise points.

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