Elsevier

Pattern Recognition

Volume 47, Issue 10, October 2014, Pages 3193-3208
Pattern Recognition

A new method for linear feature and junction enhancement in 2D images based on morphological operation, oriented anisotropic Gaussian function and Hessian information

https://doi.org/10.1016/j.patcog.2014.04.024Get rights and content

Highlights

  • In this study, we propose a new method to enhance structures with linear features in images.

  • An adaptive multi-scale morpho-Gaussian filter is proposed to enhance as well as smooth the linear structures.

  • We solve the junction suppression problem that can be seen in many existing methods.

  • The approach is applied on several types of images.

  • We make comparisons with several existing methods and the results show that our method can achieve better results.

Abstract

Feature enhancement is an important preprocessing step in many image processing tasks. It is the process of adjusting image intensities so that the enhanced results are more suitable for analysis. Good enhancement results for linear structures such as vessels or neurites can be used as inputs for segmentation and other operations. In this paper, a novel linear feature enhancement filter – an adaptive multi-scale morpho-Gaussian filter – which can enhance and smooth linear features is proposed based on morphological operation, anisotropic Gaussian function and Hessian information. This filter can enhance and smooth along the local orientation of the linear structures and the Hessian measurement is used to further enhance the linear features. We utilize the Hessian matrix to calculate the orientation information for our directional morphological operation and the oriented anisotropic Gaussian smoothing. We also propose a novel method for junction enhancement, which can solve the problem of junction suppression. We decompose the junctions and enhance along each linear structure within a junction region. We present the test results of our algorithm on images of different types and compare our method with three existing methods. The experimental results show that the proposed approach can achieve better results.

Introduction

Many objects such as retinal vessels, neurites and plant roots have linear features. Information about these structures can be used in biomedical and other types of applications. By analyzing vessel structures, diseases that involve structural or functional changes can be investigated [1], [2]. For example, hypertension may result in focal constriction of retinal arteries and arteriosclerosis can cause the arteries to show a copper or silver color [3], [4]. Moreover, the number and morphology of neuron cells can be used to predict or diagnose brain diseases such as Alzheimer׳s disease, which is characterized by the loss of neurons and synapses in the cerebral cortex and certain subcortical regions [5]. Therefore, the detection, segmentation and tracking for linear structures in images can release the biologists from heavy burden of manual work and improve efficiency.

However, the images that are to be segmented are not always of high quality due to distortions during acquisition, processing, compression, storage, transmission and reproduction, which will affect the result of segmentation or tracking [6]. The enhancement of linear features can make the image processing steps easier and greatly improve the result of feature segmentation. In medical image analysis, for example, enhancement of vessels can improve the visualization of vessels and small vessel delineation. It can also provide the input for vessel segmentation, centerline extraction and tracking.

In this paper, novel algorithms are proposed to enhance linear features as well as enhance junction regions without any suppression. Firstly, we propose an adaptive multi-scale morpho-Gaussian filter to enhance, reconnect and smooth linear features. From the Hessian matrix, the orientation of the features and a measurement based on the eigenvalues are obtained. At each pixel along its orientation, we carry out a linear morphological operation so that linear structures are enhanced and noisy structures are suppressed. The intensity profile of the linear structure׳s cross section can be approximated by a Gaussian curve [7], [8]. Multi-scale locally oriented anisotropic Gaussian templates are applied in each morphologically enhanced window to smooth the linear structures in our work. To match linear structures with various widths, the Gaussian function is used in multiple scales. The enhanced and reconnected features using the morphological operation are smoothed by using this multi-scale-anisotropic Gaussian function. Then a multi-scale Hessian-based filter is used to further enhance the linear structures. The enhancement results of existing methods in junction regions are not satisfactory because junctions are the intersection of several linear structures and have multiple orientations. To solve this issue, a novel method for junction enhancement is proposed. We first decompose the junctions into separate linear structures or branches, and then we enhance the linear features along each branch. In Section 2, we give a brief review of some of the existing approaches. Section 3 gives a brief overview about our algorithm. In Section 4, the linear feature enhancement approach is described. Our junction enhancement method is described in Section 5. We give experimental results and statistical analysis in Section 6. The paper is concluded in Section 7.

Section snippets

Related work

Due to the importance of linear feature enhancement, a variety of enhancement methods have been developed recently. Hessian based approach is one of the most popular methods, because the second order structure in the Hessian matrix describes the intensity variations for each point [9]. In Sato et al. and Lorenz et al.׳s work, eigenvalues of the Hessian matrix were analyzed to determine the local likelihood of vesselness [10], [11]. They constructed a line-structure-ness measure using the

Overview of the proposed method

We would like to give an overview of the proposed method. The algorithm steps for the whole procedure are:

  • 1.

    Calculate the orientation and the intensity variation for each pixel based on Hessian information.

  • 2.

    Correct the orientation and fill in the dark shadows next to the linear structures.

  • 3.

    Apply the adaptive multi-scale morpho-Gaussian filter:

    • (a)

      In a rectangular window, enhance each linear feature and suppress noisy features.

    • (b)

      Use the multi-scale anisotropic Gaussian kernel to smooth linear features.

  • 4.

Hessian measurements and preprocessing

Hessian matrix involves the second order gradient of images. Analysis of the eigenvalues of a Hessian matrix can provide a measurement for the principal direction and the vesselness of a linear structure. Assuming the two eigenvalues of the Hessian matrix of a 2D image are λ1 and λ2, and they satisfy |λ1|<|λ2| (assuming bright structures). The eigenvalues represent the variation of the intensity in a linear structure׳s principal orientation and its perpendicular orientation. So for a linear

Junction enhancement

Junctions are the regions where three or more linear structures meet. We call these linear structures at junction regions ‘branches’. Hessian-based methods such as Frangi et al.׳s method [12] have a problem of junction suppression [17]. In the proposed method, we do not have the junction suppression problem in the morpho-Gaussian filter step as our algorithm only uses the orientation information and the intensity variation from the Hessian matrix. Besides, enhancement of linear features in the

Experimental results and comparisons

We evaluate the performance of the proposed method through applications to 49 images which include synthetic images, neurite images, leaf vein images and retinal images. We compare our results with four other methods: Tankyevych et al.׳s method [24], Kroon et al.׳s method [22], Frangi et al.׳s method [12] and Shikata et al.׳s method [15]. The reason we choose these four methods is that Frangi et al.׳s method and Shikata et al.׳s method are two classic Hessian methods for linear feature

Conclusions

In this paper, we propose a new method to enhance linear features and junctions in images. We enhance the linear features and junctions in two steps. In the linear structure enhancement, a multi-scale morpho-Gaussian filter is proposed to enhance the linear features. This method uses the enhancement function of the morphological operation and the smoothing function of the Gaussian filter. The use of multiple scales approach ensures that the linear structures with various widths can all be

Conflict of Interest

None declared.

Acknowledgments

We would like to acknowledge and extend our gratitude to Xiao Tan at the University of New South Wales, Canberra, Australia, for his comments on this paper. We thank the anonymous reviewers for their helpful and constructive comments on this paper. The preliminary conference version of this paper was presented in [45].

Ran Su obtained her PhD degree from the University of New South Wales, and spent her research co-training at CSIRO. Her main research interest is image analysis, pattern recognition and machine learning.

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    Ran Su obtained her PhD degree from the University of New South Wales, and spent her research co-training at CSIRO. Her main research interest is image analysis, pattern recognition and machine learning.

    Changming Sun received the PhD degree in the area of computer vision from Imperial College London in 1992. Then, he joined CSIRO Computational Informatics, Australia, where he is currently a principal research scientist carrying out research and working on applied projects. His research interests include computer vision and photogrammetry, image analysis, and pattern recognition. He has served on the program/organizing committees of various international conferences. He is an Associate Editor for EURASIP Journal on Image and Video Processing, a SpringerOne journal. Dr. Sun is a member of the Australian Pattern Recognition Society.

    Chao Zhang obtained his PhD degree from the University of New South Wales, and spent his research co-training at CSIRO. His main research interest is image analysis and pattern recognition.

    Tuan D. Phamreceived his PhD in 1995 from the University of New South Wales (Sydney, Australia). His current research interests focus on image analysis and pattern recognition in medicine and biology. His research has been funded by government agencies, academic institutions, and industry. Dr. Pham is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), editorial board member and associate editor of several journals and book series. He has served as chair and technical member of a number of international conferences, and the founder of the International Symposium on Computational Models for Life Sciences (CMLS) and The International Aizu Conference on Biomedical Informatics and Technology (ACBIT).

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