A survey on curvilinear object segmentation in multiple applications

Bibiloni, Pedro; González-Hidalgo, Manuel; Massanet, Sebastià

doi:10.1016/j.patcog.2016.07.023

Cited by 41 publications

(17 citation statements)

References 110 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…State-of-the-art approaches for the delineation of curvilinear patterns in images were recently surveyed in [5]. Existing methods are based on different techniques ranging from parametric to point processes and machine learning approaches, as well as region growing, filtering and mathematical morphology.…”

Section: Introductionmentioning

confidence: 99%

Robust Inhibition-Augmented Operator for Delineation of Curvilinear Structures

Strisciuglio

Azzopardi

Petkov

2019

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Delineation of curvilinear structures in images is an important basic step of several image processing applications, such as segmentation of roads or rivers in aerial images, vessels or staining membranes in medical images, and cracks in pavements and roads, among others. Existing methods suffer from insufficient robustness to noise. In this paper, we propose a novel operator for the detection of curvilinear structures in images, which we demonstrate to be robust to various types of noise and effective in several applications. We call it RUSTICO, which stands for RobUST Inhibition-augmented Curvilinear Operator. It is inspired by the push-pull inhibition in visual cortex and takes as input the responses of two trainable B-COSFIRE filters of opposite polarity. The output of RUSTICO consists of a magnitude map and an orientation map. We carried out experiments on a data set of synthetic stimuli with noise drawn from different distributions, as well as on several benchmark data sets of retinal fundus images, crack pavements, and aerial images and a new data set of rose bushes used for automatic gardening. We evaluated the performance of RUSTICO by a metric that considers the structural properties of line networks (connectivity, area, and length) and demonstrated that RUSTICO outperforms many existing methods with high statistical significance. RUSTICO exhibits high robustness to noise and texture.

show abstract

Section: Introductionmentioning

confidence: 99%

Robust Inhibition-Augmented Operator for Delineation of Curvilinear Structures

Strisciuglio

Azzopardi

Petkov

2019

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…Over the last decade, a number of automatic methods have been proposed to segment vessels from volumetric medical images. These procedures can be categorized into rule‐based methods and machine‐learning‐based methods 2–4 . The rule‐based methods mainly include the Hessian matrix, 5–8 mathematical morphology, 9–11 and minimal cost path, 12–15 etc.…”

Section: Introductionmentioning

confidence: 99%

Vessel segmentation from volumetric images: a multi‐scale double‐pathway network with class‐balanced loss at the voxel level

et al. 2021

View full text Add to dashboard Cite

Vessel segmentation from volumetric medical images is becoming an essential pre-step in aiding the diagnosis, guiding the therapy, and patient management for vascular-related diseases. Deep learning-based methods have drawn many attentions, but most of them did not fully utilize the multiscale spatial information of vessels. To address this shortcoming, we propose a multi-scale network similar to the well-known multi-scale DeepMedic. It also includes a double-pathway architecture and a class-balanced loss at the voxel level (MDNet-Vb) to achieve both the computation efficiency and segmentation accuracy. Methods: The proposed network consists two parallel pathways to learn the multi-scale vessel morphology. Specifically, the pathway with a normal resolution uses three-dimensional (3D) U-Net fed with small inputs to learn the local details with relatively small storage and time consumption. The pathway with a low-resolution employs 3D fully convolutional network (FCN) fed with downsampled large inputs to learn the overall spatial relationships between vessels and adjacent tissues, and the morphological information of large vessels. To cope with the class-imbalanced issue in vessel segmentation, we propose a class-balanced loss at the voxel level with uniform sampling strategy. The class-balanced loss at the voxel level re-balances the loss function with a coefficient that is inversely proportional to the normalized effective number at the voxel level of each class. The uniform sampling strategy extracts training data by sampling uniformly from two classes in every epoch. Results: Our MDNet-Vb outperforms several state-of-the-art methods including ResNet, DenseNet, 3D U-Net, V-Net, and DeepMedic with the highest dice coefficients of 72.91% and 69.32% on cardiac computed tomography angiography (CTA) dataset and cerebral magnetic resonance angiography (MRA) dataset, respectively. Among four different double-pathway networks, our network (3D U-Net+3D FCN) not only has the fewest training parameters and shortest training time, but also gets competitive dice coefficients on both the CTA and MRA datasets. Compared with classical losses, our class-balanced focal loss (FL-Vb) and dice coefficient loss at the voxel level (Dsc-Vb) alleviates class imbalanced issue by improving both the sensitivity and dice coefficient on the CTA and MRA datasets. Moreover, simultaneously training on two datasets shows that our method has the highest dice coefficient of 73.06% and 65.40% on CTA and MRA datasets, respectively, outperforming the commonly used methods, such as U-Net and DeepMedic, which demonstrates the generalization potential of our network for segmenting different blood vessels. Conclusions: Our MDNet-Vb method demonstrates its superiority over other state-of-the-art methods, on both cardiac CTA and cerebral MRA datasets. For the network architecture, the MDNet-Vb combined the 3D U-Net and 3D FCN, which dramatically reduces the network parameters yet maintains the segmentation accuracy. The class-balanced loss at the voxel lev...

show abstract

“…The detection and examination of retinal vessel tree plays an essential role in clinical applications for the diagnosis of many eye diseases such as diabetic retinopathy [1,2]. The manual annotation of vessel network by graders is a laborious and time-consuming process due to the complexity of vessel structure.…”

Section: Introductionmentioning

confidence: 99%

A Local Flow Phase Stretch Transform for Robust Retinal Vessel Detection

Challoob

Gao

2020

Advanced Concepts for Intelligent Vision Systems

View full text Add to dashboard Cite

This paper presents a new method for reliably detecting retinal vessel tree using a local flow phase stretch transform (LF-PST). A local flow evaluator is proposed to increase the local contrast and the coherence of the local orientation of vessel tree. This is achieved by incorporating information about the local structure and direction of vessels, which is estimated by introducing a second curvature moment evaluation matrix (SCMEM). The SCMEM evaluates vessel patterns as only features having linearly coherent curvature. We present an oriented phase stretch transform to capture retinal vessels running at various diameters and directions. The proposed method exploits the phase angle of the transform, which includes structural features of lines and curved patterns. The LF-PST produces several phase maps, in which the vessel structure is characterized along various directions. To produce an orientation invariant response, all phases are linearly combined. The proposed method is tested on the publicly available DRIVE and IOSTAR databases with different imaging modalities and achieves encouraging segmentation results outperforming the state-of-the-art benchmark methods.

show abstract

A survey on curvilinear object segmentation in multiple applications

Cited by 41 publications

References 110 publications

Robust Inhibition-Augmented Operator for Delineation of Curvilinear Structures

Robust Inhibition-Augmented Operator for Delineation of Curvilinear Structures

Vessel segmentation from volumetric images: a multi‐scale double‐pathway network with class‐balanced loss at the voxel level

A Local Flow Phase Stretch Transform for Robust Retinal Vessel Detection

Contact Info

Product

Resources

About