Towards multi-stage texture-based active contour image segmentation

Reska, Daniel; Boldak, Cezary; Krȩtowski, Marek

doi:10.1007/s11760-016-1026-y

Cited by 4 publications

(5 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The presented method employs a multi-stage approach that was firstly outlined in [42]. The original work used a snake-based ACM that employed a vastly different evolution scheme that considered only the closest neighbourhood of the curve discrete points.…”

Section: Discussionmentioning

confidence: 99%

“…The method starts with a texture feature extraction and selection stage, similar to the method described in [42]. The stage results in a set of texture feature maps M best , where a single feature map m is a scalar image that contains the feature values for each pixel of the original image.…”

Section: Texture Feature Extraction and Selectionmentioning

confidence: 99%

“…Automatic increase of the window size (from 3×3, 5×5 and 7×7 upward) can be performed in the case of larger patterns and redundant channels for different angles can be omitted or merged in case of isotropic texture patterns. Please refer to [42] for more details on these operations. Moreover, a bank of 24 Gabor filters with four orientations (0 • , 45 • , 90 • and 135 • ), three wavelengths and two Gaussian envelope scales is considered.…”

Section: Texture Feature Extraction and Selectionmentioning

confidence: 99%

“…The concept of a multi-stage approach was initially outlined in [42] in the context of classical discrete parametric contour (snake). Apart from the similarities in the feature selection stage, the snake-based model employed a vastly different evolution scheme that considered only the closest neighbourhood of the contour points.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

GPU-accelerated image segmentation based on level sets and multiple texture features

Reska

Krȩtowski

2020

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

In this paper, we present a fast multi-stage image segmentation method that incorporates texture analysis into a level set-based active contour framework. This approach allows integrating multiple feature extraction methods and is not tied to any specific texture descriptors. Prior knowledge of the image patterns is also not required. The method starts with an initial feature extraction and selection, then performs a fast level set-based evolution process and ends with a final refinement stage that integrates a region-based model. The presented implementation employs a set of features based on Grey Level Co-occurrence Matrices, Gabor filters and structure tensors. The high performance of feature extraction and contour evolution stages is achieved with GPU acceleration. The method is validated on synthetic and natural images and confronted with results of the most similar among the accessible algorithms.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Texture Feature Extraction and Selectionmentioning

confidence: 99%

Section: Texture Feature Extraction and Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

GPU-accelerated image segmentation based on level sets and multiple texture features

Reska

Krȩtowski

2020

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

show abstract

“…An active contour detects specified properties of an image and dynamically fits to the edges of a structure by minimizing an energy function. Although successful applications have been reported for the segmentation of objects [16][17][18][19][20][21], the active contour approach has several difficulties. First, the method depends on the initial position of the snake, which needs to be carefully initialized to be close to the region of interest to avoid being distracted by noise.…”

Section: Introductionmentioning

confidence: 99%

Interior point search for nonparametric image segmentation

Onal

Chen

Balasooriya

2017

SIViP

View full text Add to dashboard Cite

Precise object boundary detection for automatic image segmentation is critical for image analysis, including that used in computer-aided diagnosis. However, such detection traditionally uses active contour or snake models requiring accurate initialization and parameter optimization. Identifying optimal parameter values requires time-consuming multiple runs and provides results that vary by user expertise, limiting the use of these models in high-throughput or realtime situations. Thus, we developed a nonparametric snake model using an interior point search method applied in iterations to find and improve the set of snake points forming the edge of a shape. At each iteration, one or more snake points are replaced by others in the edge map. We validated the model using binary and continuous edge images of single and multiple objects, and noisy and real images, comparing the results to those obtained using traditional snake models. The proposed model not only provides better results on all image types tested but is more robust than traditional snake models. Unlike traditional snake models, the proposed model requires no user interaction for initializing snakes and no preprocessing of noisy images. Thus, our method offers robust automatic image segmentation that is simpler to use and less time-consuming than traditional snake models.

show abstract

A Simplified Active Contour Model with Free Endpoints

Shen

2018

J Sign Process Syst

View full text Add to dashboard Cite

Towards multi-stage texture-based active contour image segmentation

Cited by 4 publications

References 31 publications

GPU-accelerated image segmentation based on level sets and multiple texture features

GPU-accelerated image segmentation based on level sets and multiple texture features

Interior point search for nonparametric image segmentation

A Simplified Active Contour Model with Free Endpoints

Contact Info

Product

Resources

About