Mumford-Shah Regularizer with Contextual Feedback

Erdem, Erkut

doi:10.1007/s10851-008-0109-y

Cited by 20 publications

(19 citation statements)

References 83 publications

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“…Kokkinos et al [14] investigated the use of adaptive weights for the task of separating edge areas from textured regions using a probabilistic framework, where the posterior probabilities of edge, texture, and smoothness cues were used as weights for curve evolution. Similarly, Malik et al [15] and, very recently, Erdem and Tari [16] tackled the problem of texture separation and selected weights based on data cues. However, while these methods focused on curve evolution frameworks, our current work focuses on graph-based segmentation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Contextual Energy Parameterization for Automated Image Segmentation

Rao

Hamarneh

Abugharbieh

2009

Advances in Visual Computing

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Abstract. Image segmentation techniques are predominately based on parameter-laden optimization processes. The segmentation objective function traditionally involves parameters (i.e. weights) that need to be tuned in order to balance the underlying competing cost terms of image data fidelity and contour regularization. In this paper, we propose a novel approach for automatic adaptive energy parameterization. In particular, our contributions are three-fold; 1) We spatially adapt fidelity and regularization weights to local image content in an autonomous manner. 2) We modulate the weight using a novel contextual measure of image quality based on the concept of spectral flatness. 3) We incorporate our proposed parameterization into a general segmentation framework and demonstrate its superiority to two alternative approaches: the best possible spatially-fixed parameterization and the globally optimal spatially-varying, but noncontextual, parameters. Our segmentation results are evaluated on real and synthetic data and produce a reduction in mean segmentation error when compared to alternative approaches.

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Section: Introductionmentioning

confidence: 99%

Adaptive Contextual Energy Parameterization for Automated Image Segmentation

Rao

Hamarneh

Abugharbieh

2009

Advances in Visual Computing

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“…Kokkinos et al [27], Iakovidis et al [20] and Erdem et al [28] utilize machine learning algorithms. Nevertheless, these approaches still require technical skills from the end user.…”

Section: Discussionmentioning

confidence: 99%

Automated parameterization of active contours: A brief survey

Mylona

Savelonas

Maroulis

2013

IEEE International Symposium on Signal Processing and Information Technology

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“…Although one weight value may be optimal for some regions in an image, it may not be optimal for all regions. Erdem and Tari [10] and Kokkinos et al [11] focus on edge consistency and texture cues by utilizing data-driven local cues. However, certain technical knowledge by the domain user is still required.…”

Section: Previous Workmentioning

confidence: 99%

Towards Self-Parameterized Active Contours for Medical Image Segmentation with Emphasis on Abdomen

Mylona

Savelonas

Maroulis

2013

Abdomen and Thoracic Imaging

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Medical doctors are typically required to segment medical images by means of computational tools, which suffer from parameters that are empirically selected through a cumbersome and time-consuming process. This chapter presents a framework for automated parameterization of region-based active contour regularization and data fidelity terms, which aims to relieve medical doctors from this process, as well as to enhance objectivity and reproducibility. Leaned on an observed isomorphism between the eigenvalues of structure tensors and active contour parameters, the presented framework automatically adjusts active contour parameters so as to reflect the orientation coherence in edge regions by means of the "orientation entropy." To this end, the active contour is repelled from randomly oriented edge regions and is navigated towards structured ones, accelerating contour convergence. Experiments are conducted on abdominal imaging domains, which include colon and lung images. The experimental evaluation demonstrates that the presented framework is capable of speeding up contour convergence, whereas it achieves high-quality segmentation results, albeit in an unsupervised fashion.

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Mumford-Shah Regularizer with Contextual Feedback

Cited by 20 publications

References 83 publications

Adaptive Contextual Energy Parameterization for Automated Image Segmentation

Adaptive Contextual Energy Parameterization for Automated Image Segmentation

Automated parameterization of active contours: A brief survey

Towards Self-Parameterized Active Contours for Medical Image Segmentation with Emphasis on Abdomen

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