Relaxed image foresting transforms for interactive volume image segmentation

Malmberg, Filip; Nyström, Ingela; Mehnert, Andrew; Engstrom, Craig; Bengtsson, Ewert

doi:10.1117/12.840019

Cited by 16 publications

(33 citation statements)

References 14 publications

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“…In this section, we review the methods presented in [9] and [10]. Both these methods improve the segmentations obtained by the IFT by performing operations locally around the segmentation boundary.…”

Section: Applicationsmentioning

confidence: 99%

“…In [10], we address this weakness of the IFT by proposing the relaxed IFT (RIFT). This modified version of the IFT features an additional parameter that controls the smoothness of the segmentation boundary, thereby making the results more predictable in the presence of noise and weak edges.…”

Section: The Relaxed Iftmentioning

confidence: 99%

“…Here, we show that the set of boundary elements may be computed on the fly, at virtually no additional cost, as a by-product of the DIFT algorithm 1 . This makes it possible to implement the methods proposed in [9,10] efficiently in conjunction with the DIFT, thereby making them more attractive for interactive segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…In two recent publications [9,10], we show that the segmentations obtained by the IFT may be improved by performing local operations around the boundaries of the segmented regions. These methods are reviewed, and examples of their application are given, in Section 4.…”

Section: Introductionmentioning

confidence: 99%

“…This set is usually much smaller than the set of image elements. Since the methods proposed in [9] and [10] operate only in a small region around the boundary, they may be computed efficiently if the set of boundary elements is known.…”

Section: Introductionmentioning

confidence: 99%

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Image Foresting Transform: On-the-Fly Computation of Segmentation Boundaries

Malmberg

2011

Image Analysis

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Abstract. The Image Foresting Transform (IFT) is a framework for seeded image segmentation, based on the computation of minimal cost paths in a discrete representation of an image. In two recent publications, we have shown that the segmentations obtained by the IFT may be improved by refining the segmentation locally around the boundaries between segmented regions. Since these methods operate on a small subset of the image elements only, they may be implemented efficiently if the set of boundary elements is known. Here, we show that this set may be obtained on-the-fly, at virtually no additional cost, as a by-product of the IFT algorithm.

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Section: Applicationsmentioning

confidence: 99%

Section: The Relaxed Iftmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Image Foresting Transform: On-the-Fly Computation of Segmentation Boundaries

Malmberg

2011

Image Analysis

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An adaptive probabilistic atlas for anomalous brain segmentation in MR images

et al. 2019

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Purpose: Automated segmentation of brain structures (objects) in MR three-dimensional (3D) images for quantitative analysis has been a challenge and probabilistic atlases (PAs) are among the most well-succeeded approaches. However, the existing models do not adapt to possible object anomalies due to the presence of a disease or a surgical procedure. Post-processing operation does not solve the problem, for example, tissue classification to detect and remove such anomalies inside the resulting segmentation mask, because segmentation errors on healthy tissues cannot be fixed. Such anomalies very often alter the shape and texture of the brain structures, making them different from the appearance of the model. In this paper, we present an effective and efficient adaptive probabilistic atlas, named AdaPro, to circumvent the problem and evaluate it on a challenging taskthe segmentation of the left hemisphere, right hemisphere, and cerebellum, without pons and medulla, in 3D MR-T1 brain images of Epilepsy patients. This task is challenging due to temporal lobe resections, artifacts, and the absence of contrast in some parts between the structures of interest. Methods: In AdaPro, we first build one probabilistic atlas per object of interest from a training set with normal 3D images and the corresponding 3D object masks. Second, we incorporate a texture classifier based on convex optimization which dynamically indicates the regions of the target 3D image where the PAs (shape constraints) should be further adapted. This strategy is mathematically more elegant and avoids problems with post-processing. Third, we add a new object-based delineation algorithm based on combinatorial optimization and diffusion filtering. AdaPro can then be used to locate and delineate the objects in the coordinate space of the atlas or of the test image. We also compare AdaPro with three other state-of-the-art methods: an statistical shape model based on synergistic object search and delineation, and two methods based on multi-atlas label fusion (MALF). Results: We evaluate the methods quantitatively on 3D MR-T1 brain images of 2T and 3T from epilepsy patients, before and after temporal lobe resections, and on the template and native coordinate spaces. The results show that AdaPro is considerably faster and consistently more accurate than the baselines with statistical significance in both coordinate spaces. Conclusion: AdaPro can be used as a fast and effective step for brain tissue segmentation and it can also be easily extended to segment subcortical brain structures. By choice of its components, probabilistic atlas, texture classifier, and delineation algorithm, it can also be extended to other organs and imaging modalities.

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