A Supervoxel-Based Solution to Resume Segmentation for Interactive Correction by Differential Image-Foresting Transforms

Tavares, Anderson Carlos Moreira; Miranda, Paulo A. V.; Spina, Thiago Vallin; Falcão, Alexandre X.

doi:10.1007/978-3-319-57240-6_9

Cited by 8 publications

(7 citation statements)

References 29 publications

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“…ISF falls in the category of graph-based algorithms as a particular case of a more general framework [11] -the Image Foresting Transform (IFT). The IFT is a framework for the design of image operators based on connectivity, such as distance and geodesic transforms, morphological reconstructions, multiscale skeletonization, image description, regionand boundary-based image segmentation methods [26], [24], [27], [28], [29], [30], [31], [32], [33], with extensions to clustering and classification [34], [35], [36], [37], [38]. As discussed in [39], by choice of the connectivity function, the IFT algorithm computes a watershed transform from a set of seeds that corresponds to a graph cut in which the minimum gradient value in the cut is maximized.…”

Section: Related Workmentioning

confidence: 99%

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An Iterative Spanning Forest Framework for Superpixel Segmentation

Muñoz

Chowdhury

Alexandre

et al. 2019

IEEE Trans. on Image Process.

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Superpixel segmentation has become an important research problem in image processing. In this paper, we propose an Iterative Spanning Forest (ISF) framework, based on sequences of Image Foresting Transforms, where one can choose i) a seed sampling strategy, ii) a connectivity function, iii) an adjacency relation, and iv) a seed pixel recomputation procedure to generate improved sets of connected superpixels (supervoxels in 3D) per iteration. The superpixels in ISF structurally correspond to spanning trees rooted at those seeds. We present five ISF methods to illustrate different choices of its components. These methods are compared with approaches from the state-of-the-art in effectiveness and efficiency. The experiments involve 2D and 3D datasets with distinct characteristics, and a high level application, named sky image segmentation. The theoretical properties of ISF are demonstrated in the supplementary material and the results show that some of its methods are competitive with or superior to the best baselines in effectiveness and efficiency.

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Section: Related Workmentioning

confidence: 99%

“…A first method based on the ISF framework appeared in [45] and has been successfully used in a high level application [33]. It is considered in our experiments.…”

Section: Related Workmentioning

confidence: 99%

An Iterative Spanning Forest Framework for Superpixel Segmentation

Muñoz

Chowdhury

Alexandre

et al. 2019

IEEE Trans. on Image Process.

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“…To correct an existing segmentation, the seed set is initialized with the geodesic centers of the pre-segmented labels (cell candidates) for IFT-SC ( Figure 4H). More sophisticated procedures may also be considered for non-cell objects [15]. Since the user may desire to extend, merge, or split existing cells, the system provides for both interaction modes in total eight operations that can be used when editing scribbles: add, remove, extend, split, merge, undo, redo, and erase all.…”

Section: User Interactionmentioning

confidence: 99%

SEGMENT3D: A web-based application for collaborative segmentation of 3D images used in the shoot apical meristem

Spina

Stegmaier

Falcão

et al. 2018

2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

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The quantitative analysis of 3D confocal microscopy images of the shoot apical meristem helps understanding the growth process of some plants. Cell segmentation in these images is crucial for computational plant analysis and many automated methods have been proposed. However, variations in signal intensity across the image mitigate the effectiveness of those approaches with no easy way for user correction. We propose a web-based collaborative 3D image segmentation application, SEGMENT3D, to leverage automatic segmentation results. The image is divided into 3D tiles that can be either segmented interactively from scratch or corrected from a pre-existing segmentation. Individual segmentation results per tile are then automatically merged via consensus analysis and then stitched to complete the segmentation for the entire image stack. SEGMENT3D is a comprehensive application that can be applied to other 3D imaging modalities and general objects. It also provides an easy way to create supervised data to advance segmentation using machine learning models.

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“…• IFT-SLIC [34,35]: Alexandre et al [34] developed a supervoxel generation method which initializes a seed set equally spaced like a grid, and moves them appropriately according to intensity and shape constraints, by regulating supervoxels compactness and boundary adherence. The natural returning of seeds allows the user to edit the presegmentation by IFT;…”

Section: Motivationmentioning

confidence: 99%

“…Features such as compactness (which gives a more regular shape) and boundary adherence are benefits of using this method. The format of partitions, compared to other methods, is more regular [35];…”

Section: Goals and Contributionsmentioning

confidence: 99%

Interactive 3D segmentation repair with image-foresting transform, supervoxels and seed robustness

Tavares¹

Self Cite

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Image segmentation consists on its partition into relevant regions, such as to isolate the pixels belonging to desired objects in the image domain, which is an important step for computer vision, medical image processing, and other applications. Many times automatic segmentation generates results with imperfections. The user can correct them by editing manually, interactively or can simply discard the segmentation and try to automatically generate another result by a different method. Interactive methods combine benefits from manual and automatic ones, reducing user effort and using its high-level knowledge. In seed-based methods, to continue or repair a prior segmentation (presegmentation), avoiding the user to start from scratch, it is necessary to solve the Reverse Interactive Segmentation Problem (RISP), that is, how to automatically estimate the seeds that would generate it. In order to achieve this goal, we first divide the segmented object into its composing cores. Inside a core, two seeds separately always produce the same result, making one redundant. With this, only one seed per core is required. Cores leading to segmentations which are contained in the result of other cores are redundant and can also be discarded, further reducing the seed set, a process called Redundancy Analysis. A minimal set of seeds for presegmentation is generated and the problem of interactive repair can be solved by adding new seeds or removing seeds. Within the framework of the Image-Foresting Transform (IFT), new methods such as Oriented Image-Foresting Transform (OIFT) and Oriented Relative Fuzzy Connectedness (ORFC) were developed. However, there were no known algorithms for computing the core of these methods. This work develops such algorithms, with proof of correctness. The cores also give an indication of the degree of robustness of the methods on the positioning of the seeds. Therefore, a hybrid method that combines GraphCut and the ORFC cores, as well as the Robustness Coefficient (RC), have been developed. In this work, we present another developed solution to repair segmentations, which is based on IFT-SLIC, originally used to generate supervoxels. Experimental results analyze, compare and demonstrate the potential of these solutions.

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A Supervoxel-Based Solution to Resume Segmentation for Interactive Correction by Differential Image-Foresting Transforms

Cited by 8 publications

References 29 publications

An Iterative Spanning Forest Framework for Superpixel Segmentation

An Iterative Spanning Forest Framework for Superpixel Segmentation

SEGMENT3D: A web-based application for collaborative segmentation of 3D images used in the shoot apical meristem

Interactive 3D segmentation repair with image-foresting transform, supervoxels and seed robustness

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