An Iterative Spanning Forest Framework for Superpixel Segmentation

Muñoz, John Ever; Chowdhury, Ananda S.; Alexandre, Eduardo Barreto; Galvão, Felipe Lemes; Miranda, Paulo A. V.; Falcão, Alexandre X.

doi:10.1109/tip.2019.2897941

Cited by 72 publications

(86 citation statements)

References 53 publications

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“…In order to generate consistent and compact superpixels, the simple linear iterative clustering (SLIC) algorithm [30] is used here which segregates an image into groups with approximately similar characteristics. The major advantage of SLIC method is that it creates groups and there is a very low possibility of grouping pixels of dissimilar characteristics [31]. Moreover, it involves a very low computational cost and a tradeoff is possible between accuracy and classification time.…”

Section: B Superpixel Based Feature Extraction 1) Superpixel Segmentmentioning

confidence: 99%

Automated Brain Tumor Segmentation Based on Multi-Planar Superpixel Level Features Extracted From 3D MR Images

et al. 2020

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Brain tumor segmentation from Magnetic Resonance Imaging (MRI) is of great importance for better tumor diagnosis, growth rate prediction and radiotherapy planning. But this task is extremely challenging due to intrinsically heterogeneous tumor appearance, the presence of severe partial volume effect and ambiguous tumor boundaries. In this work, a unique approach of tumor segmentation is introduced based on superpixel level features extracted from all three planes (x-y, y-z, and z-x) of 3D volumetric MR images. In order to avoid the pixel randomness and to account for precise inhomogeneous boundaries of brain tumor, each of the images belonging to a particular plane is partitioned into irregular patches (superpixels) based on their intensity and spatial similarity. Next, various statistical and textural features are extracted from each superpixel where all three planes are considered separately in order to obtain better labeling on superpixels in tumor edges. A feature selection scheme is proposed based on their performance on histogram based consistency analysis and local descriptor pattern analysis, which offers a significant reduction in feature dimension without sacrificing classification performance. For the purpose of supervised classification, Extremely Randomized Trees is used to classify these superpixels into a tumor or a non-tumor class. Finally, pixel level decision is taken based on corresponding decisions obtained in each plane. Extensive simulations are carried out on publicly available dataset and it is found that the proposed method offers better tumor segmentation performance in comparison to that obtained by some state of the art methods.

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Section: B Superpixel Based Feature Extraction 1) Superpixel Segmentmentioning

confidence: 99%

Automated Brain Tumor Segmentation Based on Multi-Planar Superpixel Level Features Extracted From 3D MR Images

et al. 2020

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“…Although this method could more directly obtain superpixel borders to fit with the object boundary, it limits the pairwise link for vertical and horizon junctions to preserve the superpixel regularity, which results in a lower object boundary adherence rate. Hence, Munoz et al propose iterative spanning forest (ISF) to extract superpixels to maintain object boundary adherence [25]. It introduces a mixture seed sampling strategy to adaptively place seed numbers according to region contents, which solves the superpixel segmentation regularity issue via graph-tree theory.…”

Section: Related Workmentioning

confidence: 99%

Superpixel Segmentation by Forgetting Geodesic Distance

2020

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Superpixel segmentation could be of benefit to computer vision tasks due to its perceptually meaningful results with similar appearance and location. To obtain the accurate superpixel segmentation, existing methods introduce geodesic distance to fit the object boundaries. However, conventional geodesic distance easily suffers from error accumulation and excessive time consumption. This paper proposes a fast superpixel segmentation method based on a new geodesic distance, called forgetting geodesic distance. In contrast to the conventional geodesic distance, the forgetting geodesic distance utilizes a forgetting factor to gradually reduce the influence of previous path cost and focuses on the latest pixels' difference. Intuitively, a pixel with lower difference with respect to the latest path contextual distance will be more similar with the corresponding region. In the new path, the path cost devotes much greater attention to the latest pixels' difference and could significantly relieve error accumulation. The pixels are also aggregated with less dependence on seeds as the path extends, which avoids the seed updating. The experimental results validate that the proposed method obtains 2 percent and 1 percent gain on average compared with most of the state-of-the-art methods in terms of BSD500 and VOC2012 datasets, respectively.

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“…For each image, let us suppose that a fine partition is produced by an initial segmentation (for instance a set of superpixels [1] [24] [43], the basins produced by a classical watershed algorithm [27], or a segmentation into individual pixels/flat zones) and contains all contours making sense in the image. We define a dissimilarity measure between adjacent tiles of this fine partition.…”

Section: Hierarchies and Partitions 21 Graph-based Hierarchical Segmmentioning

confidence: 99%

“…Image segmentation has been shown to be inherently a multi-scale problem [17]. That is why hierarchical segmentation has become a major trend in image segmentation and most top-performance segmentation techniques [4] [33] [36] [26] [46] [43] fall into this category: hierarchical segmentation does not output a single partition of the image pixels into sets but instead a single multi-scale structure that aims at capturing relevant objects at all scales. Research on this topic is still vivid as differential area profiles [31], robust segmentation of high-dimensional data [16] as well as theoretical aspects regarding the concept of partition lattice [39] [37] and optimal partition in a hierarchy [19] [20] [47].…”

Section: Introductionmentioning

confidence: 99%

Prior-Based Hierarchical Segmentation Highlighting Structures of Interest

Fehri

Velasco-Forero

Meyer

2017

Lecture Notes in Computer Science

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Image segmentation is the process of partitioning an image into a set of meaningful regions according to some criteria. Hierarchical segmentation has emerged as a major trend in this regard as it favors the emergence of important regions at different scales. On the other hand, many methods allow us to have prior information on the position of structures of interest in the images. In this paper, we present a versatile hierarchical segmentation method that takes into account any prior spatial information and outputs a hierarchical segmentation that emphasizes the contours or regions of interest while preserving the important structures in the image. Several applications are presented that illustrate the method versatility and efficiency.

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An Iterative Spanning Forest Framework for Superpixel Segmentation

Cited by 72 publications

References 53 publications

Automated Brain Tumor Segmentation Based on Multi-Planar Superpixel Level Features Extracted From 3D MR Images

Automated Brain Tumor Segmentation Based on Multi-Planar Superpixel Level Features Extracted From 3D MR Images

Superpixel Segmentation by Forgetting Geodesic Distance

Prior-Based Hierarchical Segmentation Highlighting Structures of Interest

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