Fuzzy connectedness and image segmentation

Udupa, Jayaram K.; Saha, Punam K.

doi:10.1109/jproc.2003.817883

Cited by 158 publications

(147 citation statements)

References 70 publications

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“…The value of μ κ (c, d) depends not only on the adjacency strength μ κ (c, d), but also on the intensity function f. There are many methods of finding the affinity relation for a given scene. (See the survey paper [13].) In this paper, we will always assume that an appropriate affinity has already been specified for the segmentation task on hand.…”

Section: Basic Definitions and Notationmentioning

confidence: 99%

“…The level of fuzzy connectedness between c and d is considered to be the maximum of the strengths of all paths between c and d. For segmentation purposes, FC is utilized in several ways as described below. See [13] for a review of the different FC definitions and how they are employed in segmentation and applications.…”

Section: Introductionmentioning

confidence: 99%

“…The proper design of affinity is crucial to the effectiveness of the segmentations that ensue, no matter what type of FC is used. In scale-based FC [13], which is applicable to all of AFC, RFC, and IRFC, affinity is defined not based just on the properties of the two spels under question but also on the properties of all spels in the local scale region around the two spels. In vectorial FC [27], affinity is constructed in a vectorial manner, allowing spels to assume not just scalar values but any vectorial values, which may come from the original acquisition of the image owing to multiple image properties at every spel or that may arise from vector-valued features estimated from the given scalar or vectorial image.…”

Section: Introductionmentioning

confidence: 99%

“…By using S and V to abbreviate "scale-based" and "vectorial," and by allowing a combination of these indexes with different types of FC referred to above, we may describe the FC family that is developed to date by methods denoted by AFC, SAFC, VAFC, VSAFC, RFC, SRFC, VRFC, VSRFC, IRFC, SIRFC, VIRFC, and VSIRFC. See [13] and the original articles cited therein for further details on each member of this family.…”

Section: Introductionmentioning

confidence: 99%

“…(See Udupa, Saha, Lotufo [15] and Saha, Udupa [14].) Our theory, which solves a problem of Udupa and Saha from [13], allows simultaneous segmentation involving an arbitrary number of objects. Moreover, each segment can be indicated by more than one seed, which is often more natural and easier than a single seed object identification.…”

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confidence: 99%

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Iterative relative fuzzy connectedness for multiple objects with multiple seeds

Ciesielski

Udupa

Saha

et al. 2007

Computer Vision and Image Understanding

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In this paper we present a new theory and an algorithm for image segmentation based on a strength of connectedness between every pair of image elements. The object definition used in the segmentation algorithm utilizes the notion of iterative relative fuzzy connectedness, IRFC. In previously published research, the IRFC theory was developed only for the case when the segmentation was involved with just two segments, an object and a background, and each of the segments was indicated by a single seed. (See Udupa, Saha, Lotufo [15] and Saha, Udupa [14].) Our theory, which solves a problem of Udupa and Saha from [13], allows simultaneous segmentation involving an arbitrary number of objects. Moreover, each segment can be indicated by more than one seed, which is often more natural and easier than a single seed object identification.The first iteration step of the IRFC algorithm gives a segmentation known as relative fuzzy connectedness, RFC, segmentation. Thus, the IRFC technique is an extension of the RFC method. Although the RFC theory, due to Saha and Udupa [19], is developed in the multi object/multi seed framework, the theoretical results presented here are considerably more delicate in nature and do not use the results from [19]. On the other hand, the theoretical results from [19] are immediate consequences of the results presented here. Moreover, the new framework not only subsumes previous fuzzy connectedness descriptions but also sheds new light on them. Thus, there are fundamental theoretical advances made in this paper.We present examples of segmentations obtained via our IRFC based algorithm in the multi object/ multi seed environment, and compare it with the results obtained with the RFC based algorithm. Our results indicate that, in many situations, IRFC outperforms RFC, but there also exist instances where the gain in performance is negligible.

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Section: Basic Definitions and Notationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Iterative relative fuzzy connectedness for multiple objects with multiple seeds

Ciesielski

Udupa

Saha

et al. 2007

Computer Vision and Image Understanding

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Parallel fuzzy segmentation of multiple objects

Garduño¹,

Herman²

2008

Int J Imaging Syst Tech

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The usefulness of fuzzy segmentation algorithms based on fuzzy connectedness principles has been established in numerous publications. New technologies are capable of producing larger-andlarger datasets and this causes the sequential implementations of fuzzy segmentation algorithms to be time-consuming. We have adapted a sequential fuzzy segmentation algorithm to multiprocessor machines. We demonstrate the efficacy of such a distributed fuzzy segmentation algorithm by testing it with large datasets (of the order of 50 million points/voxels/items): a speedup factor of approximately five over the sequential implementation seems to be the norm.

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Singh

Rajagopalan

Kim

et al. 2008

Int J Imaging Syst Tech

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Certain features such as small vascular lesions seen in human MRI are detected reliably only in postmortem histological samples by microscopic imaging. Co-registration of these microscopically detected features to their corresponding locations in the in-vivo images would be of great benefit to understanding the MRI signatures of specific diseases. Using non-linear Polynomial transformation, we report a method to co-register in-vivo MRIs to microscopic images of histological samples drawn off the postmortem brain. The approach utilizes digital photographs of postmortem slices as an intermediate reference to co-register the MRIs to microscopy. The overall procedure is challenging due to gross structural deformations in the postmortem brain during extraction and subsequent distortions in the histological preparations. Hemispheres of the brain were co-registered separately to mitigate these effects. Approaches relying on matching single-slices, multiple-slices and entire volumes in conjunction with different similarity measures suggested that using four slices at a time in combination with two sequential measures, Pearson correlation coefficient followed by mutual information, produced the best MRI-postmortem co-registration according to a voxel mismatch count. The accuracy of the overall registration was evaluated by measuring the 3D Euclidean distance between the locations of microscopically identified lesions on postmortem slices and their MRI-postmortem co-registered locations. The results show a mean 3D displacement of 5.1 ± 2.0 mm between the in-vivo MRI and microscopically determined locations for 21 vascular lesions in 11 subjects.

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Fuzzy connectedness and image segmentation

Abstract: Image segmentation-the process of defining objects in images-remains the most challenging problem in image processing

Cited by 158 publications

References 70 publications

Iterative relative fuzzy connectedness for multiple objects with multiple seeds

Iterative relative fuzzy connectedness for multiple objects with multiple seeds

Parallel fuzzy segmentation of multiple objects

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