Interactive morphological watershed analysis for 3D medical images

Higgins, William E.; Ojard, Eric J

doi:10.1016/0895-6111(93)90033-j

Cited by 66 publications

(33 citation statements)

References 13 publications

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“…It was introduced by Digabel and Lantuejoul (29), extended by Beucher (30), analyzed theoretically by Maisonneuve (31), and formally defined in terms of flooding simulations by Vincent and Soille (32). Its popularity is attributable to its high computational efficiency and ability to extend it to 3D spaces (6,33,34), which makes it amenable to application to data-intensive 3D confocal image stacks. We describe an algorithm that combines the attractive features of the 3D watershed algorithm with algorithms that exploit available intensity gradient based cues and the expected anatomic shape of the nuclei, using a statistical model-based approach.…”

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confidence: 99%

A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks

et al. 2003

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Background: Automated segmentation of fluorescentlylabeled cell nuclei in 3D confocal microscope images is essential to many studies involving morphological and functional analysis. A common source of segmentation error is tight clustering of nuclei. There is a compelling need to minimize these errors for constructing highly automated scoring systems. Methods: A combination of two approaches is presented. First, an improved distance transform combining intensity gradients and geometric distance is used for the watershed step. Second, an explicit mathematical model for the anatomic characteristics of cell nuclei such as size and shape measures is incorporated. This model is constructed automatically from the data. Deliberate initial over-segmentation of the image data is performed, followed by statistical model-based merging. A confidence score is computed for each detected nucleus, measuring

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confidence: 99%

A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks

et al. 2003

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“…To address these problems, a variety of interactive segmentation methods are being developed [2]. These methods range from totally manual painting of object regions or drawing of object boundaries, to the detection of object region/boundaries with minimal user assistance [3]- [7].…”

Section: Introductionmentioning

confidence: 99%

An ultra-fast user-steered image segmentation paradigm: live wire on the fly

Falcão

Udupa

Miyazawa

2000

IEEE Trans. Med. Imaging

248

154

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Abstract-We have been developing general user steered image segmentation strategies for routine use in applications involving a large number of data sets. In the past, we have presented three segmentation paradigms: live wire, live lane, and a three-dimensional (3-D) extension of the live-wire method. In this paper, we introduce an ultra-fast live-wire method, referred to as live wire on the fly, for further reducing user's time compared to the basic live-wire method. In live wire, 3-D/four-dimensional (4-D) object boundaries are segmented in a slice-by-slice fashion. To segment a two-dimensional (2-D) boundary, the user initially picks a point on the boundary and all possible minimum-cost paths from this point to all other points in the image are computed via Dijkstra's algorithm. Subsequently, a live wire is displayed in real time from the initial point to any subsequent position taken by the cursor. If the cursor is close to the desired boundary, the live wire snaps on to the boundary. The cursor is then deposited and a new live-wire segment is found next. The entire 2-D boundary is specified via a set of live-wire segments in this fashion. A drawback of this method is that the speed of optimal path computation depends on image size. On modestly powered computers, for images of even modest size, some sluggishness appears in user interaction, which reduces the overall segmentation efficiency. In this work, we solve this problem by exploiting some known properties of graphs to avoid unnecessary minimum-cost path computation during segmentation. In live wire on the fly, when the user selects a point on the boundary the live-wire segment is computed and displayed in real time from the selected point to any subsequent position of the cursor in the image, even for large images and even on low-powered computers. Based on 492 tracing experiments from an actual medical application, we demonstrate that live wire on the fly is 1.3-31 times faster than live wire for actual segmentation for varying image sizes, although the pure computational part alone is found to be about 120 times faster.

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“…Free-form variants of this approach describe the evolution of a surface, under the control of a cost function, which results in an optimum separation of candidate regions (37)(38)(39). Particularly in the medical imaging domain, techniques based on morphology have been reported (40)(41)(42) as well as an exploration of whether neural networks and adaptive logic can simplify the region classification process (43,44).…”

Section: A 3d Split and Merge Methodsmentioning

confidence: 98%