An Efficient Hillclimbing-based Watershed Algorithm and its Prototype Hardware Architecture

Rambabu, C.; Chakrabarti, Indrajit

doi:10.1007/s11265-007-0157-3

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…Rambabu et. Al [27] has proposed a hill climbing method which also produces a linear time complexity. But existing methods use sequential approach which limited them from achieving maximum efficiency.…”

Section: Watershed Segmentationmentioning

confidence: 99%

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An Improved Fast Watershed Algorithm based on finding the Shortest Paths with Breadth First Search

Suphalakshmi¹,

Anandhakumar²

2012

IJCA

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A watershed based on rainfall simulation is a proven technique for image segmentation. The only problem associated with it is the path regularization for pixels in the plateau. As the existing methods employ sequential techniques, the complexity of the algorithms remains high due to repetitive scanning of pixels. We propose an iterative method for finding the shortest and steepest path based on Breadth first search (BFS), which addresses the path regularization problem eliminating the repetitive scans. Experiments show, that the proposed algorithm significantly reduces the running time without compensating the performance when compared with the fastest known algorithm. General TermsFast watersheds, Image segmentation, Breadth First search, shortest path, path regularization. INTRODUCTIONImage segmentation is a fundamental problem in image analysis. The segmentation process can rely both on the uniformity of features within the regions or on edge evidence. Idea of watershed algorithm was developed from the field of topography. If we consider the analogy of a landscape and rain, water will find the swiftest descent path until it reaches some lake or sea, we can envisage lakes and seas correspond to regional minima [7]. The landscape can be completely partitioned into regions which draw water to a particular sea or lake. These regions are influence zones of the regional minima in an image which forms catchment basin. Though there are lot of variations and combinations with other methods, watershed still remain in research to improve either its time complexity, reducing the problem of over segmentation, and solving the path regularization. We, in this paper address all the three problems of watershed with our proposed algorithm.

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Section: Watershed Segmentationmentioning

confidence: 99%

“…From the literature we can find various flavours of watershed algorithms based on markers [24], graphs and graph cuts [19], level sets [20] and a number of improved versions from the references [25], [27], [28] and [29]. Watersheds algorithm was also extended to 3D space by Lee Seng yeong et.…”

Section: Introductionmentioning

confidence: 99%

An Improved Fast Watershed Algorithm based on finding the Shortest Paths with Breadth First Search

Suphalakshmi¹,

Anandhakumar²

2012

IJCA

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“…The background/foreground detection starts with the detection of plateau minima in the gradient stack and then, labels the largest minima of height h as background and the others as foreground. At last, we applied a fast hillclimbing technique [11] on all optical slices simultaneously.…”

Section: Background/foreground Detectionmentioning

confidence: 99%

3D Segmentation for the Study of Cell Cycle Progression in Live Drosophila Embryos

Rambabu¹,

Choo²,

Kriston‐Vizi³

et al. 2009

Proceedings of the 1st International Workshop on Medical Image Analysis and Description for Diagnosis Systems

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We study the dynamics of cell division in live Drosophila embryos using fluorescent proteins and 3D time-lapse microscopy. Accurate segmentation of nuclei and mitotic chromosomes labeled by the live reporter histone-GFP is a prerequisite for subsequent tracking and quantitative object analysis. We propose an automated 3D segmentation method based on narrow band level sets that preserves the boundary of the cell nuclei and removes signals that are artifacts of live cell imaging. We introduce an improved 3D narrow band approach in the region shrinking and growing process for accurately segmenting the cell nuclei from background. The proposed method has been evaluated with the ground truth regarding the object level accuracy and segmentation quality. Both the object level accuracy and pixel accuracy of the proposed method are around 96% and 85% respectively. Our algorithm can robustly segment nuclei and chromosomes in different phase of the division cycle.

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Segmentation of Images Using Watershed and MSER: A State-of-the-Art Review

Silvoster¹,

Kumar

2020

Studies in Systems, Decision and Control

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An Efficient Hillclimbing-based Watershed Algorithm and its Prototype Hardware Architecture

Cited by 9 publications

References 25 publications

An Improved Fast Watershed Algorithm based on finding the Shortest Paths with Breadth First Search

An Improved Fast Watershed Algorithm based on finding the Shortest Paths with Breadth First Search

3D Segmentation for the Study of Cell Cycle Progression in Live Drosophila Embryos

Segmentation of Images Using Watershed and MSER: A State-of-the-Art Review

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