Watershed-based maximum-homogeneity filtering

Michael W. Hansen; William E. Higgins

doi:10.1109/83.772247

Watershed-based maximum-homogeneity filtering

Michael W. Hansen, William E. Higgins

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

We introduce an image enhancement method referred to as the watershed-based maximum-homogeneity filter. This method first uses watershed analysis to subdivide the image into homogeneous pixel clusters called catchment basins. Next, using an adaptive, local, catchment-basin selection scheme, similar neighboring catchment basins are combined together to produce an enhanced image. Because the method starts with watershed analysis, it can preserve edge information and run with high computational efficiency. Illustrative results show that the method performs well relative to other popular nonlinear filters.

Original language	English (US)
Pages (from-to)	982-988
Number of pages	7
Journal	IEEE Transactions on Image Processing
Volume	8
Issue number	7
DOIs	https://doi.org/10.1109/83.772247
State	Published - 1999

All Science Journal Classification (ASJC) codes

Software
Computer Graphics and Computer-Aided Design

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title = "Watershed-based maximum-homogeneity filtering",

abstract = "We introduce an image enhancement method referred to as the watershed-based maximum-homogeneity filter. This method first uses watershed analysis to subdivide the image into homogeneous pixel clusters called catchment basins. Next, using an adaptive, local, catchment-basin selection scheme, similar neighboring catchment basins are combined together to produce an enhanced image. Because the method starts with watershed analysis, it can preserve edge information and run with high computational efficiency. Illustrative results show that the method performs well relative to other popular nonlinear filters.",

author = "Hansen, {Michael W.} and Higgins, {William E.}",

note = "Funding Information: Manuscript received July 14, 1996; revised August 18, 1998. A preliminary report of this work appeared in Proceedings of the 1995 International Conference on Image Processing. This work was supported in part by the National Cancer Institute of the National Institutes of Health under Grants CA-53607 and CA-74325. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Jeffrey J. Rodriguez.",

year = "1999",

doi = "10.1109/83.772247",

language = "English (US)",

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journal = "IEEE Transactions on Image Processing",

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T1 - Watershed-based maximum-homogeneity filtering

AU - Hansen, Michael W.

AU - Higgins, William E.

N1 - Funding Information: Manuscript received July 14, 1996; revised August 18, 1998. A preliminary report of this work appeared in Proceedings of the 1995 International Conference on Image Processing. This work was supported in part by the National Cancer Institute of the National Institutes of Health under Grants CA-53607 and CA-74325. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Jeffrey J. Rodriguez.

PY - 1999

Y1 - 1999

N2 - We introduce an image enhancement method referred to as the watershed-based maximum-homogeneity filter. This method first uses watershed analysis to subdivide the image into homogeneous pixel clusters called catchment basins. Next, using an adaptive, local, catchment-basin selection scheme, similar neighboring catchment basins are combined together to produce an enhanced image. Because the method starts with watershed analysis, it can preserve edge information and run with high computational efficiency. Illustrative results show that the method performs well relative to other popular nonlinear filters.

AB - We introduce an image enhancement method referred to as the watershed-based maximum-homogeneity filter. This method first uses watershed analysis to subdivide the image into homogeneous pixel clusters called catchment basins. Next, using an adaptive, local, catchment-basin selection scheme, similar neighboring catchment basins are combined together to produce an enhanced image. Because the method starts with watershed analysis, it can preserve edge information and run with high computational efficiency. Illustrative results show that the method performs well relative to other popular nonlinear filters.

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JO - IEEE Transactions on Image Processing

JF - IEEE Transactions on Image Processing

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ER -

Watershed-based maximum-homogeneity filtering

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