On the use of low-pass filters for image processing with inverse Laplacian models
A novel signal processing-oriented approach to solving problems involving inverse Laplacians is introduced. The Monogenic Signal is a powerful method of computing the phase of discrete signals in image data, however it is typically used with band-pass filters in the capacity of a feature detector. S...
| Main Authors: | , , , , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
2012
|
| _version_ | 1826265942423764992 |
|---|---|
| author | Ali, R Szilagyi, T Gooding, M Christlieb, M Brady, M |
| author_facet | Ali, R Szilagyi, T Gooding, M Christlieb, M Brady, M |
| author_sort | Ali, R |
| collection | OXFORD |
| description | A novel signal processing-oriented approach to solving problems involving inverse Laplacians is introduced. The Monogenic Signal is a powerful method of computing the phase of discrete signals in image data, however it is typically used with band-pass filters in the capacity of a feature detector. Substituting low-pass filters allows the Monogenic Signal to produce approximate solutions to the inverse Laplacian, with the added benefit of tunability and the generation of three equivariant properties (namely local energy, local phase and local orientation), which allow the development of powerful numerical solutions for a new set of problems. These principles are applied here in the context of biological cell segmentation from brightfield microscopy image data. The Monogenic Signal approach is used to generate reduced noise solutions to the Transport of Intensity Equation for optical phase recovery, and the resulting local phase and local orientation terms are combined in an iterative level set approach to accurately segment cell boundaries. Potential applications of this approach are discussed with respect to other fields. © 2011 Springer Science+Business Media, LLC. |
| first_indexed | 2024-03-06T20:31:28Z |
| format | Journal article |
| id | oxford-uuid:312f75bd-395e-452e-82a0-bddae9ab9a5d |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T20:31:28Z |
| publishDate | 2012 |
| record_format | dspace |
| spelling | oxford-uuid:312f75bd-395e-452e-82a0-bddae9ab9a5d2022-03-26T13:06:20ZOn the use of low-pass filters for image processing with inverse Laplacian modelsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:312f75bd-395e-452e-82a0-bddae9ab9a5dEnglishSymplectic Elements at Oxford2012Ali, RSzilagyi, TGooding, MChristlieb, MBrady, MA novel signal processing-oriented approach to solving problems involving inverse Laplacians is introduced. The Monogenic Signal is a powerful method of computing the phase of discrete signals in image data, however it is typically used with band-pass filters in the capacity of a feature detector. Substituting low-pass filters allows the Monogenic Signal to produce approximate solutions to the inverse Laplacian, with the added benefit of tunability and the generation of three equivariant properties (namely local energy, local phase and local orientation), which allow the development of powerful numerical solutions for a new set of problems. These principles are applied here in the context of biological cell segmentation from brightfield microscopy image data. The Monogenic Signal approach is used to generate reduced noise solutions to the Transport of Intensity Equation for optical phase recovery, and the resulting local phase and local orientation terms are combined in an iterative level set approach to accurately segment cell boundaries. Potential applications of this approach are discussed with respect to other fields. © 2011 Springer Science+Business Media, LLC. |
| spellingShingle | Ali, R Szilagyi, T Gooding, M Christlieb, M Brady, M On the use of low-pass filters for image processing with inverse Laplacian models |
| title | On the use of low-pass filters for image processing with inverse Laplacian models |
| title_full | On the use of low-pass filters for image processing with inverse Laplacian models |
| title_fullStr | On the use of low-pass filters for image processing with inverse Laplacian models |
| title_full_unstemmed | On the use of low-pass filters for image processing with inverse Laplacian models |
| title_short | On the use of low-pass filters for image processing with inverse Laplacian models |
| title_sort | on the use of low pass filters for image processing with inverse laplacian models |
| work_keys_str_mv | AT alir ontheuseoflowpassfiltersforimageprocessingwithinverselaplacianmodels AT szilagyit ontheuseoflowpassfiltersforimageprocessingwithinverselaplacianmodels AT goodingm ontheuseoflowpassfiltersforimageprocessingwithinverselaplacianmodels AT christliebm ontheuseoflowpassfiltersforimageprocessingwithinverselaplacianmodels AT bradym ontheuseoflowpassfiltersforimageprocessingwithinverselaplacianmodels |