PARAMETER ESTIMATION IN NON-HOMOGENEOUS BOOLEAN MODELS: AN APPLICATION TO PLANT DEFENSE RESPONSE
Many medical and biological problems require to extract information from microscopical images. Boolean models have been extensively used to analyze binary images of random clumps in many scientific fields. In this paper, a particular type of Boolean model with an underlying non-stationary point proc...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Slovenian Society for Stereology and Quantitative Image Analysis
2014-11-01
|
| Series: | Image Analysis and Stereology |
| Subjects: | |
| Online Access: | http://www.ias-iss.org/ojs/IAS/article/view/1076 |
| _version_ | 1811302482023809024 |
|---|---|
| author | Maria Angeles Gallego Maria Victoria Ibanez Amelia Simó |
| author_facet | Maria Angeles Gallego Maria Victoria Ibanez Amelia Simó |
| author_sort | Maria Angeles Gallego |
| collection | DOAJ |
| description | Many medical and biological problems require to extract information from microscopical images. Boolean models have been extensively used to analyze binary images of random clumps in many scientific fields. In this paper, a particular type of Boolean model with an underlying non-stationary point process is considered. The intensity of the underlying point process is formulated as a fixed function of the distance to a region of interest. A method to estimate the parameters of this Boolean model is introduced, and its performance is checked in two different settings. Firstly, a comparative study with other existent methods is done using simulated data. Secondly, the method is applied to analyze the <em>longleaf</em> data set, which is a very popular data set in the context of point processes included in the R package <em>spatstat</em>. Obtained results show that the new method provides as accurate estimates as those obtained with more complex methods developed for the general case. Finally, to illustrate the application of this model and this method, a particular type of phytopathological images are analyzed. These images show callose depositions in leaves of Arabidopsis plants. The analysis of callose depositions, is very popular in the phytopathological literature to quantify activity of plant immunity. |
| first_indexed | 2024-04-13T07:29:53Z |
| format | Article |
| id | doaj.art-1ba09b94f8674bb6a277d4dc5030a129 |
| institution | Directory Open Access Journal |
| issn | 1580-3139 1854-5165 |
| language | English |
| last_indexed | 2024-04-13T07:29:53Z |
| publishDate | 2014-11-01 |
| publisher | Slovenian Society for Stereology and Quantitative Image Analysis |
| record_format | Article |
| series | Image Analysis and Stereology |
| spelling | doaj.art-1ba09b94f8674bb6a277d4dc5030a1292022-12-22T02:56:23ZengSlovenian Society for Stereology and Quantitative Image AnalysisImage Analysis and Stereology1580-31391854-51652014-11-01341273810.5566/ias.1076925PARAMETER ESTIMATION IN NON-HOMOGENEOUS BOOLEAN MODELS: AN APPLICATION TO PLANT DEFENSE RESPONSEMaria Angeles Gallego0Maria Victoria Ibanez1Amelia Simó2University Jaume I CastellónUniversity Jaume I CastellónUniversity Jaume I CastellónMany medical and biological problems require to extract information from microscopical images. Boolean models have been extensively used to analyze binary images of random clumps in many scientific fields. In this paper, a particular type of Boolean model with an underlying non-stationary point process is considered. The intensity of the underlying point process is formulated as a fixed function of the distance to a region of interest. A method to estimate the parameters of this Boolean model is introduced, and its performance is checked in two different settings. Firstly, a comparative study with other existent methods is done using simulated data. Secondly, the method is applied to analyze the <em>longleaf</em> data set, which is a very popular data set in the context of point processes included in the R package <em>spatstat</em>. Obtained results show that the new method provides as accurate estimates as those obtained with more complex methods developed for the general case. Finally, to illustrate the application of this model and this method, a particular type of phytopathological images are analyzed. These images show callose depositions in leaves of Arabidopsis plants. The analysis of callose depositions, is very popular in the phytopathological literature to quantify activity of plant immunity.http://www.ias-iss.org/ojs/IAS/article/view/1076binary imagescallose depositionmixed volumesnon-homogeneous Boolean modelparameter estimation |
| spellingShingle | Maria Angeles Gallego Maria Victoria Ibanez Amelia Simó PARAMETER ESTIMATION IN NON-HOMOGENEOUS BOOLEAN MODELS: AN APPLICATION TO PLANT DEFENSE RESPONSE Image Analysis and Stereology binary images callose deposition mixed volumes non-homogeneous Boolean model parameter estimation |
| title | PARAMETER ESTIMATION IN NON-HOMOGENEOUS BOOLEAN MODELS: AN APPLICATION TO PLANT DEFENSE RESPONSE |
| title_full | PARAMETER ESTIMATION IN NON-HOMOGENEOUS BOOLEAN MODELS: AN APPLICATION TO PLANT DEFENSE RESPONSE |
| title_fullStr | PARAMETER ESTIMATION IN NON-HOMOGENEOUS BOOLEAN MODELS: AN APPLICATION TO PLANT DEFENSE RESPONSE |
| title_full_unstemmed | PARAMETER ESTIMATION IN NON-HOMOGENEOUS BOOLEAN MODELS: AN APPLICATION TO PLANT DEFENSE RESPONSE |
| title_short | PARAMETER ESTIMATION IN NON-HOMOGENEOUS BOOLEAN MODELS: AN APPLICATION TO PLANT DEFENSE RESPONSE |
| title_sort | parameter estimation in non homogeneous boolean models an application to plant defense response |
| topic | binary images callose deposition mixed volumes non-homogeneous Boolean model parameter estimation |
| url | http://www.ias-iss.org/ojs/IAS/article/view/1076 |
| work_keys_str_mv | AT mariaangelesgallego parameterestimationinnonhomogeneousbooleanmodelsanapplicationtoplantdefenseresponse AT mariavictoriaibanez parameterestimationinnonhomogeneousbooleanmodelsanapplicationtoplantdefenseresponse AT ameliasimo parameterestimationinnonhomogeneousbooleanmodelsanapplicationtoplantdefenseresponse |