Locating the eyes in CT brain scan data:
Abstract: "We describe a technique for locating the eyes in Computed Tomography brain scan data. The objective is to automatically localise the eyes for protection during radiotherapy planning. The image feature that is exploited is the circularity of the eyes. After data preprocessing to remov...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Edinburgh
1993
|
| Schriftenreihe: | University <Edinburgh> / Department of Artificial Intelligence: DAI research paper
620 |
| Schlagworte: | |
| Zusammenfassung: | Abstract: "We describe a technique for locating the eyes in Computed Tomography brain scan data. The objective is to automatically localise the eyes for protection during radiotherapy planning. The image feature that is exploited is the circularity of the eyes. After data preprocessing to remove parts of the CT machinery, signature analysis is performed to locate areas of interest. By applying the Canny edge detector to these areas, data is further reduced to the significant edge fragments. The Hough Transform is then applied to estimate radii and centres of the CT sections through the eyes. The Converging Squares algorithm is used as an efficient and robust method to search the Hough Transform parameter space. The results are processed by the hypothesis generation stage which clusters them according to the x, y, z coordinates of the suggested centres. The ISODATA algorithm is used for clustering. The hypotheses are assessed and sorted and the most valid hypothesis is selected and refined using a second Hough Transform. After the rejection of the invalid members of the hypothesis cluster, an ellipsoid is fitted to the new cluster centre and the results are drawn on the data. The method is fast and robust. The method was tested using five different data sets and it performed well on all of them." |
| Beschreibung: | [10] S. |
Internformat
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| 100 | 1 | |a Kaggelides, Kostis |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Locating the eyes in CT brain scan data |c Kostis Kaggelides ; Peter J. Elliot ; Robert B. Fisher |
| 264 | 1 | |a Edinburgh |c 1993 | |
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| 490 | 1 | |a University <Edinburgh> / Department of Artificial Intelligence: DAI research paper |v 620 | |
| 520 | 3 | |a Abstract: "We describe a technique for locating the eyes in Computed Tomography brain scan data. The objective is to automatically localise the eyes for protection during radiotherapy planning. The image feature that is exploited is the circularity of the eyes. After data preprocessing to remove parts of the CT machinery, signature analysis is performed to locate areas of interest. By applying the Canny edge detector to these areas, data is further reduced to the significant edge fragments. The Hough Transform is then applied to estimate radii and centres of the CT sections through the eyes. The Converging Squares algorithm is used as an efficient and robust method to search the Hough Transform parameter space. The results are processed by the hypothesis generation stage which clusters them according to the x, y, z coordinates of the suggested centres. The ISODATA algorithm is used for clustering. The hypotheses are assessed and sorted and the most valid hypothesis is selected and refined using a second Hough Transform. After the rejection of the invalid members of the hypothesis cluster, an ellipsoid is fitted to the new cluster centre and the results are drawn on the data. The method is fast and robust. The method was tested using five different data sets and it performed well on all of them." | |
| 650 | 7 | |a Pattern recognition, image processing and remote sensing |2 sigle | |
| 650 | 7 | |a Radiobiology |2 sigle | |
| 650 | 4 | |a Tomography | |
| 700 | 1 | |a Elliot, Peter J. |e Verfasser |4 aut | |
| 700 | 1 | |a Fisher, Robert B. |e Verfasser |4 aut | |
| 810 | 2 | |a Department of Artificial Intelligence: DAI research paper |t University <Edinburgh> |v 620 |w (DE-604)BV010450646 |9 620 | |
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Datensatz im Suchindex
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| author | Kaggelides, Kostis Elliot, Peter J. Fisher, Robert B. |
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| building | Verbundindex |
| bvnumber | BV010461121 |
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| format | Book |
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| id | DE-604.BV010461121 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-09T17:52:53Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-006969845 |
| oclc_num | 32223101 |
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| owner_facet | DE-91G DE-BY-TUM |
| physical | [10] S. |
| publishDate | 1993 |
| publishDateSearch | 1993 |
| publishDateSort | 1993 |
| record_format | marc |
| series2 | University <Edinburgh> / Department of Artificial Intelligence: DAI research paper |
| spelling | Kaggelides, Kostis Verfasser aut Locating the eyes in CT brain scan data Kostis Kaggelides ; Peter J. Elliot ; Robert B. Fisher Edinburgh 1993 [10] S. txt rdacontent n rdamedia nc rdacarrier University <Edinburgh> / Department of Artificial Intelligence: DAI research paper 620 Abstract: "We describe a technique for locating the eyes in Computed Tomography brain scan data. The objective is to automatically localise the eyes for protection during radiotherapy planning. The image feature that is exploited is the circularity of the eyes. After data preprocessing to remove parts of the CT machinery, signature analysis is performed to locate areas of interest. By applying the Canny edge detector to these areas, data is further reduced to the significant edge fragments. The Hough Transform is then applied to estimate radii and centres of the CT sections through the eyes. The Converging Squares algorithm is used as an efficient and robust method to search the Hough Transform parameter space. The results are processed by the hypothesis generation stage which clusters them according to the x, y, z coordinates of the suggested centres. The ISODATA algorithm is used for clustering. The hypotheses are assessed and sorted and the most valid hypothesis is selected and refined using a second Hough Transform. After the rejection of the invalid members of the hypothesis cluster, an ellipsoid is fitted to the new cluster centre and the results are drawn on the data. The method is fast and robust. The method was tested using five different data sets and it performed well on all of them." Pattern recognition, image processing and remote sensing sigle Radiobiology sigle Tomography Elliot, Peter J. Verfasser aut Fisher, Robert B. Verfasser aut Department of Artificial Intelligence: DAI research paper University <Edinburgh> 620 (DE-604)BV010450646 620 |
| spellingShingle | Kaggelides, Kostis Elliot, Peter J. Fisher, Robert B. Locating the eyes in CT brain scan data Pattern recognition, image processing and remote sensing sigle Radiobiology sigle Tomography |
| title | Locating the eyes in CT brain scan data |
| title_auth | Locating the eyes in CT brain scan data |
| title_exact_search | Locating the eyes in CT brain scan data |
| title_full | Locating the eyes in CT brain scan data Kostis Kaggelides ; Peter J. Elliot ; Robert B. Fisher |
| title_fullStr | Locating the eyes in CT brain scan data Kostis Kaggelides ; Peter J. Elliot ; Robert B. Fisher |
| title_full_unstemmed | Locating the eyes in CT brain scan data Kostis Kaggelides ; Peter J. Elliot ; Robert B. Fisher |
| title_short | Locating the eyes in CT brain scan data |
| title_sort | locating the eyes in ct brain scan data |
| topic | Pattern recognition, image processing and remote sensing sigle Radiobiology sigle Tomography |
| topic_facet | Pattern recognition, image processing and remote sensing Radiobiology Tomography |
| volume_link | (DE-604)BV010450646 |
| work_keys_str_mv | AT kaggelideskostis locatingtheeyesinctbrainscandata AT elliotpeterj locatingtheeyesinctbrainscandata AT fisherrobertb locatingtheeyesinctbrainscandata |