Medical image retrieval and automatic annotation

OHSU at ImageCLEF 2007

Jayashree Kalpathy-Cramer, William (Bill) Hersh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Oregon Health and Science University participated in the medical retrieval and medical annotation tasks of ImageCLEF 2007. In the medical retrieval task, we created a web-based retrieval system built on a full-text index of both image and case annotations. The text-based search engine was implemented in Ruby using Ferret, a port of Lucene and a custom query parser. In addition to this textual index of annotations, supervised machine learning techniques using visual features were used to classify the images based on image acquisition modality. All images were annotated with the purported modality. Purely textual runs as well as mixed runs using the purported modality were submitted, with the latter performing among the best of all participating research groups. In the automatic annotation task, we used the 'gist' technique to create the feature vectors. Using statistics derived from a set of multi-scale oriented filters, we created a 512-dimensional vector. PCA was then used to create a 100-dimensional vector. This feature vector was fed into a two layer neural network. Our error rate on the 1000 test images was 67.8 using the hierarchical error calculations.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages623-630
Number of pages8
Volume5152 LNCS
DOIs
StatePublished - 2008
Event8th Workshop of the Cross-Language Evaluation Forum, CLEF 2007 - Budapest, Hungary
Duration: Sep 19 2007Sep 21 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5152 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other8th Workshop of the Cross-Language Evaluation Forum, CLEF 2007
CountryHungary
CityBudapest
Period9/19/079/21/07

Fingerprint

Image retrieval
Image Retrieval
Medical Image
Annotation
Modality
Retrieval
Feature Vector
Ruby
Image Acquisition
Image acquisition
Supervised Learning
Search engines
Search Engine
Web-based
Error Rate
Learning systems
Machine Learning
Health
Classify
Statistics

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Kalpathy-Cramer, J., & Hersh, W. B. (2008). Medical image retrieval and automatic annotation: OHSU at ImageCLEF 2007. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5152 LNCS, pp. 623-630). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5152 LNCS). https://doi.org/10.1007/978-3-540-85760-0-79

Medical image retrieval and automatic annotation : OHSU at ImageCLEF 2007. / Kalpathy-Cramer, Jayashree; Hersh, William (Bill).

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5152 LNCS 2008. p. 623-630 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5152 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kalpathy-Cramer, J & Hersh, WB 2008, Medical image retrieval and automatic annotation: OHSU at ImageCLEF 2007. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 5152 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5152 LNCS, pp. 623-630, 8th Workshop of the Cross-Language Evaluation Forum, CLEF 2007, Budapest, Hungary, 9/19/07. https://doi.org/10.1007/978-3-540-85760-0-79
Kalpathy-Cramer J, Hersh WB. Medical image retrieval and automatic annotation: OHSU at ImageCLEF 2007. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5152 LNCS. 2008. p. 623-630. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-85760-0-79
Kalpathy-Cramer, Jayashree ; Hersh, William (Bill). / Medical image retrieval and automatic annotation : OHSU at ImageCLEF 2007. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5152 LNCS 2008. pp. 623-630 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{88db7892a8ad477f964ce9f75dc93381,
title = "Medical image retrieval and automatic annotation: OHSU at ImageCLEF 2007",
abstract = "Oregon Health and Science University participated in the medical retrieval and medical annotation tasks of ImageCLEF 2007. In the medical retrieval task, we created a web-based retrieval system built on a full-text index of both image and case annotations. The text-based search engine was implemented in Ruby using Ferret, a port of Lucene and a custom query parser. In addition to this textual index of annotations, supervised machine learning techniques using visual features were used to classify the images based on image acquisition modality. All images were annotated with the purported modality. Purely textual runs as well as mixed runs using the purported modality were submitted, with the latter performing among the best of all participating research groups. In the automatic annotation task, we used the 'gist' technique to create the feature vectors. Using statistics derived from a set of multi-scale oriented filters, we created a 512-dimensional vector. PCA was then used to create a 100-dimensional vector. This feature vector was fed into a two layer neural network. Our error rate on the 1000 test images was 67.8 using the hierarchical error calculations.",
author = "Jayashree Kalpathy-Cramer and Hersh, {William (Bill)}",
year = "2008",
doi = "10.1007/978-3-540-85760-0-79",
language = "English (US)",
isbn = "3540857591",
volume = "5152 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "623--630",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Medical image retrieval and automatic annotation

T2 - OHSU at ImageCLEF 2007

AU - Kalpathy-Cramer, Jayashree

AU - Hersh, William (Bill)

PY - 2008

Y1 - 2008

N2 - Oregon Health and Science University participated in the medical retrieval and medical annotation tasks of ImageCLEF 2007. In the medical retrieval task, we created a web-based retrieval system built on a full-text index of both image and case annotations. The text-based search engine was implemented in Ruby using Ferret, a port of Lucene and a custom query parser. In addition to this textual index of annotations, supervised machine learning techniques using visual features were used to classify the images based on image acquisition modality. All images were annotated with the purported modality. Purely textual runs as well as mixed runs using the purported modality were submitted, with the latter performing among the best of all participating research groups. In the automatic annotation task, we used the 'gist' technique to create the feature vectors. Using statistics derived from a set of multi-scale oriented filters, we created a 512-dimensional vector. PCA was then used to create a 100-dimensional vector. This feature vector was fed into a two layer neural network. Our error rate on the 1000 test images was 67.8 using the hierarchical error calculations.

AB - Oregon Health and Science University participated in the medical retrieval and medical annotation tasks of ImageCLEF 2007. In the medical retrieval task, we created a web-based retrieval system built on a full-text index of both image and case annotations. The text-based search engine was implemented in Ruby using Ferret, a port of Lucene and a custom query parser. In addition to this textual index of annotations, supervised machine learning techniques using visual features were used to classify the images based on image acquisition modality. All images were annotated with the purported modality. Purely textual runs as well as mixed runs using the purported modality were submitted, with the latter performing among the best of all participating research groups. In the automatic annotation task, we used the 'gist' technique to create the feature vectors. Using statistics derived from a set of multi-scale oriented filters, we created a 512-dimensional vector. PCA was then used to create a 100-dimensional vector. This feature vector was fed into a two layer neural network. Our error rate on the 1000 test images was 67.8 using the hierarchical error calculations.

UR - http://www.scopus.com/inward/record.url?scp=70349788974&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70349788974&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-85760-0-79

DO - 10.1007/978-3-540-85760-0-79

M3 - Conference contribution

SN - 3540857591

SN - 9783540857594

VL - 5152 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 623

EP - 630

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -