Paper Info
Title
Image-based diagnostic aid for interstitial lung disease with secondary data integration.
Abstract
ABSTRACT Interstitial lung diseases (ILDs) are a relatively heterogeneous group of around 150 illnesses with often very unspecific symptoms. The most complete imaging method for the characterisation of ILDs is the high-resolution computed,tomography (HRCT) of the chest but a correct interpretation of these images is difficult even for specialists as many diseases are rare and thus little experience exists. Moreover, interpreting HRCT images requires knowledge of the context defined by clinical data of the studied case. A computerised diagnostic aid tool based on HRCT images with associated medical data to retrieve similar cases of ILDs from a dedicated database can bring quick and precious information for example for emergency radiologists. The experience from a pilot project highlighted the need for detailed database containing high-quality annotations in addition to clinical data. The state of the art is studied to identify requirements for image–based diagnostic aid for interstitial lung disease with secondary data integration. The data acquisition steps are detailed. The selection of the most relevant clinical parameters is done in collaboration with lung specialists from current literature, along with knowledge bases of computer–based diagnostic decision support systems. In order to perform high–quality annotations of the interstitial lung tissue in the HRCT images an annotation software and its own file format is implemented for DICOM images. A multimedia database is implemented to store ILD cases with clinical data and annotated image series. Cases from the University & University Hospitals of Geneva (HUG) are retrospectively and prospectively collected to populate the database. Currently, 59 cases with certified diagnosis and their clinical parameters are stored in the database as well as 254 image series of which 26 have their regions of interest annotated. The available data was used to test primary visual features for the classification of lung tissue patterns. These
Year
DOI
Venue
2007
10.1117/12.709533
Medical Imaging: Computer-Aided Diagnosis
Keywords
Field
DocType
similar case retrieval.,content-based image retrieval,quantitative image analysis,database construction,chest high-resolution ct,feature extrac- tion,texture analysis,knowledge base,decision support systems,data integration,data acquisition,databases,region of interest,computed tomography,data integrity,decision support system,high resolution,medical diagnostics,multimedia
Data integration,File format,Data mining,Multimedia database,DICOM,Image Series,Feature extraction,Interstitial lung disease,Medical physics,Medicine,Content-based image retrieval
Conference
Citations 
PageRank 
References 
6
0.62
6
Authors
6
Name
Order
Citations
PageRank
Adrien Depeursinge141838.83
Henning Müller22538218.89
Asmâa Hidki360.62
Pierre-Alexandre Poletti41029.07
Alexandra Platon514212.05
Antoine Geissbuhler681549.75