Classification of diffuse lung disease patterns on high-resolution computed tomography by a bag of words approach.
Med Image Comput Comput Assist Interv
; 14(Pt 3): 183-90, 2011.
Article
in En
| MEDLINE
| ID: mdl-22003698
ABSTRACT
Visual inspection of diffuse lung disease (DLD) patterns on high-resolution computed tomography (HRCT) is difficult because of their high complexity. We proposed a bag of words based method on the classification of these textural patters in order to improve the detection and diagnosis of DLD for radiologists. Six kinds of typical pulmonary patterns were considered in this work. They were consolidation, ground-glass opacity, honeycombing, emphysema, nodular and normal tissue. Because they were characterized by both CT values and shapes, we proposed a set of statistical measure based local features calculated from both CT values and the eigen-values of Hessian matrices. The proposed method could achieve the recognition rate of 95.85%, which was higher comparing with one global feature based method and two other CT values based bag of words methods.
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Collection:
01-internacional
Database:
MEDLINE
Main subject:
Radiology
/
Image Processing, Computer-Assisted
/
Radiographic Image Interpretation, Computer-Assisted
/
Lung Diseases
Type of study:
Diagnostic_studies
Limits:
Humans
Language:
En
Journal:
Med Image Comput Comput Assist Interv
Journal subject:
DIAGNOSTICO POR IMAGEM
/
INFORMATICA MEDICA
Year:
2011
Document type:
Article
Affiliation country:
Japan