Literature DB >> 17846834

Automatic multilevel medical image annotation and retrieval.

A Mueen1, R Zainuddin, M Sapiyan Baba.   

Abstract

Image retrieval at the semantic level mostly depends on image annotation or image classification. Image annotation performance largely depends on three issues: (1) automatic image feature extraction; (2) a semantic image concept modeling; (3) algorithm for semantic image annotation. To address first issue, multilevel features are extracted to construct the feature vector, which represents the contents of the image. To address second issue, domain-dependent concept hierarchy is constructed for interpretation of image semantic concepts. To address third issue, automatic multilevel code generation is proposed for image classification and multilevel image annotation. We make use of the existing image annotation to address second and third issues. Our experiments on a specific domain of X-ray images have given encouraging results.

Mesh:

Year:  2007        PMID: 17846834      PMCID: PMC3043841          DOI: 10.1007/s10278-007-9070-3

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  5 in total

Review 1.  Automatic medical image annotation and keyword-based image retrieval using relevance feedback.

Authors:  Byoung Chul Ko; JiHyeon Lee; Jae-Yeal Nam
Journal:  J Digit Imaging       Date:  2012-08       Impact factor: 4.056

2.  MIARS: a medical image retrieval system.

Authors:  A Mueen; R Zainuddin; M Sapiyan Baba
Journal:  J Med Syst       Date:  2009-05-06       Impact factor: 4.460

3.  Automatic medical X-ray image classification using annotation.

Authors:  Mohammad Reza Zare; Ahmed Mueen; Woo Chaw Seng
Journal:  J Digit Imaging       Date:  2014-02       Impact factor: 4.056

4.  Generate Structured Radiology Report from CT Images Using Image Annotation Techniques: Preliminary Results with Liver CT.

Authors:  Samira Loveymi; Mir Hossein Dezfoulian; Muharram Mansoorizadeh
Journal:  J Digit Imaging       Date:  2020-04       Impact factor: 4.056

5.  Predicting visual semantic descriptive terms from radiological image data: preliminary results with liver lesions in CT.

Authors:  Adrien Depeursinge; Camille Kurtz; Christopher Beaulieu; Sandy Napel; Daniel Rubin
Journal:  IEEE Trans Med Imaging       Date:  2014-05-01       Impact factor: 10.048

  5 in total

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