Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Interactive radiographic image retrieval system
KTH, School of Technology and Health (STH).
2017 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 139, 209-220 p.Article in journal (Refereed) Published
Abstract [en]

Background and Objective Content based medical image retrieval (CBMIR) systems enable fast diagnosis through quantitative assessment of the visual information and is an active research topic over the past few decades. Most of the state-of-the-art CBMIR systems suffer from various problems: computationally expensive due to the usage of high dimensional feature vectors and complex classifier/clustering schemes. Inability to properly handle the “semantic gap” and the high intra-class versus inter-class variability problem of the medical image database (like radiographic image database). This yields an exigent demand for developing highly effective and computationally efficient retrieval system. Methods We propose a novel interactive two-stage CBMIR system for diverse collection of medical radiographic images. Initially, Pulse Coupled Neural Network based shape features are used to find out the most probable (similar) image classes using a novel “similarity positional score” mechanism. This is followed by retrieval using Non-subsampled Contourlet Transform based texture features considering only the images of the pre-identified classes. Maximal information compression index is used for unsupervised feature selection to achieve better results. To reduce the semantic gap problem, the proposed system uses a novel fuzzy index based relevance feedback mechanism by incorporating subjectivity of human perception in an analytic manner. Results Extensive experiments were carried out to evaluate the effectiveness of the proposed CBMIR system on a subset of Image Retrieval in Medical Applications (IRMA)-2009 database consisting of 10,902 labeled radiographic images of 57 different modalities. We obtained overall average precision of around 98% after only 2–3 iterations of relevance feedback mechanism. We assessed the results by comparisons with some of the state-of-the-art CBMIR systems for radiographic images. Conclusions Unlike most of the existing CBMIR systems, in the proposed two-stage hierarchical framework, main importance is given on constructing efficient and compact feature vector representation, search-space reduction and handling the “semantic gap” problem effectively, without compromising the retrieval performance. Experimental results and comparisons show that the proposed system performs efficiently in the radiographic medical image retrieval field.

Place, publisher, year, edition, pages
Elsevier, 2017. Vol. 139, 209-220 p.
Keyword [en]
Content based medical image retrieval, Fuzzy logic, Multiscale geometric analysis, Pulse couple neural network, Radiographic images, Relevance feedback, Classification (of information), Complex networks, Content based retrieval, Database systems, Diagnosis, Feedback control, Hierarchical systems, Image enhancement, Image retrieval, Information retrieval, Medical applications, Medical imaging, Medical information systems, Neural networks, Semantics, Vector spaces, Image retrieval in medical applications, Multi-scale geometric analysis, Non-sub-sampled contourlet transforms, Subjectivity of human perception, Unsupervised feature selection, Search engines, classifier, comparative effectiveness, compression, data base, experimental model, feedback system, human, nervous system, perception, quantitative study, visual information
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-202209DOI: 10.1016/j.cmpb.2016.10.023ScopusID: 2-s2.0-85007093170OAI: oai:DiVA.org:kth-202209DiVA: diva2:1082976
Note

Correspondence Address: Chowdhury, M.; Machine Intelligence Unit, Indian Statistical InstituteIndia; email: st.manishc@gmail.com. QC 20170320

Available from: 2017-03-20 Created: 2017-03-20 Last updated: 2017-03-20Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Chowdhury, Manish
By organisation
School of Technology and Health (STH)
In the same journal
Computer Methods and Programs in Biomedicine
Medical Engineering

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf