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Journal of Imaging Informatics in Medicine
Erschienen in: Journal of Digital Imaging 4/2020

12.05.2020 | Original Paper

An Efficient Content-Based Image Retrieval System for the Diagnosis of Lung Diseases

verfasst von: Muhammad Kashif, Gulistan Raja, Furqan Shaukat

Erschienen in: Journal of Imaging Informatics in Medicine | Ausgabe 4/2020

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Abstract

The main problem in content-based image retrieval (CBIR) systems is the semantic gap which needs to be reduced for efficient retrieval. The common imaging signs (CISs) which appear in the patient’s lung CT scan play a significant role in the identification of cancerous lung nodules and many other lung diseases. In this paper, we propose a new combination of descriptors for the effective retrieval of these imaging signs. First, we construct a feature database by combining local ternary pattern (LTP), local phase quantization (LPQ), and discrete wavelet transform. Next, joint mutual information (JMI)–based feature selection is deployed to reduce the redundancy and to select an optimal feature set for CISs retrieval. To this end, similarity measurement is performed by combining visual and semantic information in equal proportion to construct a balanced graph and the shortest path is computed for learning contextual similarity to obtain final similarity between each query and database image. The proposed system is evaluated on a publicly available database of lung CT imaging signs (LISS), and results are retrieved based on visual feature similarity comparison and graph-based similarity comparison. The proposed system achieves a mean average precision (MAP) of 60% and 0.48 AUC of precision-recall (P-R) graph using only visual features similarity comparison. These results further improve on graph-based similarity measure with a MAP of 70% and 0.58 AUC which shows the superiority of our proposed scheme.
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Metadaten
Titel
An Efficient Content-Based Image Retrieval System for the Diagnosis of Lung Diseases
verfasst von
Muhammad Kashif
Gulistan Raja
Furqan Shaukat
Publikationsdatum
12.05.2020
Verlag
Springer International Publishing
Erschienen in
Journal of Imaging Informatics in Medicine / Ausgabe 4/2020
Print ISSN: 2948-2925
Elektronische ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-020-00338-w

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