An Efficient Content-Based Image Retrieval System for the Diagnosis of Lung Diseases.
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 lu...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 4; pp. 971 - 988 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=146122218&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146122218 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2020 vid: 33 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146122218 144191823 10.1007/s10278-020-00338-w 146122218 ppf: 971 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Efficient Content-Based Image Retrieval System for the Diagnosis of Lung Diseases. aug: au: Kashif, Muhammad Raja, Gulistan Shaukat, Furqan affil: Faculty of Electronics and Electrical Engineering, Univeristy of Engineering & Technology, Taxila, Pakistan sug: subj: Lung Diseases Diagnosis Image Retrieval Systems ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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