Content-Based Image Retrieval System for Pulmonary Nodules Using Optimal Feature Sets and Class Membership-Based Retrieval.
Lung cancer manifests itself in the form of lung nodules, the diagnosis of which is essential to plan the treatment. Automated retrieval of nodule cases will assist the budding radiologists in self-learning and differential diagnosis. This paper presents a content-based image retrieval (CBIR) system...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 3; pp. 362 - 386 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2019
|
| 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=136223486&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136223486 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2019 vid: 32 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136223486 136223486 136223486 10.1007/s10278-018-0136-1 136223486 ppf: 362 ppct: 24 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Content-Based Image Retrieval System for Pulmonary Nodules Using Optimal Feature Sets and Class Membership-Based Retrieval. aug: au: Mehre, Shrikant A. Mukhopadhyay, Sudipta Dhara, Ashis Kumar Garg, Mandeep Kalra, Naveen Khandelwal, Niranjan affil: Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India sug: subj: Picture Archiving and Communication Systems Methods Lung Neoplasms Diagnosis Learning Methods Radiologists Technology, Radiologic Tomography, X-Ray Computed Diagnosis, Computer Assisted Radiographic Image Interpretation, Computer-Assisted Diagnostic Imaging Classification Diagnosis, Differential ab: Lung cancer manifests itself in the form of lung nodules, the diagnosis of which is essential to plan the treatment. Automated retrieval of nodule cases will assist the budding radiologists in self-learning and differential diagnosis. This paper presents a content-based image retrieval (CBIR) system for lung nodules using optimal feature sets and learning to enhance the performance of retrieval. The classifiers with more features suffer from the curse of dimensionality. Like classification schemes, we found that the optimal feature set selected using the minimal-redundancy-maximal-relevance (mRMR) feature selection technique improves the precision performance of simple distance-based retrieval (SDR). The performance of the classifier is always superior to SDR, which leans researchers towards conventional classifier-based retrieval (CCBR). While CCBR improves the average precision and provides 100% precision for correct classification, it fails for misclassification leading to zero retrieval precision. The class membership-based retrieval (CMR) is found to bridge this gap for texture-based retrieval. Here, CMR is proposed for nodule retrieval using shape-, margin-, and texture-based features. It is found again that optimal feature set is important for the classifier used in CMR as well as for the feature set used for retrieval, which may lead to different feature sets. The proposed system is evaluated using two independent databases from two continents: a public database LIDC/IDRI and a private database PGIMER-IITKGP, using three distance metrics, i.e., Canberra, City block, and Euclidean. The proposed CMR-based retrieval system with optimal feature sets performs better than CCBR and SDR with optimal features in terms of average precision. Apart from average precision and standard deviation of precision, the fraction of queries with zero precision retrieval is also measured. pubtype: Academic Journal doctype: diagnostic images equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|