Computer-aided diagnosis systems for osteoporosis detection: a comprehensive survey.
Computer-aided diagnosis (CAD) has revolutionized the field of medical diagnosis. They assist in improving the treatment potentials and intensify the survival frequency by early diagnosing the diseases in an efficient, timely, and cost-effective way. The automatic segmentation has led the radiologis...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 9; pp. 1873 - 1918 |
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| Autores principales: | , |
| Formato: | review Journal Article |
| Publicado: |
Springer Nature
Sep2020
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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=145048069&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145048069 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2020 vid: 58 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 145048069 144442736 145048069 NLM32583141 145048069 10.1007/s11517-020-02171-3 NLM32583141 145048069 ppf: 1873 ppct: 45 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Computer-aided diagnosis systems for osteoporosis detection: a comprehensive survey. aug: au: Wani, Insha Majeed Arora, Sakshi affil: School of Computer Science and Engineering, SMVDU, Katra, J&K, India sug: subj: Diagnosis, Computer Assisted Methods Osteoporosis Image Interpretation, Computer Assisted Methods Diagnosis, Computer Assisted Statistics and Numerical Data Adult Image Interpretation, Computer Assisted Statistics and Numerical Data Absorptiometry, Photon Logic Male Magnetic Resonance Imaging Female Ultrasonography Aged Bone Density Finite Element Analysis Middle Age Tomography, X-Ray Computed Mathematics Biomedical Engineering Osteoporosis Epidemiology Aged, 80 and Over Artificial Intelligence Algorithms Adult: 19-44 years Aged: 65+ years Middle Aged: 45-64 years Aged, 80 & over Male Female ab: Computer-aided diagnosis (CAD) has revolutionized the field of medical diagnosis. They assist in improving the treatment potentials and intensify the survival frequency by early diagnosing the diseases in an efficient, timely, and cost-effective way. The automatic segmentation has led the radiologist to successfully segment the region of interest to improve the diagnosis of diseases from medical images which is not so efficiently possible by manual segmentation. The aim of this paper is to survey the vision-based CAD systems especially focusing on the segmentation techniques for the pathological bone disease known as osteoporosis. Osteoporosis is the state of the bones where the mineral density of bones decreases and they become porous, making the bones easily susceptible to fractures by small injury or a fall. The article covers the image acquisition techniques for acquiring the medical images for osteoporosis diagnosis. The article also discusses the advanced machine learning paradigms employed in segmentation for osteoporosis disease. Other image processing steps in osteoporosis like feature extraction and classification are also briefly described. Finally, the paper gives the future directions to improve the osteoporosis diagnosis and presents the proposed architecture. Graphical abstract. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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