MRI Image Segmentation Model with Support Vector Machine Algorithm in Diagnosis of Solitary Pulmonary Nodule
This study focused on the application value of MRI images processed by a Support Vector Machine (SVM) algorithm-based model in diagnosis of benign and malignant solitary pulmonary nodule (SPN). The SVM algorithm was constrained by a self-paced regularization item and gradient value to establish the...
Uloženo v:
| Vydáno v: | Contrast media and molecular imaging Ročník 2021; s. 9668836 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
England
Hindawi
2021
|
| Témata: | |
| ISSN: | 1555-4309, 1555-4317, 1555-4317 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | This study focused on the application value of MRI images processed by a Support Vector Machine (SVM) algorithm-based model in diagnosis of benign and malignant solitary pulmonary nodule (SPN). The SVM algorithm was constrained by a self-paced regularization item and gradient value to establish the MRI image segmentation model (SVM-L) for lung. Its performance was compared factoring into the Dice index (DI), sensitivity (SE), specificity (SP), and Mean Square Error (MSE). 28 SPN patients who underwent the parallel MRI examination were selected as research subjects and were divided into the benign group (11 patients) and malignant group (17 patients) according to different plans for diagnosis and treatment. The apparent diffusion coefficient (ADC) at different b values was analyzed, and the steepest slope (SS) and washout ratio (WR) values in the two groups were calculated. The result showed that the MSE, DI, SE, SP values, and operation time of the SVM-L model were (0.41 ± 0.02), (0.84 ± 0.13), (0.89 ± 0.04), (0.993 ± 0.004), and (30.69 ± 2.60)s, respectively, apparently superior to those of the other algorithms, but there were no statistic differences (P>0.05) in the WR value between the two groups of patients. The SS values of the time-signal curve in the benign and malignant groups were (2.52 ± 0.69) %/s and (3.34 ± 00.41) %/s, respectively. Obviously, the SS value of the benign group was significantly lower than that of the malignant group (P<0.01). The ADC value with different b values in the benign group was significantly lower than that of the malignant group (P<0.01). It suggested that the SVM-L model significantly improved the quality of lung MRI images and increased the accuracy to differentiate benign and malignant SPN, providing reference for the diagnosis and treatment of SPN patients. |
|---|---|
| AbstractList | This study focused on the application value of MRI images processed by a Support Vector Machine (SVM) algorithm-based model in diagnosis of benign and malignant solitary pulmonary nodule (SPN). The SVM algorithm was constrained by a self-paced regularization item and gradient value to establish the MRI image segmentation model (SVM-L) for lung. Its performance was compared factoring into the Dice index (DI), sensitivity (SE), specificity (SP), and Mean Square Error (MSE). 28 SPN patients who underwent the parallel MRI examination were selected as research subjects and were divided into the benign group (11 patients) and malignant group (17 patients) according to different plans for diagnosis and treatment. The apparent diffusion coefficient (ADC) at different b values was analyzed, and the steepest slope (SS) and washout ratio (WR) values in the two groups were calculated. The result showed that the MSE, DI, SE, SP values, and operation time of the SVM-L model were (0.41 ± 0.02), (0.84 ± 0.13), (0.89 ± 0.04), (0.993 ± 0.004), and (30.69 ± 2.60)s, respectively, apparently superior to those of the other algorithms, but there were no statistic differences (P > 0.05) in the WR value between the two groups of patients. The SS values of the time-signal curve in the benign and malignant groups were (2.52 ± 0.69) %/s and (3.34 ± 00.41) %/s, respectively. Obviously, the SS value of the benign group was significantly lower than that of the malignant group (P < 0.01). The ADC value with different b values in the benign group was significantly lower than that of the malignant group (P < 0.01). It suggested that the SVM-L model significantly improved the quality of lung MRI images and increased the accuracy to differentiate benign and malignant SPN, providing reference for the diagnosis and treatment of SPN patients.This study focused on the application value of MRI images processed by a Support Vector Machine (SVM) algorithm-based model in diagnosis of benign and malignant solitary pulmonary nodule (SPN). The SVM algorithm was constrained by a self-paced regularization item and gradient value to establish the MRI image segmentation model (SVM-L) for lung. Its performance was compared factoring into the Dice index (DI), sensitivity (SE), specificity (SP), and Mean Square Error (MSE). 28 SPN patients who underwent the parallel MRI examination were selected as research subjects and were divided into the benign group (11 patients) and malignant group (17 patients) according to different plans for diagnosis and treatment. The apparent diffusion coefficient (ADC) at different b values was analyzed, and the steepest slope (SS) and washout ratio (WR) values in the two groups were calculated. The result showed that the MSE, DI, SE, SP values, and operation time of the SVM-L model were (0.41 ± 0.02), (0.84 ± 0.13), (0.89 ± 0.04), (0.993 ± 0.004), and (30.69 ± 2.60)s, respectively, apparently superior to those of the other algorithms, but there were no statistic differences (P > 0.05) in the WR value between the two groups of patients. The SS values of the time-signal curve in the benign and malignant groups were (2.52 ± 0.69) %/s and (3.34 ± 00.41) %/s, respectively. Obviously, the SS value of the benign group was significantly lower than that of the malignant group (P < 0.01). The ADC value with different b values in the benign group was significantly lower than that of the malignant group (P < 0.01). It suggested that the SVM-L model significantly improved the quality of lung MRI images and increased the accuracy to differentiate benign and malignant SPN, providing reference for the diagnosis and treatment of SPN patients. This study focused on the application value of MRI images processed by a Support Vector Machine (SVM) algorithm-based model in diagnosis of benign and malignant solitary pulmonary nodule (SPN). The SVM algorithm was constrained by a self-paced regularization item and gradient value to establish the MRI image segmentation model (SVM-L) for lung. Its performance was compared factoring into the Dice index (DI), sensitivity (SE), specificity (SP), and Mean Square Error (MSE). 28 SPN patients who underwent the parallel MRI examination were selected as research subjects and were divided into the benign group (11 patients) and malignant group (17 patients) according to different plans for diagnosis and treatment. The apparent diffusion coefficient (ADC) at different b values was analyzed, and the steepest slope (SS) and washout ratio (WR) values in the two groups were calculated. The result showed that the MSE, DI, SE, SP values, and operation time of the SVM-L model were (0.41 ± 0.02), (0.84 ± 0.13), (0.89 ± 0.04), (0.993 ± 0.004), and (30.69 ± 2.60)s, respectively, apparently superior to those of the other algorithms, but there were no statistic differences (P>0.05) in the WR value between the two groups of patients. The SS values of the time-signal curve in the benign and malignant groups were (2.52 ± 0.69) %/s and (3.34 ± 00.41) %/s, respectively. Obviously, the SS value of the benign group was significantly lower than that of the malignant group (P<0.01). The ADC value with different b values in the benign group was significantly lower than that of the malignant group (P<0.01). It suggested that the SVM-L model significantly improved the quality of lung MRI images and increased the accuracy to differentiate benign and malignant SPN, providing reference for the diagnosis and treatment of SPN patients. This study focused on the application value of MRI images processed by a Support Vector Machine (SVM) algorithm-based model in diagnosis of benign and malignant solitary pulmonary nodule (SPN). The SVM algorithm was constrained by a self-paced regularization item and gradient value to establish the MRI image segmentation model (SVM-L) for lung. Its performance was compared factoring into the Dice index (DI), sensitivity (SE), specificity (SP), and Mean Square Error (MSE). 28 SPN patients who underwent the parallel MRI examination were selected as research subjects and were divided into the benign group (11 patients) and malignant group (17 patients) according to different plans for diagnosis and treatment. The apparent diffusion coefficient (ADC) at different values was analyzed, and the steepest slope (SS) and washout ratio (WR) values in the two groups were calculated. The result showed that the MSE, DI, SE, SP values, and operation time of the SVM-L model were (0.41 ± 0.02), (0.84 ± 0.13), (0.89 ± 0.04), (0.993 ± 0.004), and (30.69 ± 2.60)s, respectively, apparently superior to those of the other algorithms, but there were no statistic differences ( > 0.05) in the WR value between the two groups of patients. The SS values of the time-signal curve in the benign and malignant groups were (2.52 ± 0.69) %/s and (3.34 ± 00.41) %/s, respectively. Obviously, the SS value of the benign group was significantly lower than that of the malignant group ( < 0.01). The ADC value with different values in the benign group was significantly lower than that of the malignant group ( < 0.01). It suggested that the SVM-L model significantly improved the quality of lung MRI images and increased the accuracy to differentiate benign and malignant SPN, providing reference for the diagnosis and treatment of SPN patients. |
| Author | Zhang, Meihua Wang, Lingang Zhu, Hanlin Feng, Bo Zheng, Yanli |
| Author_xml | – sequence: 1 givenname: Bo orcidid: 0000-0003-0935-6604 surname: Feng fullname: Feng, Bo organization: Department of RadiologyHangzhou Ninth People’s HospitalNo. 98 Yilong RoadYipeng StreetQiantang DistrictHangzhou 311225Zhejiang ProvinceChina – sequence: 2 givenname: Meihua orcidid: 0000-0002-1182-7092 surname: Zhang fullname: Zhang, Meihua organization: Department of RadiologyHangzhou Ninth People’s HospitalNo. 98 Yilong RoadYipeng StreetQiantang DistrictHangzhou 311225Zhejiang ProvinceChina – sequence: 3 givenname: Hanlin orcidid: 0000-0003-1089-0943 surname: Zhu fullname: Zhu, Hanlin organization: Department of RadiologyHangzhou Ninth People’s HospitalNo. 98 Yilong RoadYipeng StreetQiantang DistrictHangzhou 311225Zhejiang ProvinceChina – sequence: 4 givenname: Lingang orcidid: 0000-0001-9078-5750 surname: Wang fullname: Wang, Lingang organization: Department of RadiologyHangzhou Ninth People’s HospitalNo. 98 Yilong RoadYipeng StreetQiantang DistrictHangzhou 311225Zhejiang ProvinceChina – sequence: 5 givenname: Yanli orcidid: 0000-0002-2676-0695 surname: Zheng fullname: Zheng, Yanli organization: Department of RadiologyHangzhou Ninth People’s HospitalNo. 98 Yilong RoadYipeng StreetQiantang DistrictHangzhou 311225Zhejiang ProvinceChina |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34377105$$D View this record in MEDLINE/PubMed |
| BookMark | eNo9kEtPwzAMgCM0xB5w44xyREJlSZO0zXEar0kMEAOuVdq6XVCajKbVxL-n08YutmV_suxvjAbWWUDokpJbSoWYhiSkUxlFScKiEzTqWyLgjMaDY03kEI29_yaEcybZGRoyzuKYEjFCZvm-wItaVYBXUNVgW9VqZ_HSFWDwVrdrvOo2G9e0-Avy1jV4qfK1toBnpnJNP6-xtvhOq8o6rz12JV45o1vV_OK3ztTO7qoXV3QGztFpqYyHi0OeoM-H-4_5U_D8-riYz56DnIekDTIJcVFSkWVZUWY0B5knICXnlDKlGC9V3AMQ8ixSRc5iwaIwL5JM9kHEQrEJut7v3TTupwPfprX2ORijLLjOp6GISCgTKZIevTqgXVZDkW4aXfcHp_-GeuBmD_RPF2qrjwQl6c5_uvOfHvyzP1eceAA |
| CitedBy_id | crossref_primary_10_5812_mejrh_158082 crossref_primary_10_1155_2021_7830909 crossref_primary_10_1109_ACCESS_2024_3447697 crossref_primary_10_1080_10447318_2023_2175494 |
| ContentType | Journal Article |
| Copyright | Copyright © 2021 Bo Feng et al. |
| Copyright_xml | – notice: Copyright © 2021 Bo Feng et al. |
| DBID | RHU RHW RHX CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1155/2021/9668836 |
| DatabaseName | Hindawi Publishing Complete Hindawi Publishing Subscription Journals Hindawi Publishing Open Access Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: RHX name: Hindawi Publishing Open Access url: http://www.hindawi.com/journals/ sourceTypes: Publisher – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1555-4317 |
| Editor | Teekaraman, Yuvaraja |
| Editor_xml | – sequence: 1 givenname: Yuvaraja surname: Teekaraman fullname: Teekaraman, Yuvaraja |
| ExternalDocumentID | 34377105 10_1155_2021_9668836 |
| Genre | Journal Article |
| GroupedDBID | --- .3N .GA 05W 0R~ 1L6 1OC 33P 3SF 3V. 3WU 4.4 50Y 50Z 52M 52O 52T 52U 52V 52W 53G 5GY 702 7PT 7X7 7XC 8-0 8-1 8-3 8-4 8-5 8FE 8FH 8FI 8UM 930 A01 A03 AAESR AAFWJ AAJEY AAONW ABIJN ABPVW ADBBV ADIZJ AENEX AEUQT AFBPY AFKRA ALAGY ALMA_UNASSIGNED_HOLDINGS AMBMR AOIJS ATCPS ATUGU AZBYB AZVAB BAFTC BCNDV BENPR BHBCM BHPHI BPHCQ BROTX BRXPI BVXVI BYOGL CS3 D-6 D-7 D-E D-F DPXWK DU5 EBD EBS EMOBN F00 F01 F04 F21 F5P FYUFA G-S G.N GODZA GROUPED_DOAJ H.X HBH HCIFZ HHY HHZ HYE HZ~ IAO IHR ITC LAW LITHE LP6 LP7 M1P MK4 MY~ N04 N05 NF~ O66 O9- OIG OK1 P2P P2W P2X P2Z P4B P4D PATMY PQQKQ PROAC PYCSY Q.N QB0 R.K RHU RHW RHX RPM RWI RX1 RYL SUPJJ SV3 UB1 UKHRP W8V W99 WBKPD WIH WIJ WVDHM XV2 ~IA ~WT .Y3 24P 31~ 88E 8FJ AAEVG AAHHS AANHP AAZKR ABUWG ACBWZ ACCFJ ACCMX ACRPL ACXQS ACYXJ ADNMO ADZOD AEEZP AEIMD AEQDE AFTUV AGFTA AIWBW AJBDE ALIPV ASPBG AVWKF AZFZN BDRZF CCPQU CGR CUY CVF ECM EIF EJD FEDTE H13 HF~ HMCUK HVGLF LH4 LW6 NPM PGMZT PSQYO WRC WYUIH 7X8 AAMMB AEFGJ AGXDD AIDQK AIDYY |
| ID | FETCH-LOGICAL-c420t-b9e7df15bbbdfb1ce9c8e9944113aa34fa7e7de24b6adc375362cd8b9cd8575a3 |
| IEDL.DBID | RHX |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000683123700002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1555-4309 1555-4317 |
| IngestDate | Wed Oct 01 14:51:31 EDT 2025 Wed Feb 19 02:26:54 EST 2025 Sun Jun 02 18:51:55 EDT 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2021 Bo Feng et al. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c420t-b9e7df15bbbdfb1ce9c8e9944113aa34fa7e7de24b6adc375362cd8b9cd8575a3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-1182-7092 0000-0003-0935-6604 0000-0001-9078-5750 0000-0002-2676-0695 0000-0003-1089-0943 |
| OpenAccessLink | https://dx.doi.org/10.1155/2021/9668836 |
| PMID | 34377105 |
| PQID | 2560298958 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_2560298958 pubmed_primary_34377105 hindawi_primary_10_1155_2021_9668836 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-00-00 |
| PublicationDateYYYYMMDD | 2021-01-01 |
| PublicationDate_xml | – year: 2021 text: 2021-00-00 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | Contrast media and molecular imaging |
| PublicationTitleAlternate | Contrast Media Mol Imaging |
| PublicationYear | 2021 |
| Publisher | Hindawi |
| Publisher_xml | – name: Hindawi |
| SSID | ssj0044393 |
| Score | 2.2476478 |
| Snippet | This study focused on the application value of MRI images processed by a Support Vector Machine (SVM) algorithm-based model in diagnosis of benign and... |
| SourceID | proquest pubmed hindawi |
| SourceType | Aggregation Database Index Database Publisher |
| StartPage | 9668836 |
| SubjectTerms | Adult Aged Algorithms Diffusion Magnetic Resonance Imaging - methods Female Follow-Up Studies Humans Image Processing, Computer-Assisted - methods Lung Neoplasms - pathology Lung Neoplasms - surgery Male Middle Aged Prognosis Solitary Pulmonary Nodule - diagnosis Support Vector Machine Young Adult |
| Title | MRI Image Segmentation Model with Support Vector Machine Algorithm in Diagnosis of Solitary Pulmonary Nodule |
| URI | https://dx.doi.org/10.1155/2021/9668836 https://www.ncbi.nlm.nih.gov/pubmed/34377105 https://www.proquest.com/docview/2560298958 |
| Volume | 2021 |
| WOSCitedRecordID | wos000683123700002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LS8NAEB5sEfEivq2PskKvwTx2m91jUYs9tJRWJbewSXbbQppI0yr-e2eT1IMieMiSQJKF2dl83zwyA9BBSLepQ5UlJHMtigBoCVwMC8FVSpdpnzmybDbhj0Y8CMS4LpJU_A7hI9oZ89y5Q1bOuddtQIMzk7k1eQq2H1yKmFrm0TPGLOrZYpvf_uNZZLhzY-d-LP5mkyWq9A_hoKaDpFet3xHsqOwY9oZ1wPsE0uFkQAZL3PRkqmbL-kehjJgWZikxTlRi-nIihyavpf-dDMvsSEV66SxHw3--JIuMPFQJdYuC5JpMTcqbXH2S8SZFJTRnozzZpOoUXvqPz_dPVt0gwYqpa6-tSCg_0Q6LoijRkRMrEXMlBDIcx5PSo1r6eINyadSVSeyhZdJ144RHAgekadI7g2aWZ-oCiMbNKLmNR4LyVJpLnVAf6YmtmBCxakGnFl74VpXBCEvzgbHQyDisZdyC261kQ9RTE3yQmco3RWiolan2zngLziuRf7_Jo56PTIdd_m-SK9g3l5Uj5Bqa69VG3cBu_L5eFKs2NPyA4zgaD9ulnnwBLSO1Dg |
| linkProvider | Hindawi Publishing |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=MRI+Image+Segmentation+Model+with+Support+Vector+Machine+Algorithm+in+Diagnosis+of+Solitary+Pulmonary+Nodule&rft.jtitle=Contrast+media+and+molecular+imaging&rft.au=Feng%2C+Bo&rft.au=Zhang%2C+Meihua&rft.au=Zhu%2C+Hanlin&rft.au=Wang%2C+Lingang&rft.date=2021&rft.pub=Hindawi&rft.issn=1555-4309&rft.eissn=1555-4317&rft.volume=2021&rft_id=info:doi/10.1155%2F2021%2F9668836&rft.externalDocID=10_1155_2021_9668836 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1555-4309&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1555-4309&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1555-4309&client=summon |