Skip to main content

2023 | OriginalPaper | Buchkapitel

Whole Tumor Area Estimation in Incremental Brain MRI Using Dilation and Erosion-Based Binary Morphing

verfasst von : Orcan Alpar, Ondrej Krejcar

Erschienen in: Bioinformatics and Biomedical Engineering

Verlag: Springer Nature Switzerland

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Magnetic resonance imaging (MRI) technology is rapidly advancing and three-dimensional (3D) scanners started to play an important role on diagnosis. However, not every medical center has access to 3D magnetic resonance imaging (MRI) devices; therefore, it is safe to state that the majority of MRI scans are still two-dimensional. According to the setup values adjusted before any scan, there might be consistent gaps between the MRI slices, especially when the increment value exceeds the thickness. The gap causes miscalculation of the lesion volumes and misjudgments when the lesions are reconstructed in three-dimensional space due to excessive interpolation. Therefore, in this paper, we present the details of three types of conventional morphing methods, one dilation-based and two erosion-based, and compare them to figure out which one provides better solution for filling up the gaps in incremental brain MRI. Among three types of morphing methods, the highest average dice score coefficient (DSC) is calculated as %91.95, which is obtained by the multiplicative dilation morphing method for HG/0004 set of BraTS 2012.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat van Kreveld, M., Miltzow, T., Ophelders, T., Sonke, W., Vermeulen, J.L.: Between shapes, using the Hausdorff distance. Comput. Geom. 100, 101817 (2022)CrossRef van Kreveld, M., Miltzow, T., Ophelders, T., Sonke, W., Vermeulen, J.L.: Between shapes, using the Hausdorff distance. Comput. Geom. 100, 101817 (2022)CrossRef
2.
Zurück zum Zitat Bouts, Q.W., Kostitsyna, I., van Kreveld, M., Meulemans, W., Sonke, W., Verbeek, K.: Mapping polygons to the grid with small Hausdorff and Fréchet distance. In: 24th Annual European Symposium on Algorithms (ESA 2016), pp. 22:1–22:16 (2016) Bouts, Q.W., Kostitsyna, I., van Kreveld, M., Meulemans, W., Sonke, W., Verbeek, K.: Mapping polygons to the grid with small Hausdorff and Fréchet distance. In: 24th Annual European Symposium on Algorithms (ESA 2016), pp. 22:1–22:16 (2016)
3.
Zurück zum Zitat Kistler, M., Bonaretti, S., Pfahrer, M., Niklaus, R., Büchler, P.: The virtual skeleton database: an open access repository for biomedical research and collaboration. J. Med. Internet Res. 15(11), e245 (2013)CrossRefPubMedPubMedCentral Kistler, M., Bonaretti, S., Pfahrer, M., Niklaus, R., Büchler, P.: The virtual skeleton database: an open access repository for biomedical research and collaboration. J. Med. Internet Res. 15(11), e245 (2013)CrossRefPubMedPubMedCentral
4.
Zurück zum Zitat Menze, B.H.,et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014) Menze, B.H.,et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2014)
5.
Zurück zum Zitat Cheng, Q., Sun, P., Yang, C., Yang, Y., Liu, P.X.: A morphing-Based 3D point cloud reconstruction framework for medical image processing. Comput. Meth. Program. Biomed. 193, 105495 (2020)CrossRef Cheng, Q., Sun, P., Yang, C., Yang, Y., Liu, P.X.: A morphing-Based 3D point cloud reconstruction framework for medical image processing. Comput. Meth. Program. Biomed. 193, 105495 (2020)CrossRef
6.
Zurück zum Zitat Chavez, T., Bowman, T., Wu, J., Bailey, K., El-Shenawee, M.: Assessment of terahertz imaging for excised breast cancer tumors with image morphing. J. Infrared Millimeter Terahertz Waves 39(12), 1283–1302 (2018)CrossRef Chavez, T., Bowman, T., Wu, J., Bailey, K., El-Shenawee, M.: Assessment of terahertz imaging for excised breast cancer tumors with image morphing. J. Infrared Millimeter Terahertz Waves 39(12), 1283–1302 (2018)CrossRef
8.
Zurück zum Zitat Alpar, O., Dolezal, R., Ryska, P., Krejcar, O.: Nakagami-Fuzzy imaging framework for precise lesion segmentation in MRI. Pattern Recogn. 128, 108675 (2022)CrossRef Alpar, O., Dolezal, R., Ryska, P., Krejcar, O.: Nakagami-Fuzzy imaging framework for precise lesion segmentation in MRI. Pattern Recogn. 128, 108675 (2022)CrossRef
9.
Zurück zum Zitat Ayadi, W., Elhamzi, W., Charfi, I., Atri, M.: Deep CNN for brain tumor classification. Neural Process. Lett. 53(1), 671–700 (2021)CrossRef Ayadi, W., Elhamzi, W., Charfi, I., Atri, M.: Deep CNN for brain tumor classification. Neural Process. Lett. 53(1), 671–700 (2021)CrossRef
11.
Zurück zum Zitat Alpar, O.: A mathematical fuzzy fusion framework for whole tumor segmentation in multimodal MRI using Nakagami imaging. Expert Syst. Appl. 216, 119462 (2023)CrossRef Alpar, O.: A mathematical fuzzy fusion framework for whole tumor segmentation in multimodal MRI using Nakagami imaging. Expert Syst. Appl. 216, 119462 (2023)CrossRef
12.
Zurück zum Zitat Singh, V.K., et al.: Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network. Expert Syst. Appl. 139, 112855 (2020)CrossRef Singh, V.K., et al.: Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network. Expert Syst. Appl. 139, 112855 (2020)CrossRef
13.
Zurück zum Zitat Alpar, O.: Nakagami imaging with related distributions for advanced thermogram pseudocolorization. J. Therm. Biol 93, 102704 (2020)CrossRefPubMed Alpar, O.: Nakagami imaging with related distributions for advanced thermogram pseudocolorization. J. Therm. Biol 93, 102704 (2020)CrossRefPubMed
14.
Zurück zum Zitat Pramanik, S., Banik, D., Bhattacharjee, D., Nasipuri, M., Bhowmik, M.K., Majumdar, G.: Suspicious-region segmentation from breast thermogram using DLPE-based level set method. IEEE Trans. Med. Imaging 38(2), 572–584 (2018)CrossRefPubMed Pramanik, S., Banik, D., Bhattacharjee, D., Nasipuri, M., Bhowmik, M.K., Majumdar, G.: Suspicious-region segmentation from breast thermogram using DLPE-based level set method. IEEE Trans. Med. Imaging 38(2), 572–584 (2018)CrossRefPubMed
15.
Zurück zum Zitat Kumar, V., et al.: Automated and real-time segmentation of suspicious breast masses using convolutional neural network. PLoS ONE 13(5), e0195816 (2018)CrossRefPubMedPubMedCentral Kumar, V., et al.: Automated and real-time segmentation of suspicious breast masses using convolutional neural network. PLoS ONE 13(5), e0195816 (2018)CrossRefPubMedPubMedCentral
16.
Zurück zum Zitat Nair, T., Precup, D., Arnold, D.L., Arbel, T.: Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation. Med. Image Anal. 59, 101557 (2020)CrossRefPubMed Nair, T., Precup, D., Arnold, D.L., Arbel, T.: Exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation. Med. Image Anal. 59, 101557 (2020)CrossRefPubMed
17.
Zurück zum Zitat Alpar, O., Krejcar, O., Dolezal, R.: Distribution-based imaging for multiple sclerosis lesion segmentation using specialized fuzzy 2-means powered by Nakagami transmutations. Appl. Soft Comput. 108, 107481 (2021)CrossRef Alpar, O., Krejcar, O., Dolezal, R.: Distribution-based imaging for multiple sclerosis lesion segmentation using specialized fuzzy 2-means powered by Nakagami transmutations. Appl. Soft Comput. 108, 107481 (2021)CrossRef
18.
Zurück zum Zitat Aslani, S., et al.: Multi-branch convolutional neural network for multiple sclerosis lesion segmentation. Neuroimage 196, 1–15 (2019)CrossRefPubMed Aslani, S., et al.: Multi-branch convolutional neural network for multiple sclerosis lesion segmentation. Neuroimage 196, 1–15 (2019)CrossRefPubMed
19.
Zurück zum Zitat Billast, M., Meyer, M.I., Sima, D.M., Robben, D.: Improved inter-scanner MS lesion segmentation by adversarial training on longitudinal data. In: International MICCAI Brainlesion Workshop (2019) Billast, M., Meyer, M.I., Sima, D.M., Robben, D.: Improved inter-scanner MS lesion segmentation by adversarial training on longitudinal data. In: International MICCAI Brainlesion Workshop (2019)
21.
Zurück zum Zitat Tan, T.Y., Zhang, L., Lim, C.P.: Adaptive melanoma diagnosis using evolving clustering, ensemble and deep neural networks. Knowl. Based Syst. 187, 104807 (2020)CrossRef Tan, T.Y., Zhang, L., Lim, C.P.: Adaptive melanoma diagnosis using evolving clustering, ensemble and deep neural networks. Knowl. Based Syst. 187, 104807 (2020)CrossRef
22.
Zurück zum Zitat Corbat, L., Nauval, M., Henriet, J., Lapayre, J.C.: A fusion method based on deep learning and case-based reasoning which improves the resulting medical image segmentations.. Expert Syst. Appl. 113200 (2020) Corbat, L., Nauval, M., Henriet, J., Lapayre, J.C.: A fusion method based on deep learning and case-based reasoning which improves the resulting medical image segmentations.. Expert Syst. Appl. 113200 (2020)
23.
Zurück zum Zitat Song, L.I., Geoffrey, K.F., Kaijian, H.E.: Bottleneck feature supervised U-Net for pixel-wise liver and tumor segmentation. Expert Syst. Appl. 145, 113131 (2020)CrossRef Song, L.I., Geoffrey, K.F., Kaijian, H.E.: Bottleneck feature supervised U-Net for pixel-wise liver and tumor segmentation. Expert Syst. Appl. 145, 113131 (2020)CrossRef
Metadaten
Titel
Whole Tumor Area Estimation in Incremental Brain MRI Using Dilation and Erosion-Based Binary Morphing
verfasst von
Orcan Alpar
Ondrej Krejcar
Copyright-Jahr
2023
DOI
https://doi.org/10.1007/978-3-031-34953-9_10

Premium Partner