«Al-Farg’oniy avlodlari» («The descendants of Al-Fargani»)
Published March 27, 2024 | Version v1
Journal article Open

Tibbiy tasvirlar ichida alohida qiziqish hududlarini (Region of interest–ROI) avtomatik aniqlash va izolyatsiya qilish

Creators

Description

Tibbiy tasvirni segmentlash sog‘liqni saqlash sohasida qaror qabul qilishning aniqligini oshirishda muhim ahamiyatga ega. Bu tibbiy tasvirlarda turli xil tuzilmalar yoki qiziqish hududlari (regions of interest - ROI) identifikatsiyasi va segmentatsiyasini o‘z ichiga oladi. Qiziqish hududlarini aniqlash, turli xil kassaliklarini diagnostikasi, davolashni rejalashtirish va monitoringida muhim ahamiyatga ega. Ushbu maqolada tibbiy tasvirlarda identifikatsiya aniqligining muhimligi, shuningdek aniqlikkka erishish algoritmlarining o‘rni taqdid qilinadi.

Files

_22_305_142-146_Ermatova.pdf

Files (959.5 kB)

Name Size Download all
md5:f1e3f8514b6e5ace8989b64bc0c5a403
959.5 kB Preview Download

Additional details

References

  • Yanhui Guo, Amira S. Ashour. "11 - Neutrosophic sets in dermoscopic medical image segmentation". Neutrosophic Set in Medical Image Analysis 2019, Pages 229-243
  • Jin, S., Yu, S., Peng, J. et al. A novel medical image segmentation approach by using multi-branch segmentation network based on local and global information synchronous learning. Sci Rep 13, 6762 (2023). https://doi.org/10.1038/s41598-023-33357-y
  • Kong Z, Li T, Luo J, Xu S. Automatic Tissue Image Segmentation Based on Image Processing and Deep Learning. J Healthc Eng. 2019 Jan 31;2019:2912458. doi: 10.1155/2019/2912458. PMID: 30838122; PMCID: PMC6374831.
  • Sharma N, Aggarwal LM. Automated medical image segmentation techniques. J Med Phys. 2010 Jan;35(1):3-14. doi: 10.4103/0971-6203.58777. PMID: 20177565; PMCID: PMC2825001.
  • Li, Mengfang, and Yanzhou Zhang. "Medical image analysis using deep learning algorithms." Frontiers in Public Health 11 (2023): 1273253.
  • Vigueras-Guillén, Juan P., et al. "Automatic detection of the region of interest in corneal endothelium images using dense convolutional neural networks." Medical Imaging 2019: Image Processing. Vol. 10949. SPIE, 2019.