Academic Journal
Image segmentation algorithms
| العنوان: | Image segmentation algorithms |
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| المؤلفون: | Mirzaev Nomaz, Radjabov Sobirjon, Khashimov Akhmad, Noraliev Nurilla, Mirzaeva Gulmira |
| المصدر: | BIO Web of Conferences, Vol 138, p 02013 (2024) |
| بيانات النشر: | EDP Sciences |
| سنة النشر: | 2024 |
| المجموعة: | Directory of Open Access Journals: DOAJ Articles |
| مصطلحات موضوعية: | Microbiology, QR1-502, Physiology, QP1-981, Zoology, QL1-991 |
| الوصف: | This article focuses on the methods of segmentation of kidney images, with the main emphasis on segmentation methods based on neural networks. During this work, we got acquainted with neural network-based algorithms and decided to use the U-Net algorithm for segmentation. A neural network architecture mainly consists of a descending (left) and an expanding (right) part. The structure of U-Net neural network architectures, which are currently widely used for medical images, have been investigated and experimental studies have been carried out on extracting the kidney region in medical images. This paper uses the Unet neural network to segment kidney images and further refines its results using morphological operations. In the next step, the kidney area itself was extracted from the main abdominal image. A dataset used in the Kidney Tumor Segmentation Challenge (KiTS19) was used to train the neural network, where kidney regions were defined in the images. As an example, we took medical images. But in general, it will be possible to extract segments from images of energy objects using the proposed segmentation algorithms. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English French |
| Relation: | https://www.bio-conferences.org/articles/bioconf/pdf/2024/57/bioconf_aquaculture24_02013.pdf; https://doaj.org/toc/2117-4458; https://doaj.org/article/e95606bac6744003898bf84519e2be89 |
| DOI: | 10.1051/bioconf/202413802013 |
| الاتاحة: | https://doi.org/10.1051/bioconf/202413802013 https://doaj.org/article/e95606bac6744003898bf84519e2be89 |
| رقم الانضمام: | edsbas.5CC9C2CA |
| قاعدة البيانات: | BASE |
| DOI: | 10.1051/bioconf/202413802013 |
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