Szczegóły publikacji
Opis bibliograficzny
Using local normalization and local thresholding in the detection of small objects in MR brain images / Patrycja KWIEK, Elżbieta POCIASK // W: The latest developments and challenges in biomedical engineering : proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical Engineering : Lodz, Poland, September 27–29, 2023 / eds. Paweł Strumiłło, [et al.]. — Cham : Springer Nature, cop. 2024. — (Lecture Notes in Networks and Systems ; ISSN 2367-3370 ; LNNS 746). — ISBN: 978-3-031-38429-5; e-ISBN: 978-3-031-38430-1. — S. 55–65. — Bibliogr., Abstr. — Publikacja dostępna online od: 2023-09-11
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 150832 |
|---|---|
| Data dodania do BaDAP | 2023-12-16 |
| DOI | 10.1007/978-3-031-38430-1_5 |
| Rok publikacji | 2024 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Czasopismo/seria | Lecture Notes in Networks and Systems |
Abstract
A lot of time and effort has been put into finding an automatic segmentation system which could detect the region and position of white matter hypersensitivities in MRI brain scans. At the cellular level, changes in the white matter of the brain can be understood as a loss of myelin around axons. These changes might be detected by MRI due to local changes in water content. By establishing a method which could detect such irregularities, it would be possible to help diagnose some serious autoimmune diseases. In this work, a wide variety of local thresholding algorithms that are applied after local normalization is evaluated in terms of how accurately they segment these hypersensitivities. With the use of ImageJ, the following algorithms were tested: Bernsen, Contrast, Mean, Median, MidGrey, Otsu, Sauvola and SDA. The most accurate segmentation results are presented for all algorithms. For most of the ALT algorithms, the use of local normalization increased the achieved Dice value by as much as 100%. Contrary to what might be expected, the best results were achieved for various values of the sigma1 and sigma2 parameters of local normalization, both for single images and for a group of algorithms for a given image, thus it is difficult to apply the presented solutions to other examples.
