Szczegóły publikacji
Opis bibliograficzny
Using histogram skewness and kurtosis features for detection of white matter hyperintensities in MRI images / Anna Baran, Adam PIÓRKOWSKI // 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. 67–79. — Bibliogr., Abstr. — Publikacja dostępna online od: 2023-09-11
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 150833 |
|---|---|
| Data dodania do BaDAP | 2023-12-16 |
| DOI | 10.1007/978-3-031-38430-1_6 |
| Rok publikacji | 2024 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Czasopismo/seria | Lecture Notes in Networks and Systems |
Abstract
White matter hyperintensities are regions of hyperintensive signal in white brain matter that appear in T2 or FLAIR MRI imaging. They show demyelination, cerebral oedema, tumors, or angiogenesis. Their presence is associated with a number of neurological diseases such as dementia, cognitive impairment, depression, schizophrenia, or even cerebral small vessel disease and multiple sclerosis. WMH areas are thought to appear on MRI images even a few years before the clinical manifestation of certain neurological diseases. Automatic segmentation algorithms can help better understand white matter lesions as part of large-scale studies. It is believed that changes in lesions’ appearance can be monitored over time, thus their correlation with certain diseases can be better understood. This work describes the use of histogram features for the detection of white matter hyperintensities, with a focus on skewness and kurtosis. The first aim of this study is to find features that can be used to identify white matter hyperintensities; then, the authors propose a fully automatic detection algorithm based on a sliding window method to obtain local histograms, and a Support Vector Machine algorithm to perform binary classification. The authors explored the effects of different combinations of characteristics and the relationship between feature values and hyperintensity percentage in a disc-shaped window. The described study is preliminary and contains an initial examination of the effectiveness of using histogram features for the detection of white matter hyperintensities in MRI images.
