New Vessel Extraction Method by Using Skew Normal Distribution for MRA Images

Vascular-related diseases pose significant public health challenges and are a leading cause of mortality and disability. Understanding the complex structure of the vascular system and its processes is crucial for addressing these issues. Recent advancements in medical imaging technology have enabled...

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Bibliographic Details
Published in:	Stats (Basel, Switzerland) Vol. 7; no. 1; pp. 203 - 219
Main Authors:	Bahrami, Tohid, Khamnei, Hossein Jabbari, Lakestani, Mehrdad, Kibria, B. M. Golam
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01-02-2024
Subjects:	Algorithms Asymmetry Blood circulation disorders Blood vessels Deep learning Distribution EM algorithm Flow velocity Hypothesis testing Image processing Magnetic resonance angiography Medical imaging Methods MRA images Normal distribution Parameter estimation skew normal distribution Skewness vessel extraction Iran
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Summary:	Vascular-related diseases pose significant public health challenges and are a leading cause of mortality and disability. Understanding the complex structure of the vascular system and its processes is crucial for addressing these issues. Recent advancements in medical imaging technology have enabled the generation of high-resolution 3D images of vascular structures, leading to a diverse array of methods for vascular extraction. While previous research has often assumed a normal distribution of image data, this paper introduces a novel vessel extraction method that utilizes the skew normal distribution for more accurate probability distribution modeling. The proposed method begins with a preprocessing step to enhance vessel structures and reduce noise in Magnetic Resonance Angiography (MRA) images. The skew normal distribution, known for its ability to model skewed data, is then employed to characterize the intensity distribution of vessels. By estimating the parameters of the skew normal distribution using the Expectation-Maximization (EM) algorithm, the method effectively separates vessel pixels from the background and non-vessel regions. To extract vessels, a thresholding technique is applied based on the estimated skew normal distribution parameters. This segmentation process enables accurate vessel extraction, particularly in detecting thin vessels and enhancing the delineation of vascular edges with low contrast. Experimental evaluations on a diverse set of MRA images demonstrate the superior performance of the proposed method compared to previous approaches in terms of accuracy and computational efficiency. The presented vessel extraction method holds promise for improving the diagnosis and treatment of vascular-related diseases. By leveraging the skew normal distribution, it provides accurate and efficient vessel segmentation, contributing to the advancement of vascular imaging in the field of medical image analysis.
ISSN:	2571-905X 2571-905X
DOI:	10.3390/stats7010013