Segmenting Small Blood Vessels in MRA/MRI: A Machine Learning Approach with Bilateral Filtering Integration

The adoption of artificial intelligence (AI) in various sectors, including healthcare, has gained significant popularity due to its potential to improve services. In the medical field, misdiagnosis has been a major problem, leading to increased mortality rates. Accurate diagnosis is crucial for effective treatment and management of diseases. This research aims to develop a machine-learning model for segmenting small blood vessels in magnetic resonance angiography (MRA) and magnetic resonance imaging (MRI) datasets using bilateral filtering. The research identifies the limitations of existing machine learning models in blood vessel segmentation, particularly the loss of important edge information due to convolutions that blur images. To address this issue, a non-linear bilateral filter is introduced to enhance the segmentation of blood vessels in MRI images. The proposed framework aims to improve the accuracy of the segmentation algorithm by reducing image blurring and noise through bilateral filtering. The objectives of this research include training and testing a machine-learning prototype using bilateral filtering, exploring the weaknesses of existing models in blood vessel segmentation, and developing a machine-learning model specifically designed for segmenting small blood vessels using bilateral filtering. Various studies have proposed machine learning algorithms, such as convolutional neural networks, for blood vessel segmentation. The review emphasizes the importance of bilateral filtering in improving classification accuracy by reducing image blurring. In conclusion, this research aims to contribute to the field of medical image analysis by developing a framework that utilizes bilateral filtering to enhance the segmentation of small blood vessels in MRA and MRI datasets. The proposed machine learning model has the potential to improve the accuracy of blood vessel segmentation, enabling more accurate diagnoses and reducing misdiagnosis-related mortality rates.