Cerebrovascular Segmentation Based on Hidden Markov Model in Phase-Contrast Angiography Images

Background: Phase-Contrast Angiography (PCA) is an acceptable magnetic resonance imaging method for cerebrovascular diseases diagnosis. However, it is an important and great challenge to accurately extract cerebrovascular structures from PCA images because of the complex vascular structures and large amount of noise. To accomplish this task, this work proposes a cerebrovascular segmentation algorithm based on Local Binary Fitting (LBF) and Hidden Markov Model (HMM), which can accurately extraction features from PCA data. Results: Dice Similarity Coefficient (DSC), False Positive Score (FPN), and False Negative Score (FTN) are defined as metrics to assess this algorithm. Results show this method obtain higher accuracy (74.58%, 4.93%, 24.48%) than compared methods. Conclusion: Based on quantitative results, it appears that the proposed method has a higher accuracy rate compared to other methods. Due to no human correction and has no training process, it performs well on small datasets. Thus, this algorithm can accord with clinical requirements.