Tsallis-Entropy Segmentation through MRF and Alzheimer anatomic reference for Brain Magnetic Resonance Parcellation

Azimbagirad, Mehran; Simozo, Fabrício Henrique; Filho, Antônio Castelo; Murta, Leonardo

doi:10.1016/j.mri.2019.11.002

Cited by 6 publications

(3 citation statements)

References 39 publications

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“…Table 2 shows the accuracy comparison summary of the proposed method versus three other methods, which are: Method A based on [7], Method B based on [8] and Method C based on [9]. Individual tissue is listed with the average and standard deviation of DC measurements.…”

Section: Methodsmentioning

confidence: 99%

“…For instance, Guerrout et al [7] described the hidden Markov random field theory and Broyden-Fletcher-Goldfarb-Shanno for tissue labeling. Azimbagirad et al [8] proposed a method based on intensity histogram dedicated to Alzheimer's diseased brains. Ji et al [9] used rough conception to incorporate with spatial relationship for overcoming noise.…”

Section: Introductionmentioning

confidence: 99%

“…The pipeline is driven forward along the optimization path under global and local constraints. The spatial information employs a combination of posterior term for global constraint characterization and prior term for local characterization in Eq (8)…”

mentioning

confidence: 99%

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MR brain tissue classification based on the spatial information enhanced Gaussian mixture model

Bian¹

2022

THC

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BACKGROUND: Classifying T1-weighted Magnetic Resonance brain scans into cerebrospinal fluid, gray matter and white matter is one of the most critical tasks in neurodegenerative disease analysis. Since manual delineation is a labor-intensive and time-consuming process, automated methods have been widely adopted for this purpose. One group of commonly used method by biomedical researchers are based on Gaussian mixture model. The main drawbacks of this model include complex computational cost and parameter selection with the presence of imaging defects such as intensity inhomogeneity and noise. OBJECTIVE: To alleviate these aspects, an improved Gaussian mixture model-based method is proposed in this work. METHODS: Standard mixture model was used to formulate individual voxel intensity. A set of spatial weightings were created to represent local tissue characteristics. The emphasis of this method is its “lite” and robust implementation mode highlighted by a dedicated entropy term. The Expectation-Maximization algorithm was then iteratively executed to estimate model parameters. The Maximum a Posteriori criterion was employed to determine for each voxel if it belongs to a certain tissue. RESULTS: The proposed method was validated on both simulated and real MR scans. The averaged Dice coefficient of segmented brain tissues on each dataset ranged between [66.41, 87.42] for cerebrospinal fluid, [80.57, 85.35] for gray matter, and [83.17, 85.63] for white matter. CONCLUSIONS: Experiments illustrated the effectiveness and reliability in tissue classification against imaging defects compared with manually constructed reference standard.

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Section: Methodsmentioning

confidence: 99%