Handbook of medical image processing and analysis

By I. Bankman. 2009

400 citations recorded by the source corpus.

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Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation (2019, background): “Readers can also refer to (Goodfellow et al., 2016) about DL and (Bankman, 2008) for MRI processing.”
A review on automatic fetal and neonatal brain MRI segmentation (2017, neutral): “(2013) Beare et al. (2016) Liu et al. (2016) Classification Anbeek et al.”
Multi-Parametric Neuroimaging Reproducibility: A 3T Resource Study (2010, background): “B 0 mapping MRI field maps provide information about the local magnetic field inhomogeneity, which is an important contributing factor for artifacts and signal loss (Bankman, 2008).”
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, background): “Several key technologies arise from the various medical imaging applications, including: [4], [5], [6], [7] • Medical image reconstruction, which aims to form a visual representation (aka an image) from signals acquired by a medical imaging device such as a CT or MRI scanner.”
Tracking in cell and developmental biology. (2009, methodology): “…recognition methods have been developed in the past 30 years to enable computer assisted diagnosis in various clinical applications [19] [20] [21], it is only since relatively recently that similar methods are being explored to facilitate automated image analysis in biological imaging [22]…”