[
    {
        "id": "authors:41nmf-n8k69",
        "collection": "authors",
        "collection_id": "41nmf-n8k69",
        "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:LAIieeetmi98",
        "type": "article",
        "title": "Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms",
        "author": [
            {
                "family_name": "Laidlaw",
                "given_name": "David H.",
                "clpid": "Laidlaw-D-H"
            },
            {
                "family_name": "Fleischer",
                "given_name": "Kurt W.",
                "clpid": "Fleischer-K-W"
            },
            {
                "family_name": "Barr",
                "given_name": "Alan H.",
                "clpid": "Barr-A-H"
            }
        ],
        "abstract": "The authors present a new algorithm for identifying the distribution of different material types in volumetric datasets such as those produced with magnetic resonance imaging (MRI) or computed tomography (CT). Because the authors allow for mixtures of materials and treat voxels as regions, their technique reduces errors that other classification techniques can create along boundaries between materials and is particularly useful for creating accurate geometric models and renderings from volume data. It also has the potential to make volume measurements more accurately and classifies noisy, low-resolution data well. There are two unusual aspects to the authors' approach. First, they assume that, due to partial-volume effects, or blurring, voxels can contain more than one material, e.g., both muscle and fat; the authors compute the relative proportion of each material in the voxels. Second, they incorporate information from neighboring voxels into the classification process by reconstructing a continuous function, \u03c1(x), from the samples and then looking at the distribution of values that \u03c1(x) takes on within the region of a voxel. This distribution of values is represented by a histogram taken over the region of the voxel; the mixture of materials that those values measure is identified within the voxel using a probabilistic Bayesian approach that matches the histogram by finding the mixture of materials within each voxel most likely to have created the histogram. The size of regions that the authors classify is chosen to match the sparing of the samples because the spacing is intrinsically related to the minimum feature size that the reconstructed continuous function can represent.",
        "doi": "10.1109/42.668696",
        "issn": "0278-0062",
        "publisher": "IEEE Transactions on Medical Imaging",
        "publication": "IEEE Transactions on Medical Imaging",
        "publication_date": "1998-02-01",
        "series_number": "1",
        "volume": "17",
        "issue": "1",
        "pages": "74-86"
    },
    {
        "id": "authors:khz19-3vv29",
        "collection": "authors",
        "collection_id": "khz19-3vv29",
        "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:GHOieeetmi95",
        "type": "article",
        "title": "Pure phase-encoded MRI and classification of solids",
        "author": [
            {
                "family_name": "Ghosh",
                "given_name": "Pratik",
                "clpid": "Ghosh-P"
            },
            {
                "family_name": "Laidlaw",
                "given_name": "David H.",
                "clpid": "Laidlaw-D-H"
            },
            {
                "family_name": "Fleischer",
                "given_name": "Kurt W.",
                "clpid": "Fleischer-K-W"
            },
            {
                "family_name": "Barr",
                "given_name": "Alan H.",
                "clpid": "Barr-A-H"
            },
            {
                "family_name": "Jacobs",
                "given_name": "Russell E.",
                "orcid": "0000-0002-1382-8486",
                "clpid": "Jacobs-R-E"
            }
        ],
        "abstract": "Here, the authors combine a pure phase-encoded magnetic resonance imaging (MRI) method with a new tissue-classification technique to make geometric models of a human tooth. They demonstrate the feasibility of three-dimensional imaging of solids using a conventional 11.7-T NMR spectrometer. In solid-state imaging, confounding line-broadening effects are typically eliminated using coherent averaging methods. Instead, the authors circumvent them by detecting the proton signal at a fixed phase-encode time following the radio-frequency excitation. By a judicious choice of the phase-encode time in the MRI protocol, the authors differentiate enamel and dentine sufficiently to successfully apply a new classification algorithm. This tissue-classification algorithm identifies the distribution of different material types, such as enamel and dentine, in volumetric data. In this algorithm, the authors treat a voxel as a volume, not as a single point, and assume that each voxel may contain more than one material. They use the distribution of MR image intensities within each voxel-sized volume to estimate the relative proportion of each material using a probabilistic approach. This combined approach, involving MRI and data classification, is directly applicable to bone imaging and hard-tissue contrast-based modeling of biological solids.",
        "doi": "10.1109/42.414627",
        "issn": "0278-0062",
        "publisher": "IEEE",
        "publication": "IEEE Transactions on Medical Imaging",
        "publication_date": "1995-09",
        "series_number": "3",
        "volume": "14",
        "issue": "3",
        "pages": "616-620"
    }
]