Identification of Enhancing MS Lesions in MR Images using Non-Parametric Image Subtraction

Paul Bromiley; Maja Pokric; Neil Thacker; Alex Houston; Reyer Zwiggelaar

Identification of Enhancing MS Lesions in MR Images using Non-Parametric Image Subtraction

Paul Bromiley, Maja Pokric, Neil Thacker, Alex Houston (Editor), Reyer Zwiggelaar (Editor)

Research output: Contribution to conference › Poster

Abstract

Simple pixel-by-pixel image subtraction is widely used in image analysis to identify changes in image pairs. For example, multiple sclerosis (MS) produces lesions in the brain that can be detected by subtraction of MRI scans taken before and after the injection of GdDTPA contrast agent, which highlights the lesions. However, the result is returned in arbitrary units of pixel grey-level, with no statistically well-defined meaning. We describe a new, non-parametric subtraction measure, analogous to standard statistical tests, which allows regional data fusion and direct probabilistic interpretation of image differences. We demonstrate the technique using scans of MS lesions, but is expected to be applicable to a wide range of image formation processes.

Original language	English
Pages	117-120
Number of pages	4
Publication status	Published - 2002
Event	6th Medical Image Understanding and Analysis Conference - University of Portsmouth Duration: 22 Jul 2002 → 23 Jul 2002

Conference

Conference	6th Medical Image Understanding and Analysis Conference
City	University of Portsmouth
Period	22/07/02 → 23/07/02

Cite this

@conference{f9b1d5f7021f4485b951185592208778,

title = "Identification of Enhancing MS Lesions in MR Images using Non-Parametric Image Subtraction",

abstract = "Simple pixel-by-pixel image subtraction is widely used in image analysis to identify changes in image pairs. For example, multiple sclerosis (MS) produces lesions in the brain that can be detected by subtraction of MRI scans taken before and after the injection of GdDTPA contrast agent, which highlights the lesions. However, the result is returned in arbitrary units of pixel grey-level, with no statistically well-defined meaning. We describe a new, non-parametric subtraction measure, analogous to standard statistical tests, which allows regional data fusion and direct probabilistic interpretation of image differences. We demonstrate the technique using scans of MS lesions, but is expected to be applicable to a wide range of image formation processes.",

author = "Paul Bromiley and Maja Pokric and Neil Thacker and Alex Houston and Reyer Zwiggelaar",

year = "2002",

language = "English",

pages = "117--120",

note = "6th Medical Image Understanding and Analysis Conference ; Conference date: 22-07-2002 Through 23-07-2002",

}

TY - CONF

T1 - Identification of Enhancing MS Lesions in MR Images using Non-Parametric Image Subtraction

AU - Bromiley, Paul

AU - Pokric, Maja

AU - Thacker, Neil

A2 - Houston, Alex

A2 - Zwiggelaar, Reyer

PY - 2002

Y1 - 2002

N2 - Simple pixel-by-pixel image subtraction is widely used in image analysis to identify changes in image pairs. For example, multiple sclerosis (MS) produces lesions in the brain that can be detected by subtraction of MRI scans taken before and after the injection of GdDTPA contrast agent, which highlights the lesions. However, the result is returned in arbitrary units of pixel grey-level, with no statistically well-defined meaning. We describe a new, non-parametric subtraction measure, analogous to standard statistical tests, which allows regional data fusion and direct probabilistic interpretation of image differences. We demonstrate the technique using scans of MS lesions, but is expected to be applicable to a wide range of image formation processes.

AB - Simple pixel-by-pixel image subtraction is widely used in image analysis to identify changes in image pairs. For example, multiple sclerosis (MS) produces lesions in the brain that can be detected by subtraction of MRI scans taken before and after the injection of GdDTPA contrast agent, which highlights the lesions. However, the result is returned in arbitrary units of pixel grey-level, with no statistically well-defined meaning. We describe a new, non-parametric subtraction measure, analogous to standard statistical tests, which allows regional data fusion and direct probabilistic interpretation of image differences. We demonstrate the technique using scans of MS lesions, but is expected to be applicable to a wide range of image formation processes.

M3 - Poster

SP - 117

EP - 120

T2 - 6th Medical Image Understanding and Analysis Conference

Y2 - 22 July 2002 through 23 July 2002

ER -

Identification of Enhancing MS Lesions in MR Images using Non-Parametric Image Subtraction

Abstract

Conference

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