Automatic image registration of three-dimensional images of the head of cats and dogs by use of maximization of mutual information

Peter Böttcher Department of Small Animal Surgery, Faculty of Veterinary Medicine, Ludwig-Maximilians-University, Munich, Germany.
Present address is the Department of Small Animal Medicine, Faculty of Veterinary Medicine, University of Leipzig, An den Tierkliniken 23, 04103 Leipzig, Germany.

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Johann Maierl Department of Veterinary Anatomy, Faculty of Veterinary Medicine, Ludwig-Maximilians-University, Munich, Germany.

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Silke Hecht Department of Small Animal Surgery, Faculty of Veterinary Medicine, Ludwig-Maximilians-University, Munich, Germany.
Present address is the Department of Clinical Sciences, Massachusetts School of Veterinary Medicine, Tufts University, North Grafton, MA 01536.

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Ulrike Matis Department of Small Animal Surgery, Faculty of Veterinary Medicine, Ludwig-Maximilians-University, Munich, Germany.

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Hans-Georg Liebich Department of Veterinary Anatomy, Faculty of Veterinary Medicine, Ludwig-Maximilians-University, Munich, Germany.

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Abstract

Objective—To validate mutual information criterion as a ready-to-use technique for automated alignment (ie, registration) of 3-dimensional (3-D) multimodal image data of the head of cats and dogs.

Sample Population—Corresponding 3-D magnetic resonance imaging (MRI) and computed tomography (CT) brain scans of a 6-month-old Doberman Pinscher with a brain cyst; CT images of the head of a European shorthair cat with a meningioma before and immediately, 3, and 6 months after surgical resection; and CT and corresponding stacked anatomic cryosection images of the entire head of a 2-year-old sexually intact female Beagle.

Procedure—All images were matched retrospectively by use of an in-house computer program developed on the basis of a mutual information image registration algorithm. Accuracy of the resulting registrations was evaluated by visual inspection.

Results—All registrations were judged to be highly accurate. Additional manual corrections were not necessary.

Conclusions and Clinical Relevance—Mutual information registration criterion can by applied to 3-D multimodal head images of cats and dogs for full automatic rigid-body image registration. The combination of such aligned images would considerably facilitate efforts of veterinary clinicians as indicated by its widespread use in brain surgery and radiation therapy of humans. (Am J Vet Res 2004;65:1680–1687)

Abstract

Objective—To validate mutual information criterion as a ready-to-use technique for automated alignment (ie, registration) of 3-dimensional (3-D) multimodal image data of the head of cats and dogs.

Sample Population—Corresponding 3-D magnetic resonance imaging (MRI) and computed tomography (CT) brain scans of a 6-month-old Doberman Pinscher with a brain cyst; CT images of the head of a European shorthair cat with a meningioma before and immediately, 3, and 6 months after surgical resection; and CT and corresponding stacked anatomic cryosection images of the entire head of a 2-year-old sexually intact female Beagle.

Procedure—All images were matched retrospectively by use of an in-house computer program developed on the basis of a mutual information image registration algorithm. Accuracy of the resulting registrations was evaluated by visual inspection.

Results—All registrations were judged to be highly accurate. Additional manual corrections were not necessary.

Conclusions and Clinical Relevance—Mutual information registration criterion can by applied to 3-D multimodal head images of cats and dogs for full automatic rigid-body image registration. The combination of such aligned images would considerably facilitate efforts of veterinary clinicians as indicated by its widespread use in brain surgery and radiation therapy of humans. (Am J Vet Res 2004;65:1680–1687)

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