12 May 2015 Neuromorphometry of primary brain tumors by magnetic resonance imaging
Nidiyare Hevia-Montiel, Pedro I. Rodriguez-Perez, Paul J. Lamothe-Molina, Alfonso Arellano-Reynoso, Ernesto Bribiesca, Marco A. Alegria-Loyola
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Abstract
Magnetic resonance imaging is a technique for the diagnosis and classification of brain tumors. Discrete compactness is a morphological feature of two-dimensional and three-dimensional objects. This measure determines the compactness of a discretized object depending on the sum of the areas of the connected voxels and has been used for understanding the morphology of nonbrain tumors. We hypothesized that regarding brain tumors, we may improve the malignancy grade classification. We analyzed the values in 20 patients with different subtypes of primary brain tumors: astrocytoma, oligodendroglioma, and glioblastoma multiforme subdivided into the contrast-enhanced and the necrotic tumor regions. The preliminary results show an inverse relationship between the compactness value and the malignancy grade of gliomas. Astrocytomas exhibit a mean of 973±14, whereas oligodendrogliomas exhibit a mean of 942±21. In contrast, the contrast-enhanced region of the glioblastoma presented a mean of 919±43, and the necrotic region presented a mean of 869±66. However, the volume and area of the enclosing surface did not show a relationship with the malignancy grade of the gliomas. Discrete compactness appears to be a stable characteristic between primary brain tumors of different malignancy grades, because similar values were obtained from different patients with the same type of tumor.
© 2015 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2015/$25.00 © 2015 SPIE
Nidiyare Hevia-Montiel, Pedro I. Rodriguez-Perez, Paul J. Lamothe-Molina, Alfonso Arellano-Reynoso, Ernesto Bribiesca, and Marco A. Alegria-Loyola "Neuromorphometry of primary brain tumors by magnetic resonance imaging," Journal of Medical Imaging 2(2), 024503 (12 May 2015). https://doi.org/10.1117/1.JMI.2.2.024503
Published: 12 May 2015
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Tumors

Brain

Magnetic resonance imaging

Neuroimaging

3D image processing

Image segmentation

Data acquisition

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