There are several different types of medical imaging modalities, among others magnetic resonance imaging
(MRI), positron emission tomography (PET), ultrasound, computed tomography (CT) or two-dimensional electrophoresis
images (2D-electrophoresis). The number of images is increasing rapidly and the development of automatic image processing
systems is necessary in order to aid in diagnostic decisions and therapy assessments. One of the most important
features in an image is texture, thus it is one of the central concepts in computer vision and should always be taken into
account as an innate property. There are various methods of extracting textural features from images; this work considers
statistical methods for texture analysis. Those methods analyze the spatial distribution of gray values by computing local
features. Depending on the number of pixels, statistical methods can be classified into first- (one pixel), second- (two pixels)
and high-order (three or more pixels). Second- and high-order statistics estimate properties of two or more pixel values
occurring at specific locations relative to each other. This paper is focused on the high-order statistics texture analysis
of CT, MRI, PET and 2D-electrophoresis images.
Keywords: Texture analysis, biomedical images, feature selection, Kernel-Based techniques, Higher-order statistics,
feature extraction, biomedical imaging, computer vision.
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