Ultrasound image segmentation thesis

Image Edge detection significantly reduces the amount of data and filters out useless information, while preserving the important structural properties in an image. Since edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection algorithms. In this paper the comparative analysis of various Image Edge Detection techniques is presented. Evaluation of the images showed that under noisy conditions Canny, LoG Laplacian of GaussianRobert, Prewitt, Sobel exhibit better performance, respectively.

Ultrasound image segmentation thesis

The first and the second order neighborhoods are the most commonly used neighborhoods in image segmentation. The first order neighborhood consists of 4 nearest nodes in a 2D image and 6 nearest nodes in a 3D image, while the second order neighborhood consists of 8 nearest nodes in a 2D image and 18 nearest nodes in a 3D image; see Figure 4.

Markov random field model can be represented with a graphwhere represents the nodes and determines the links also called edges that connect the nodes according to the neighborhood relationship.

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Such graph structure corresponds to an image, where nodes correspond to pixels or voxels and the links connecting the nodes represent the contextual dependency between pixels or voxels. More reading about MRF can be found in [ 4 ]. Image Features Image features represent distinctive characteristics of an object or an image structure to be segmented.

Features rely on numerical measurements, including quantitative visual appearance and shape descriptors, that can help to discriminate between the structures of interest and their background.

The outcome of image segmentation highly depends on appropriate feature selection choosing the most relevant features and accurate feature extraction.

The statistical features are based on first and second order statistics of gray level intensities in an image. First order features are derived from the image grey value histogram and include the intensity, mean, median, and standard deviation of the pixel values.

Ultrasound image segmentation thesis

Since these features do not incorporate any information on the spatial distribution of the pixel values, they are often used in combination with second order features. Second order descriptors are used to describe image texture and are typically computed using gray level cooccurrence matrix [ 5 ].

First and second order features are often called appearance features in the literature. This is because the visual appearance of an object of interest is typically associated with its pixel or voxel intensities gray values in brain MRI and spatial interaction between intensities intensity cooccurrence in an image.

The threshold is derived from the overall intensity distribution of the image. In the presence of image noise and other imaging artifacts, first order features are not sufficient for accurate brain MRI segmentation.

In this case more powerful second order discriminative features have to be used that include spatial interaction between intensities. In the spatial interaction models each intensity depends on a subset of the neighboring intensities; see Figures 5 and 4.

Additionally, image segmentation performance can be also improved by incorporating probabilistic prior shape models, which have been extensively used in medical image segmentation [ 6 — 10 ]. The probabilistic prior shape models specify an average shape and variation of an object of interest and are typically estimated from a population of coaligned images of the object training data sets [ 11 ].

One of the most popular features for image segmentation is edges. Edges refer to boundaries of an object surface where the intensities change sharply [ 12 ]. Such changes are typically detected by thresholding the first and second order spatial derivatives of the intensities the intensity gradient and Laplacian.

However, edges detected in this way are sensitive to image noise [ 13 ] and often require image smoothing as a preprocessing step [ 1415 ]. Another more robust method for edge detected is the phase congruency method [ 1617 ], which is a frequency-based method for feature detection.

This feature detection method, using local phase and energy, is based on a plausible model of how mammalians detect edges suggested by Morrone and Owens [ 18 ] and successfully explains the psychophysical effect of human feature perception.

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However, when intensity values are corrupted by MRI artifacts such as image noise, partial volume effect PVEand bias field effect, intensity-based segmentation algorithms will lead to wrong results. Thus, to obtain relevant and accurate segmentation results, very often several preprocessing steps are necessary to prepare MRI data.In general, polymers with a R 2 Si O unit are termed silicones, while the Si O repeat unit is also called siloxane.

The strength of the Si O bond gives the polymer its thermal and chemical stability, which is important for its use in high-temperature applications [1,2].In PDMS, the flexibility of the siloxane backbone permits the chains to easily arrange and .

The time-of-flight 3D laser scanner is an active scanner that uses laser light to probe the subject. At the heart of this type of scanner is a time-of-flight laser range leslutinsduphoenix.com laser range finder finds the distance of a surface by timing the round-trip time of a pulse of light.

Ultrasound image segmentation thesis

The time-of-flight 3D laser scanner is an active scanner that uses laser light to probe the subject. At the heart of this type of scanner is a time-of-flight laser range leslutinsduphoenix.com laser range finder finds the distance of a surface by timing the round-trip time of a pulse of light.

Advanced Ultrasound diagnosis in the Palm of your hand. Dr. Antonios Makropoulos - Chief Scientific Officer. Dr.

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Antonios has a PhD in medical image analysis from Imperial College London and a MSc in Artificial Intelligence from Edinburgh University. International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research.

Spatial Compounding and Segmentation of Volumetric Ultrasound Data Sets for Generating Interactive Anatomic Models by Jeremy Wynne Cannon Submitted to .

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