Дисертації з теми "Segmentation technology"

Segmentation is a process that separates objects in an image. In medical images, particularly image volumes, the field of application is wide. For example 3D visualisations of the anatomy could benefit enormously from segmentation. The aim of this thesis is to construct a segmentation tool.

The project consist three main parts. First, a survey of the actual need of segmentation in medical image volumes was carried out. Then a unique three-step model for a segmentation tool was implemented, tested and evaluated.

The first step of the segmentation tool is a seed-growing method that uses the intensity and an orientation tensor estimate to decide which voxels that are part of the project. The second step uses an active contour, a deformable “balloon”. The contour is shrunk to fit the segmented border from the first step, yielding a surface suitable for visualisation. The last step consists of letting the contour reshape according to the orientation tensor estimate.

The use evaluation establishes the usefulness of the tool. The model is flexible and well adapted to the users’ requests. For unclear objects the segmentation may fail, but the cause is mostly poor image quality. Even though much work remains to be done on the second and third part of the tool, the results are most promising.

This Master's Thesis addresses the problem of segmenting an image sequence with respect to the motion in the sequence. As a basis for the motion estimation, 3D orientation tensors are used. The goal of the segmentation is to partition the images into regions, characterized by having a coherent motion. The motion model is affine with respect to the image coordinates. A method to estimate the parameters of the motion model from the orientation tensors in a region is presented. This method can also be generalized to a large class of motion models.

Two segmentation algorithms are presented together with a postprocessing algorithm. All these algorithms are based on the competitive algorithm, a general method for distributing points between a number of regions, without relying on arbitrary threshold values. The first segmentation algorithm segments each image independently, while the second algorithm recursively takes advantage of the previous segmentation. The postprocessing algorithm stabilizes the segmentations of a whole sequence by imposing continuity constraints.

The algorithms have been implemented and the results of applying them to a test sequence are presented. Interesting properties of the algorithms are that they are robust to the aperture problem and that they do not require a dense velocity ¯eld.

It is finally discussed how the algorithms can be developed and improved. It is straightforward to extend the algorithms to base the segmentations on alternative or additional features, under not too restrictive conditions on the features.

This thesis presents a 3D semi-automatic segmentation technique for extracting the lumen surface of the Carotid arteries including the bifurcation from 3D and 4D ultrasound examinations.

Ultrasound images are inherently noisy. Therefore, to aid the inspection of the acquired data an adaptive edge preserving filtering technique is used to reduce the general high noise level. The segmentation process starts with edge detection with a recursive and separable 3D Monga-Deriche-Canny operator. To reduce the computation time needed for the segmentation process, a seeded region growing technique is used to make an initial model of the artery. The final segmentation is based on the inflatable balloon model, which deforms the initial model to fit the ultrasound data. The balloon model is implemented with the finite element method.

The segmentation technique produces 3D models that are intended as pre-planning tools for surgeons. The results from a healthy person are satisfactory and the results from a patient with stenosis seem rather promising. A novel 4D model of wall motion of the Carotid vessels has also been obtained. From this model, 3D compliance measures can easily be obtained.

In ordinary image coding, the same image quality is obtained in all parts of an image. If it is known that there is only one viewer, and where in the image that viewer is focusing, the quality can be degraded in other parts of the image without incurring any perceptible coding artefacts. This master's thesispresents a coding scheme where an image is segmented into homogeneous regions which are then separately coded, and where knowledge about the user's focus point is used to obtain further data reduction. It is concluded that the coding performance does not quite reach the levels attained when applying focus-based quality degradation to coding schemes not based on segmentation.

Part 1: The bandwidth of the ultrasound Doppler signal is proposed as a classification function of blood and tissue signal in transthoracial echocardiography of the left ventricle. The new echocardiographic mode, Bandwidth Imaging, utilizes the difference in motion between tissue and blood. Specifically, Bandwidth Imaging is the absolute value of the normalized autocorrelation function with lag one. Bandwidth Imaging is therefore linearly dependent on the the square of the bandwidth estimated from the Doppler spectrum. A 2-tap Finite Impulse Response high-pass filter is used prior to autocorrelation calculation to account for the high level of DC clutter noise in the apical regions. Reasonable pulse strategies are discussed and several images of Bandwidth Imaging are included. An in vivo experiment is presented, where the apparent error rate of Bandwidth Imaging is compared with apparent error rate of Second-Harmonic Imaging on 15 healthy men. The apparent error rate is calculated from signal from all myocardial wall segments defined in \cite{Cer02}. The ground truth of the position of the myocardial wall segments is determined by manual tracing of endocardium in Second-Harmonic Imaging. A hypotheses test of Bandwidth Imaging having lower apparent error rate than

Second-Harmonic Imaging is proved for a p-value of 0.94 in 3 segments of end diastole and 1 segment in end systole on non averaged data. When data is averaged by a structural element of 5 radial, 3 lateral and 4 temporal samples, the numbers of segments are increased to 9 in end diastole and to 6 in end systole. These segments are mostly located in apical and anterior wall regions. Further, a global measure GM is defined as the proportion of misclassified area in the regions close to endocardium in an image. The hypothesis test of Second-Harmonic Imaging having lower GM than Bandwidth Imaging is proved for a p-value of 0.94 in the four-chamber view in end systole in any type of averaging. On the other side, the hypothesis test of Bandwidth Imaging having lower GM than Second-Harmonic Imaging is proved for a p-value of 0.94 in long-axis view in end diastole in any type of averaging. Moreover, if images are averaged by the above structural element the test indicates that Bandwidth Imaging has a lower apparent error rate than Second-Harmonic Imaging in all views and times (end diastole or end systole), except in four-chamber view in end systole. This experiment indicates that Bandwidth Imaging can supply additional information for automatic border detection routines on endocardium.

Part 2: Knowledge Based Imaging is suggested as a method to distinguish blood from tissue signal in transthoracial echocardiography. This method utilizes the maximum likelihood function to classify blood and tissue signal. Knowledge Based Imaging uses the same pulse strategy as Bandwidth Imaging, but is significantly more difficult to implement. Therefore, Knowledge Based Imaging and Bandwidth Imaging are compared with Fundamental Imaging by a computer simulation based on a parametric model of the signal. The rate apparent error rate is calculated in any reasonable tissue to blood signal ratio, tissue to white noise ratio and clutter to white noise ratio. Fundamental Imaging classifies well when tissue to blood signal ratio is high and tissue to white noise ratio is higher than clutter to white noise ratio. Knowledge Based Imaging classifies also well in this environment. In addition, Knowledge Based Imaging classifies well whenever blood to white noise ratio is above 30 dB. This is the case, even when clutter to white noise ratio is higher than tissue to white noise ratio and tissue to blood signal ratio is zero. Bandwidth Imaging performs similar to Knowledge Based Imaging, but blood to white noise ratio has to be 20 dB higher for a reasonable classification. Also the highpass filter coefficient prior to Bandwidth Imaging calculation is discussed by the simulations. Some images of different parameter settings of Knowledge Based Imaging are visually compared with Second-Harmonic Imaging, Fundamental Imaging and Bandwidth Imaging. Changing parameters of Knowledge Based Imaging can make the image look similar to both Bandwidth Imaging and Fundamental Imaging.

During pre-operative planning for hip replacement, the choice of prosthesis can be aided by measurements in x-ray images of the hip. Some measurements can be done automatically but this require robust and precise image processing algorithms which can detect anatomical features. The Trochanter minor is an important landmark on the femoral shaft. In this thesis, three di.erent image processing algorithms are explained and tested for automatic landmark detection on Trochanter minor. The algorithms handled are Active Shape Models, Shortest path algorithm and a segmentation technique based on cumulated cost maps. The results indicate that cumulated cost maps are an e.ective tool for rough segmentation of the Trochanter Minor. A snake algorithm was then applied which could .nd the edge of the Trochanter minor in all images used in the test. The edge can be used to locate a curvature extremum which can be used as a landmark point.

One of the fields where image processing finds its application but that remains as anunexplored territory is the analysis of cell morphology. This master thesis proposes a systemto carry out this research and sets the necessary technical basis to make it feasible, rangingfrom the processing of time-lapse sequences using image segmentation to the representation,description and classification of cells in terms of morphology.

Due to the highly variability of cell morphological characteristics several segmentationmethods have been implemented to face each of the problems encountered: Edge-detection,region-growing and marked watershed were found to be successful processing algorithms.This variability inherent to cells and the fact that human eye has a natural disposition to solvesegmentation problems finally lead to the development of a user-friendly interactiveapplication, the Time Lapse Sequence Processor (TLSP). Although it was initially consideredas a mere interface to perform cell segmentation, TLSP concept has evolved into theconstruction of a complete multifunction tool to perform cell morphology analysis:segmentation, morphological data extraction, analysis and management, cell tracking andrecognition system, etc. In its last version, TLSP v0.2 Alpha contains several segmentationtools, improved user interface and, data extraction and management capabilities.

Finally, a wide set of recommendations and improvements have been discussed, pointing the path for future development in this area.

This Master’s thesis studies the possibility of using image processing as a tool to facilitate vine management, in particular shoot counting and assessment of the grapevine canopy. Both are areas where manual inspection is done today. The thesis presents methods of capturing images and segmenting different parts of a vine. It also presents and evaluates different approaches on how shoot counting can be done. Within canopy assessment, the emphasis is on methods to estimate canopy density. Other possible assessment areas are also discussed, such as canopy colour and measurement of canopy gaps and fruit exposure. An example of a vine assessment system is given.

By applying a segmentation procedure to two different sets of computed tomography scans, two geometrical models of the abdominal aorta, containing one inlet and two outlets have been constructed. One of these depicts a healthy blood vessel while the other displays one afflicted with a Abdominal Aortic Aneurysm.

After inputting these geometries into the computational dynamics software FLUENT, six simulations of laminar, stationary flow of a fluid that was assumed to be Newtonian were performed. The mass flow rate across the model outlet boundaries was varied for the different simulations to produce a basis for a parameter analysis study.

The segmentation data was also used as input data to a surface description procedure which produced not only the surface itself, but also the first and second directional derivatives in every one of its defining spatial data points. These sets of derivatives were followingly applied in an additional procedure that calculated values of Gaussian curvature.

A parameter variance analysis was carried out to evaluate the performance of the surface generation procedure. An array of resultant surfaces and surface directional derivatives were obtained. Values of Gaussian curvature were calculated in the defining spatial data points of a few selected surfaces.

The curvature values of a selected data set were visualized through a contour plot as well as through a surface map. Comparisons between the curvature surface map and one wall shear stress surface map were made.