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A volumetric approach to segmentation and texture characterisation of ultrasound images



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Visual interpretation of noisy images is not an easy problem. This is certainly apparent with ultrasound images. Due to the noise inherent in the images, it is often the case that discrepancies as to location of object boundaries and detection of different tissues arise even among highly trained physicians. The relatively low cost and short image acquisition time, however, make ultrasound an attractive imaging modality. Currently, diagnostic evaluation of ultrasound images is performed on two-dimensional (2D) cross-sections of the object of interest. No depth information is available and there is no way of viewing the outer surface of the object. The only way for a physician to visualise the entire object is by mentally reconstructing the object based on a series of a 2D images as well as prior expectations of the morphology of the object. In the case of abnormal or diseased growth, the physician's expectations often do not correspond to the actual morphology of the object. However, the use of three-dimensional (3D) data acquisition and visualisation may be used to overcome these problems. The present work addresses a number of difficulties in processing 3D ultrasound data. This includes special treatment of the volumetric ultrasound data obtained from a 3D probe, determination of 3D features of the different tissue types present in the ultrasound data and identification and localisation of objects (segmentation) in the volumetric ultrasound data. Experimental results obtained from synthesised and real ultrasound data demonstrate that the present work contributes significantly to the use of ultrasound imaging as a diagnostic tool. As well, the present work can be applied to different imaging modalities or different applications areas and is thus beneficial to the area of biomedical image processing, in general.



ultrasonography, diagnosis, three-dimensional imaging in medicine, computer science, ultrasonic -- image quality, image processing



Doctor of Philosophy (Ph.D.)


Computer Science


Computer Science



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