Ultrasound-Specific Segmentation via Decorrelation and Statistical Region-Based Active Contours

Segmentation of ultrasound images is often a very challenging task due to speckle noise that contaminates the image. It is well known that speckle noise exhibits an asymmetric distribution as well as significant spatial correlation. Since these attributes can be difficult to model, many previous ultrasound segmentation methods oversimplify the problem by assuming that the noise is white and/or Gaussian, resulting in generic approaches that are actually more suitable to MR and X-ray segmentation than ultrasound. Unlike these methods, in this paper we present an ultrasound-specific segmentation approach that first decorrelates the image, and then performs segmentation on the whitened result using statistical region-based active contours. In particular, we design a gradient ascent flow that evolves the active contours to maximize a log likelihood functional based on the Fisher-Tippett distribution. We present experimental results that demonstrate the effectiveness of our method

Ultrasound-Specific Segmentation via Decorrelation and Statistical Region-Based Active Contours

Segmentation of ultrasound images is often a very challenging task due to speckle noise that contaminates the image. It is well known that speckle noise exhibits an asymmetric distribution as well as significant spatial correlation. Since these attributes can be difficult to model, many previous ultrasound segmentation methods oversimplify the problem by assuming that the noise is white and/or Gaussian, resulting in generic approaches that are actually more suitable to MR and X-ray segmentation than ultrasound. Unlike these methods, in this paper we present an ultrasound-specific segmentation approach that first decorrelates the image, and then performs segmentation on the whitened result using statistical region-based active contours. In particular, we design a gradient ascent flow that evolves the active contours to maximize a log likelihood functional based on the Fisher-Tippett distribution. We present experimental results that demonstrate the effectiveness of our method

Statistical Region Based Segmentation of Ultrasound Images

Segmentation of ultrasound images is a challenging problem due to speckle, which corrupts the image and can result in weak or missing image boundaries, poor signal to noise ratio, and diminished contrast resolution. Speckle is a random interference pattern that is characterized by an asymmetric distribution as well as significant spatial correla- tion. These attributes of speckle are challenging to model in a segmentation approach, so many previous ultrasound segmentation methods simplify the problem by assuming that the speckle is white and/or Gaussian distributed. Unlike these methods, in this paper we present an ultrasound-specific segmentation approach that addresses both the spatial correlation of the data as well as its intensity distribution. We first decorrelate the image and then apply a region-based active contour whose motion is derived from an appropri- ate parametric distribution for maximum likelihood image segmentation. We consider zero-mean complex Gaussian, Rayleigh, and Fisher-Tippett flows, which are designed to model fully formed speckle in the in-phase/quadrature (IQ), envelope detected, and display (log compressed) images, respectively. We present experimental results demon- strating the effectiveness of our method, and compare the results to other parametric and non-parametric active contours

Image and Signal Processing in Intravascular Ultrasound

Intravascular ultrasound (rvUS) is a new imaging mOdality providing real-time, crosssectional, high-resolution images of the arterial lumen and vessel wall. In contrast to conventional x-ray angiography that only displays silhouette views of the vessel lumen, IVUS imaging permits visualization of lesion morphology and accurate measurements of arterial cross-sectional dimensions in patients. These unique capabilities have led to many important clinical applications including quantitative assessment of the severity, restenosis, progression of atherosclerosis, selection and guidance of catheterbased therapeutic procedures and short- and long-term evaluation of the outcome of an intravascular intervention. Like the progress of other medial imaging modalities, the advent of IVUS techniques has brought in new challenges in the field of signal and image processing. Quantitative analysis of IVUS images requires the identification of arterial structures such as the lumen and plaque within an image. Manual contour tracing is well known to be time consuming and subjective. Development of an automated contour detection method may improve the reproducibility of quantitative IVUS and avoid a tedious manual procedure. Computerized three-dimensional (3D) reconstruction of an IVUS image series may extend the tomographic data to a more powerful volumetric assessment of the vessel segment. Obviously, this could not be achieved without the advance of 3D image processing techniques. Furthermore, it is demonstrated that processing of the original radio frequency (RF) echo signals provides an efficient means to improve the IVUS image quality as well as a new approach to extract volumetric flow information. The goals of the studies reported in this thesis are therefore directed toward development of video image and RF signal processing techniques for image enhancement, automated contour detection, 3D reconstruction and flow imaging. In this chapter several IVUS scanning mechanisms and some background information about ultrasonic imaging are briefly introduced. The principles of different video-based contour detection approaches and examples of contour detection in echocardiograms are discussed. Subsequently, applications of RF analysis in IVUS images are reviewed, followed by the scope of this thesis in the final part

Optimización en GPU de algoritmos para la mejora del realce y segmentación en imágenes hepáticas

This doctoral thesis deepens the GPU acceleration for liver enhancement and segmentation. With this motivation, detailed research is carried out here in a compendium of articles. The work developed is structured in three scientific contributions, the first one is based upon enhancement and tumor segmentation, the second one explores the vessel segmentation and the last is published on liver segmentation. These works are implemented on GPU with significant speedups with great scientific impact and relevance in this doctoral thesis The first work proposes cross-modality based contrast enhancement for tumor segmentation on GPU. To do this, it takes target and guidance images as an input and enhance the low quality target image by applying two dimensional histogram approach. Further it has been observed that the enhanced image provides more accurate tumor segmentation using GPU based dynamic seeded region growing. The second contribution is about fast parallel gradient based seeded region growing where static approach has been proposed and implemented on GPU for accurate vessel segmentation. The third contribution describes GPU acceleration of Chan-Vese model and cross-modality based contrast enhancement for liver segmentation

Automated segmentation of soft tissue in abdominal CT scans

Master'sMASTER OF SCIENC

Optimizing the lateral beamforming step for filtered-delay multiply and sum beamforming to improve active contour segmentation using ultrafast ultrasound imaging

As an alternative to delay-and-sum beamforming, a novel beamforming technique called filtered-delay multiply and sum (FDMAS) was introduced recently to improve ultrasound B-mode image quality. Although a considerable amount of work has been performed to evaluate FDMAS performance, no study has yet focused on the beamforming step size, , in the lateral direction. Accordingly, the performance of FDMAS was evaluated in this study by fine-tuning to find its optimal value and improve boundary definition when balloon snake active contour (BSAC) segmentation was applied to a B-mode image in ultrafast imaging. To demonstrate the effect of altering in the lateral direction on FDMAS, measurements were performed on point targets, a tissue-mimicking phantom and in vivo carotid artery, by using the ultrasound array research platform II equipped with one 128-element linear array transducer, which was excited by 2-cycle sinusoidal signals. With 9-angle compounding, results showed that the lateral resolution (LR) of the point target was improved by 67.9% and 81.2%, when measured at −6 dB and −20 dB respectively, when was reduced from to . Meanwhile the image contrast ratio (CR) measured on the CIRS phantom was improved by 10.38 dB at the same reduction and the same number of compounding angles. The enhanced FDMAS results with lower side lobes and less clutter noise in the anechoic regions provides a means to improve boundary definition on a B-mode image when BSAC segmentation is applied