4 research outputs found

    Brightness and Contrast Modification in Ultrasonography Images Using Edge Detection Results

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    Currently, ultrasonography device become an important equipment for supporting diagnosis in diesases. Unfortunetaly, a lot of ultrasonography images do not provide enough information for supporting diagnosis especially images produced by low-resolution ultrasonography. It is caused by image quality that has been produced is inadequate because of noise. This research aims to improve image quality by modifying brightness and contrast to the edge detection algorithms. By modifying the brightness and contrast will cause the value of standard deviation of the ultrasonography image is lowered. Raising setting values will cause deviation standard value becomes smaller, and also the result of standard deviation is inversely proportional to the value of RMSE.  The results show that this modification can improve image quality by reducing noise significantly

    Channel characterisation and modelling for transcranial Doppler ultrasound.

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    The detection of micro-embolic signals (MES) is a mature application of transcranial Doppler (TCD) ultrasound. It involves the identification of abnormally highpitched signals within the arterial waveform as a method of diagnosis and prediction of embolic complications in stroke patients. More recently, algorithms have been developed to help characterise and classify MES using advanced signal processing techniques. These advances aim to improve our understanding of the causes of cereberovascular disease, helping to target the most appropriate interventions and quantifying the risk to patients of further stroke events. However, there are a number of limitations with current TCD systems which reduce their effectiveness. In particular, improvements in our understanding of the scattering effects in TCD ultrasound propagation channels will benefit our ability to develop algorithms that more robustly and reliably identify the consistency and material make-up of MES. This thesis explores TCD propagation channels in three related research areas. Firstly, a method of characterising TCD ultrasound propagation channels is proposed. Isotropic and non-isotropic three dimensional space (3-D) spherical scattering channel models are described in terms of theoretical reference models, simulation models, and sum of sinusoids (SoS) simulators, allowing the statistical properties to be analysed and reported. Secondly, a TCD ultrasound medical blood flow phantom is described. The phantom, designed to replicate blood flow in the middle cerebral arteries (MCA) for TCD ultrasound studies, is discussed in terms of material selection, physical construction and acoustic characteristics, including acoustic velocity, attenuation and backscatter coefficients. Finally, verification analysis is performed on the non-isotropic models against firstly, the blood flow phantom, and secondly, a patient recordings database. This analysis expands on areas of agreement and disagreement before assessing the usefulness of the models and describing their potential to improve signal processing approaches for detection of MES. The proposed non-isotropic channel reference model, simulation model, SoS simulator, and blood flow phantom are expected to contribute to improvements in the design, testing, and performance evaluation of future TCD ultrasound systems

    Generalizable automated pixel-level structural segmentation of medical and biological data

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    Over the years, the rapid expansion in imaging techniques and equipments has driven the demand for more automation in handling large medical and biological data sets. A wealth of approaches have been suggested as optimal solutions for their respective imaging types. These solutions span various image resolutions, modalities and contrast (staining) mechanisms. Few approaches generalise well across multiple image types, contrasts or resolution. This thesis proposes an automated pixel-level framework that addresses 2D, 2D+t and 3D structural segmentation in a more generalizable manner, yet has enough adaptability to address a number of specific image modalities, spanning retinal funduscopy, sequential fluorescein angiography and two-photon microscopy. The pixel-level segmentation scheme involves: i ) constructing a phase-invariant orientation field of the local spatial neighbourhood; ii ) combining local feature maps with intensity-based measures in a structural patch context; iii ) using a complex supervised learning process to interpret the combination of all the elements in the patch in order to reach a classification decision. This has the advantage of transferability from retinal blood vessels in 2D to neural structures in 3D. To process the temporal components in non-standard 2D+t retinal angiography sequences, we first introduce a co-registration procedure: at the pairwise level, we combine projective RANSAC with a quadratic homography transformation to map the coordinate systems between any two frames. At the joint level, we construct a hierarchical approach in order for each individual frame to be registered to the global reference intra- and inter- sequence(s). We then take a non-training approach that searches in both the spatial neighbourhood of each pixel and the filter output across varying scales to locate and link microvascular centrelines to (sub-) pixel accuracy. In essence, this \link while extract" piece-wise segmentation approach combines the local phase-invariant orientation field information with additional local phase estimates to obtain a soft classification of the centreline (sub-) pixel locations. Unlike retinal segmentation problems where vasculature is the main focus, 3D neural segmentation requires additional exibility, allowing a variety of structures of anatomical importance yet with different geometric properties to be differentiated both from the background and against other structures. Notably, cellular structures, such as Purkinje cells, neural dendrites and interneurons, all display certain elongation along their medial axes, yet each class has a characteristic shape captured by an orientation field that distinguishes it from other structures. To take this into consideration, we introduce a 5D orientation mapping to capture these orientation properties. This mapping is incorporated into the local feature map description prior to a learning machine. Extensive performance evaluations and validation of each of the techniques presented in this thesis is carried out. For retinal fundus images, we compute Receiver Operating Characteristic (ROC) curves on existing public databases (DRIVE & STARE) to assess and compare our algorithms with other benchmark methods. For 2D+t retinal angiography sequences, we compute the error metrics ("Centreline Error") of our scheme with other benchmark methods. For microscopic cortical data stacks, we present segmentation results on both surrogate data with known ground-truth and experimental rat cerebellar cortex two-photon microscopic tissue stacks.Open Acces
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