Segmentation of Intensity Inhomogeneous Brain MR Images Using Active Contours

Segmentation of intensity inhomogeneous regions is a well-known problem in image analysis applications. This paper presents a region-based active contour method for image segmentation, which properly works in the context of intensity inhomogeneity problem. The proposed region-based active contour method embeds both region and gradient information unlike traditional methods. It contains mainly two terms, area and length, in which the area term practices a new region-based signed pressure force (SPF) function, which utilizes mean values from a certain neighborhood using the local binary fitted (LBF) energy model. In turn, the length term uses gradient information. The novelty of our method is to locally compute new SPF function, which uses local mean values and is able to detect boundaries of the homogenous regions. Finally, a truncated Gaussian kernel is used to regularize the level set function, which not only regularizes it but also removes the need of computationally expensive reinitialization. The proposed method targets the segmentation problem of intensity inhomogeneous images and reduces the time complexity among locally computed active contour methods. The experimental results show that the proposed method yields better segmentation result as well as less time complexity compared with the state-of-the-art active contour methods

Passive scalars in turbulent channel flow at high Reynolds number

We study passive scalars in turbulent plane channels at computationally high Reynolds number, thus allowing us to observe previously unnoticed effects. The mean scalar profiles are found to obey a generalized logarithmic law which includes a linear correction term in the whole lower half-channel, and they follow a universal parabolic defect profile in the core region. This is consistent with recent findings regarding the mean velocity profiles in channel flow. The scalar variances also exhibit a near universal parabolic distribution in the core flow and hints of a sizeable log layer, unlike the velocity variances. The energy spectra highlight the formation of large scalar-bearing eddies with size proportional to the channel height which are caused by a local production excess over dissipation, and which are clearly visible in the flow visualizations. Close correspondence of the momentum and scalar eddies is observed, with the main difference being that the latter tend to form sharper gradients, which translates into higher scalar dissipation. Another notable Reynolds number effect is the decreased correlation of the passive scalar field with the vertical velocity field, which is traced to the reduced effectiveness of ejection event

An improved segmentation method for lung cancer detection

Segmentation is one of the image processing technique which is use to segments an object from the background of an image. Problems may occur when segmenting an object from background normally due to cases such inhomogeneity intensity and others. This research proposed new segmentation method to be use in lung cancer diagnosis or detection purpose. Methods of image segmentation from every article are analyzed basically for advantage, features and drawbacks of each proposed method. Research suggest a combination of model, which have been proposed in other thesis or article with their results. Best model from reviewed articles which suggest best results is chosen, model A and model B to be combined and produce much better results in terms of accuracy and efficiency

Statistical Shape Modelling and Segmentation of the Respiratory Airway

The human respiratory airway consists of the upper (nasal cavity, pharynx) and the lower (trachea, bronchi) respiratory tracts. Accurate segmentation of these two airway tracts can lead to better diagnosis and interpretation of airway-specific diseases, and lead to improvement in the localization of abnormal metabolic or pathological sites found within and/or surrounding the respiratory regions. Due to the complexity and the variability displayed in the anatomical structure of the upper respiratory airway along with the challenges in distinguishing the nasal cavity from non-respiratory regions such as the paranasal sinuses, it is difficult for existing algorithms to accurately segment the upper airway without manual intervention. This thesis presents an implicit non-parametric framework for constructing a statistical shape model (SSM) of the upper and lower respiratory tract, capable of distinct shape generation and be adapted for segmentation. An SSM of the nasal cavity was successfully constructed using 50 nasal CT scans. The performance of the SSM was evaluated for compactness, specificity and generality. An averaged distance error of 1.47 mm was measured for the generality assessment. The constructed SSM was further adapted with a modified locally constrained random walk algorithm to segment the nasal cavity. The proposed algorithm was evaluated on 30 CT images and outperformed comparative state-of-the-art and conventional algorithms. For the lower airway, a separate algorithm was proposed to automatically segment the trachea and bronchi, and was designed to tolerate the image characteristics inherent in low-contrast CT images. The algorithm was evaluated on 20 clinical low-contrast CT from PET-CT patient studies and demonstrated better performance (87.1±2.8 DSC and distance error of 0.37±0.08 mm) in segmentation results against comparative state-of-the-art algorithms

The role of tropical and extra-tropical waves in the Hadley circulation

The tropical overturning circulation is examined in a moist aquaplanet general circulation model forced using a non-interactive sea surface temperature (SST) distribution that varies between a present-day Earth-like profile and one that is globally uniform. A traditional Hadley Cell (HC)-like flow is observed in all experiments along with the poleward transport of heat and angular momentum. In simulations with non-zero SST gradients, latent heat released from organized convection near the equator sets up a deep tropical cell; midlatitude baroclinic Rossby waves flux heat and angular momentum poleward, reinforcing the thermally direct circulation. As the imposed SST gradient is weakened, the HC transitions from a thermally and eddy-driven regime to one that's completely eddy-driven. When the SST is globally uniform, equatorial waves concentrate precipitation in the tropics and facilitate the lower-level convergence necessary for the ascending branch of the HC. Conventional midlatitude Rossby waves become very weak, but upper-level baroclinicity generates waves that cause equatorward transport of heat and poleward transport of momentum. Moreover, these upper-level waves induce a circulation that opposes the time-mean HC, thus highlighting the role of tropical waves in driving a traditional overturning flow for uniform SSTs. In all cases, anomalies associated with the tropical waves closely resemble those that sum to give the upper-level zonal mean divergent outflow. Through their ability to modulate tropical rainfall and the related latent heating, equatorial waves cause considerable hemispheric asymmetry in the HC and impart synoptic and intraseasonal variability to the tropical overturning circulation.Comment: 30 pages, 12 figures, submitted to QJRM

Foetal echocardiographic segmentation

Congenital heart disease affects just under one percentage of all live births [1]. Those defects that manifest themselves as changes to the cardiac chamber volumes are the motivation for the research presented in this thesis. Blood volume measurements in vivo require delineation of the cardiac chambers and manual tracing of foetal cardiac chambers is very time consuming and operator dependent. This thesis presents a multi region based level set snake deformable model applied in both 2D and 3D which can automatically adapt to some extent towards ultrasound noise such as attenuation, speckle and partial occlusion artefacts. The algorithm presented is named Mumford Shah Sarti Collision Detection (MSSCD). The level set methods presented in this thesis have an optional shape prior term for constraining the segmentation by a template registered to the image in the presence of shadowing and heavy noise. When applied to real data in the absence of the template the MSSCD algorithm is initialised from seed primitives placed at the centre of each cardiac chamber. The voxel statistics inside the chamber is determined before evolution. The MSSCD stops at open boundaries between two chambers as the two approaching level set fronts meet. This has significance when determining volumes for all cardiac compartments since cardiac indices assume that each chamber is treated in isolation. Comparison of the segmentation results from the implemented snakes including a previous level set method in the foetal cardiac literature show that in both 2D and 3D on both real and synthetic data, the MSSCD formulation is better suited to these types of data. All the algorithms tested in this thesis are within 2mm error to manually traced segmentation of the foetal cardiac datasets. This corresponds to less than 10% of the length of a foetal heart. In addition to comparison with manual tracings all the amorphous deformable model segmentations in this thesis are validated using a physical phantom. The volume estimation of the phantom by the MSSCD segmentation is to within 13% of the physically determined volume