Medical image enhancement using threshold decomposition driven adaptive morphological filter

One of the most common degradations in medical images is their poor contrast quality. This suggests the use of contrast enhancement methods as an attempt to modify the intensity distribution of the image. In this paper, a new edge detected morphological filter is proposed to sharpen digital medical images. This is done by detecting the positions of the edges and then applying a class of morphological filtering. Motivated by the success of threshold decomposition, gradientbased operators are used to detect the locations of the edges. A morphological filter is used to sharpen these detected edges. Experimental results demonstrate that the detected edge deblurring filter improved the visibility and perceptibility of various embedded structures in digital medical images. Moreover, the performance of the proposed filter is superior to that of other sharpener-type filters

Candidate Coronagraphic Detections of Protoplanetary Disks around Four Young Stars

We present potential detections of H-band scattered light emission around four young star, selected from a total sample of 45 young stars observed with the CIAO coronagraph of the Subaru telescope. Two CTTS, CI Tau and DI Cep, and two WTTS, LkCa 14 and RXJ 0338.3+1020 were detected. In all four cases, the extended emission is within the area of the residual PSF halo, and is revealed only through careful data reduction. We compare the observed extended emission with simulations of the scattered light emission, to evaluate the plausibility and nature of the detected emission.Comment: 9 Figures, 40 page

Achieving the Way for Automated Segmentation of Nuclei in Cancer Tissue Images through Morphology-Based Approach: a Quantitative Evaluation

In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for cancer diagnosis and therapy. We present a fully automated morphology-based technique able to perform accurate nuclear segmentations in images with heterogeneous staining and multiple tissue layers and we compare it with an alternate semi-automated method based on a well established segmentation approach, namely active contours. We discuss active contours’ limitations in the segmentation of immunohistochemical images and we demonstrate and motivate through extensive experiments the better accuracy of our fully automated approach compared to various active contours implementations

A Cosmic Watershed: the WVF Void Detection Technique

On megaparsec scales the Universe is permeated by an intricate filigree of clusters, filaments, sheets and voids, the Cosmic Web. For the understanding of its dynamical and hierarchical history it is crucial to identify objectively its complex morphological components. One of the most characteristic aspects is that of the dominant underdense Voids, the product of a hierarchical process driven by the collapse of minor voids in addition to the merging of large ones. In this study we present an objective void finder technique which involves a minimum of assumptions about the scale, structure and shape of voids. Our void finding method, the Watershed Void Finder (WVF), is based upon the Watershed Transform, a well-known technique for the segmentation of images. Importantly, the technique has the potential to trace the existing manifestations of a void hierarchy. The basic watershed transform is augmented by a variety of correction procedures to remove spurious structure resulting from sampling noise. This study contains a detailed description of the WVF. We demonstrate how it is able to trace and identify, relatively parameter free, voids and their surrounding (filamentary and planar) boundaries. We test the technique on a set of Kinematic Voronoi models, heuristic spatial models for a cellular distribution of matter. Comparison of the WVF segmentations of low noise and high noise Voronoi models with the quantitatively known spatial characteristics of the intrinsic Voronoi tessellation shows that the size and shape of the voids are succesfully retrieved. WVF manages to even reproduce the full void size distribution function.Comment: 24 pages, 15 figures, MNRAS accepted, for full resolution, see http://www.astro.rug.nl/~weygaert/tim1publication/watershed.pd

Development of a fusion adaptive algorithm for marine debris detection within the post-Sandy restoration framework

Recognition of marine debris represent a difficult task due to the extreme variability of the marine environment, the possible targets, and the variable skill levels of human operators. The range of potential targets is much wider than similar fields of research such as mine hunting, localization of unexploded ordnance or pipeline detection. In order to address this additional complexity, an adaptive algorithm is being developing that appropriately responds to changes in the environment, and context. The preliminary step is to properly geometrically and radiometrically correct the collected data. Then, the core engine manages the fusion of a set of statistically- and physically-based algorithms, working at different levels (swath, beam, snippet, and pixel) and using both predictive modeling (that is, a high-frequency acoustic backscatter model) and phenomenological (e.g., digital image processing techniques) approaches. The expected outcome is the reduction of inter-algorithmic cross-correlation and, thus, the probability of false alarm. At this early stage, we provide a proof of concept showing outcomes from algorithms that dynamically adapt themselves to the depth and average backscatter level met in the surveyed environment, targeting marine debris (modeled as objects of about 1-m size). The project relies on a modular software library, called Matador (Marine Target Detection and Object Recognition)

Adaptive Optics Imaging of the AU Microscopii Circumstellar Disk: Evidence for Dynamical Evolution

We present an H-band image of the light scattered from circumstellar dust around the nearby (10 pc) young M star AU Microscopii (AU Mic, GJ 803, HD 197481), obtained with the Keck adaptive optics system. We resolve the disk both vertically and radially, tracing it over 17-60 AU from the star. Our AU Mic observations thus offer the possibility to probe at high spatial resolution (0.04" or 0.4 AU per resolution element) for morphological signatures of the debris disk on Solar-System scales. Various sub-structures (dust clumps and gaps) in the AU Mic disk may point to the existence of orbiting planets. No planets are seen in our H-band image down to a limiting mass of 1 M_Jup at >20 AU, although the existence of smaller planets can not be excluded from the current data. Modeling of the disk surface brightness distribution at H-band and R-band, in conjunction with the optical to sub-millimeter spectral energy distribution, allows us to constrain the disk geometry and the dust grain properties. We confirm the nearly edge-on orientation of the disk inferred from previous observations, and deduce an inner clearing radius <=10 AU. We find evidence for a lack of small grains in the inner (<60 AU) disk, either as a result of primordial disk evolution, or because of destruction by Poynting-Robertson and/or corpuscular drag. A change in the power-law index of the surface brightness profile is observed near 33 AU, similar to a feature known in the profile of the beta Pic circumstellar debris disk. By comparing the time scales for inter-particle collisions and Poynting-Robertson drag between the two systems, we argue that the breaks are linked to one of these two processes.Comment: 17 pages, 7 figures, 1 table; accepted by Ap

Development of retinal blood vessel segmentation methodology using wavelet transforms for assessment of diabetic retinopathy

Automated image processing has the potential to assist in the early detection of diabetes, by detecting changes in blood vessel diameter and patterns in the retina. This paper describes the development of segmentation methodology in the processing of retinal blood vessel images obtained using non-mydriatic colour photography. The methods used include wavelet analysis, supervised classifier probabilities and adaptive threshold procedures, as well as morphology-based techniques. We show highly accurate identification of blood vessels for the purpose of studying changes in the vessel network that can be utilized for detecting blood vessel diameter changes associated with the pathophysiology of diabetes. In conjunction with suitable feature extraction and automated classification methods, our segmentation method could form the basis of a quick and accurate test for diabetic retinopathy, which would have huge benefits in terms of improved access to screening people for risk or presence of diabetes