Diffuse liver disease classification from ultrasound surface characterization, clinical and laboratorial data

In this work liver contour is semi-automatically segmented and quantified in order to help the identification and diagnosis of diffuse liver disease. The features extracted from the liver contour are jointly used with clinical and laboratorial data in the staging process. The classification results of a support vector machine, a Bayesian and a k-nearest neighbor classifier are compared. A population of 88 patients at five different stages of diffuse liver disease and a leave-one-out cross-validation strategy are used in the classification process. The best results are obtained using the k-nearest neighbor classifier, with an overall accuracy of 80.68%. The good performance of the proposed method shows a reliable indicator that can improve the information in the staging of diffuse liver disease.

Vision-based representation and recognition of human activities in image sequences

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2013

Statistical pattern recognition for audio-forensics : empirical investigations on the application scenarios audio steganalysis and microphone forensics

Magdeburg, Univ., Fak. für Informatik, Diss., 2013

Three Generation Family With Pattern Dystrophy and a Successful Treatment of Subfoveal Cnv Related to Pattern Dystrophy With Anti-vegf Intravitreal Injections

Purpose: To phenotype a large 3 generation Swiss family with pattern dystrophy and to report a successful result of treatment with ranibizumab of a subfoveal choroidal neovascularisation (CNV) associated with pattern dystrophy in 1 patient Patients and methods: 4 affected and 3 unaffected patients (3 female 4 male, age range: 19 - 80 years) were assessed with a complete ophthalmologic examination. AF images were taken using Heidelberg Retina Angiograph and the digital color photos, fluorescein angiogragraphy (FFA) using the same TOPCON 501 camera. Electroretinogram (full-field and multifocal) was performed in 1 affected patient. One 48 years old patient developed a subfoveal CNV, which was treated with 2 injections of ranibizumab, at 3 months interval. Blood sample was taken for molecular analysis (screening of the gene RDS). Results: Two patients had a typical fundoscopic appearance of pattern dystrophy with butterfly shaped deposit at the fovea and some peripheral flecks, as shown with AF imaging.. Two others affected patients had a more unusual appearance with some macular atrophy in one or both eyes, surrounded by flecks. The visual acuity ranged from 1.0 to 0.1 according to Snellen EDTRS chart. The patient with subfoveal CNV presented a drop of vision form 1.0 to 0.6 within 10 days prior to the diagnosis and also reported some metamorphopsia. FFA and optical computerized tomography (OCT) confirmed a classic CNV. After the 1st injection her vision improved to 1.0 but persistent metamorphopsia and fluid on OCT motivated a second injection. One month after the second injection the OCT was flat and the patient had no symptoms. The results of RDS screening will be presented at the meeting. Conclusion: We present a family with pattern dystrophy, with some members having an unusual fundus appearance, which was mistaken for an early onset dry AMD. The AF imaging is a useful tool in diagnosing this condition. A CNV associated with pattern dystrophy a rare. This is the first report of a successful treatment of the CNV with anti-VEGF intravitreal injections.

Expression of non-TLR pattern recognition receptors in the spleen of BALB/c mice infected with Plasmodium yoelii and Plasmodium chabaudi chabaudi AS

The spleen plays a crucial role in the development of immunity to malaria, but the role of pattern recognition receptors (PRRs) in splenic effector cells during malaria infection is poorly understood. In the present study, we analysed the expression of selected PRRs in splenic effector cells from BALB/c mice infected with the lethal and non-lethal Plasmodium yoelii strains 17XL and 17X, respectively, and the non-lethal Plasmodium chabaudi chabaudi AS strain. The results of these experiments showed fewer significant changes in the expression of PRRs in AS-infected mice than in 17X and 17XL-infected mice. Mannose receptor C type 2 (MRC2) expression increased with parasitemia, whereas Toll-like receptors and sialoadhesin (Sn) decreased in mice infected with P. chabaudi AS. In contrast, MRC type 1 (MRC1), MRC2 and EGF-like module containing mucin-like hormone receptor-like sequence 1 (F4/80) expression decreased with parasitemia in mice infected with 17X, whereas MRC1 an MRC2 increased and F4/80 decreased in mice infected with 17XL. Furthermore, macrophage receptor with collagenous structure and CD68 declined rapidly after initial parasitemia. SIGNR1 and Sn expression demonstrated minor variations in the spleens of mice infected with either strain. Notably, macrophage scavenger receptor (Msr1) and dendritic cell-associated C-type lectin 2 expression increased at both the transcript and protein levels in 17XL-infected mice with 50% parasitemia. Furthermore, the increased lethality of 17X infection in Msr1 -/- mice demonstrated a protective role for Msr1. Our results suggest a dual role for these receptors in parasite clearance and protection in 17X infection and lethality in 17XL infection.

Early pattern recognition in severe perinatal asphyxia: a prospective MRI study.

On the basis of MRI examinations in 88 neonates and infants with perinatal asphyxia, we defined 6 different patterns on T2-weighted images: pattern A--scattered hyperintensity of both hemispheres of the telencephalon with blurred border zones between cortex and white matter, indicating diffuse brain injury; pattern B--parasagittal hyperintensity extending into the corona radiata, corresponding to the watershed zones; pattern C--hyper- and hypointense lesions in thalamus and basal ganglia, which relate to haemorrhagic necrosis or iron deposition in these areas; pattern D--periventricular hyperintensity, mainly along the lateral ventricles, i.e. periventricular leukomalacia (PVL), originating from the matrix zone; pattern E--small multifocal lesions varying from hyper--to hypointense, interpreted as necrosis and haemorrhage; pattern F--periventricular centrifugal hypointense stripes in the centrum semiovale and deep white matter of the frontal and occipital lobes. Contrast was effectively inverted on T1-weighted images. Patterns A, B and C were found in 17%, 25% and 37% of patients, and patterns D, E and F in 19%, 17% and 35%, respectively. In 49 patients a combination of patterns was observed, but 30% of the initial images were normal. At follow-up, persistent abnormalities were seen in all children with patterns A and D, but in only 52% of those with pattern C. Myelination was retarded most often in patients with diffuse brain injury and PVL (patterns A and D).

Application of fuzzy-rule-based postprocessing to correlation methods in pattern recognition

One of the most important problems in optical pattern recognition by correlation is the appearance of sidelobes in the correlation plane, which causes false alarms. We present a method that eliminate sidelobes of up to a given height if certain conditions are satisfied. The method can be applied to any generalized synthetic discriminant function filter and is capable of rejecting lateral peaks that are even higher than the central correlation. Satisfactory results were obtained in both computer simulations and optical implementation.

Evaluation of Bubble Formation and Break Up in Suppression Pools by Using Pattern Recognition Methods

During a possible loss of coolant accident in BWRs, a large amount of steam will be released from the reactor pressure vessel to the suppression pool. Steam will be condensed into the suppression pool causing dynamic and structural loads to the pool. The formation and break up of bubbles can be measured by visual observation using a suitable pattern recognition algorithm. The aim of this study was to improve the preliminary pattern recognition algorithm, developed by Vesa Tanskanen in his doctoral dissertation, by using MATLAB. Video material from the PPOOLEX test facility, recorded during thermal stratification and mixing experiments, was used as a reference in the development of the algorithm. The developed algorithm consists of two parts: the pattern recognition of the bubbles and the analysis of recognized bubble images. The bubble recognition works well, but some errors will appear due to the complex structure of the pool. The results of the image analysis were reasonable. The volume and the surface area of the bubbles were not evaluated. Chugging frequencies calculated by using FFT fitted well into the results of oscillation frequencies measured in the experiments. The pattern recognition algorithm works in the conditions it is designed for. If the measurement configuration will be changed, some modifications have to be done. Numerous improvements are proposed for the future 3D equipment.

Surface Reflectance Recognition and Real-World Illumination Statistics

Humans distinguish materials such as metal, plastic, and paper effortlessly at a glance. Traditional computer vision systems cannot solve this problem at all. Recognizing surface reflectance properties from a single photograph is difficult because the observed image depends heavily on the amount of light incident from every direction. A mirrored sphere, for example, produces a different image in every environment. To make matters worse, two surfaces with different reflectance properties could produce identical images. The mirrored sphere simply reflects its surroundings, so in the right artificial setting, it could mimic the appearance of a matte ping-pong ball. Yet, humans possess an intuitive sense of what materials typically "look like" in the real world. This thesis develops computational algorithms with a similar ability to recognize reflectance properties from photographs under unknown, real-world illumination conditions. Real-world illumination is complex, with light typically incident on a surface from every direction. We find, however, that real-world illumination patterns are not arbitrary. They exhibit highly predictable spatial structure, which we describe largely in the wavelet domain. Although they differ in several respects from the typical photographs, illumination patterns share much of the regularity described in the natural image statistics literature. These properties of real-world illumination lead to predictable image statistics for a surface with given reflectance properties. We construct a system that classifies a surface according to its reflectance from a single photograph under unknown illuminination. Our algorithm learns relationships between surface reflectance and certain statistics computed from the observed image. Like the human visual system, we solve the otherwise underconstrained inverse problem of reflectance estimation by taking advantage of the statistical regularity of illumination. For surfaces with homogeneous reflectance properties and known geometry, our system rivals human performance.

Translation Invariance in Object Recognition, and Its Relation to Other Visual Transformations

Human object recognition is generally considered to tolerate changes of the stimulus position in the visual field. A number of recent studies, however, have cast doubt on the completeness of translation invariance. In a new series of experiments we tried to investigate whether positional specificity of short-term memory is a general property of visual perception. We tested same/different discrimination of computer graphics models that were displayed at the same or at different locations of the visual field, and found complete translation invariance, regardless of the similarity of the animals and irrespective of direction and size of the displacement (Exp. 1 and 2). Decisions were strongly biased towards same decisions if stimuli appeared at a constant location, while after translation subjects displayed a tendency towards different decisions. Even if the spatial order of animal limbs was randomized ("scrambled animals"), no deteriorating effect of shifts in the field of view could be detected (Exp. 3). However, if the influence of single features was reduced (Exp. 4 and 5) small but significant effects of translation could be obtained. Under conditions that do not reveal an influence of translation, rotation in depth strongly interferes with recognition (Exp. 6). Changes of stimulus size did not reduce performance (Exp. 7). Tolerance to these object transformations seems to rely on different brain mechanisms, with translation and scale invariance being achieved in principle, while rotation invariance is not.

Visual Recognition and Categorization on the Basis of Similarities to Multiple Class Prototypes

To recognize a previously seen object, the visual system must overcome the variability in the object's appearance caused by factors such as illumination and pose. Developments in computer vision suggest that it may be possible to counter the influence of these factors, by learning to interpolate between stored views of the target object, taken under representative combinations of viewing conditions. Daily life situations, however, typically require categorization, rather than recognition, of objects. Due to the open-ended character both of natural kinds and of artificial categories, categorization cannot rely on interpolation between stored examples. Nonetheless, knowledge of several representative members, or prototypes, of each of the categories of interest can still provide the necessary computational substrate for the categorization of new instances. The resulting representational scheme based on similarities to prototypes appears to be computationally viable, and is readily mapped onto the mechanisms of biological vision revealed by recent psychophysical and physiological studies.

Medical image retrieval based on complexity analysis

Texture is one of the most important visual attributes used in image analysis. It is used in many content-based image retrieval systems, where it allows the identification of a larger number of images from distinct origins. This paper presents a novel approach for image analysis and retrieval based on complexity analysis. The approach consists of a texture segmentation step, performed by complexity analysis through BoxCounting fractal dimension, followed by the estimation of complexity of each computed region by multiscale fractal dimension. Experiments have been performed with MRI database in both pattern recognition and image retrieval contexts. Results show the accuracy of the method and also indicate how the performance changes as the texture segmentation process is altered.