Karolina Nurzynska

Influence of applied corneal endothelium image segmentation techniques on the clinical parameters

The corneal endothelium state is verified on the basis of an in vivo specular microscope image from which the shape and density of cells are exploited for data description. Due to the relatively low image quality resulting from a high magnification of the living, non-stained tissue, both manual and automatic analysis of the data is a challenging task. Although, many automatic or semi-automatic solutions have already been introduced, all of them are prone to inaccuracy. This work presents a comparison of four methods (fully-automated or semi-automated) for endothelial cell segmentation, all of which represent a different approach to cell segmentation; fast robust stochastic watershed (FRSW), KH method, active contours solution (SNAKE), and TOPCON ImageNET. Moreover, an improvement framework is introduced which aims to unify precise cell border location in images pre-processed with differing techniques. Finally, the influence of the selected methods on clinical parameters is examined, both with and without the improvement framework application. The experiments revealed that although the image segmentation approaches differ, the measures calculated for clinical parameters are in high accordance when CV (coefficient of variation), and CVSL (coefficient of variation of cell sides length) are considered. Higher variation was noticed for the H (hexagonality) metric. Utilisation of the improvement framework assured better repeatability of precise endothelial cell border location between the methods while diminishing the dispersion of clinical parameter values calculated for such images. Finally, it was proven statistically that the image processing method applied for endothelial cell analysis does not influence the ability to differentiate between the images using medical parameters.

3D Object Reconstruction from Parallel Cross-Sections

The paper presents a novel approach to the contour stitching method which improves the reconstruction of 3D objects from parallel contours. The innovation assumes the construction of outer and inner contour from many contours on one cross-section in case of branching. This exchange allows easy one to one connection, between contours on adjoining cross-sections, which has already been defined. Additionally, such a definition permits from artificial holes generation. Moreover, the method takes care of smooth surface reconstruction in case of concave and convex shapes. The performance of the novel algorithm has been tested on the set of artificial data as well as on the set of data gathered from CT scans and proved it to work well.

CAS: Cell Annotation Software – Research on Neuronal Tissue Has Never Been so Transparent

CAS (Cell Annotation Software) is a novel tool for analysis of microscopic images and selection of the cell soma or nucleus, depending on the research objectives in medicine, biology, bioinformatics, etc. It replaces time-consuming and tiresome manual analysis of single images not only with automatic methods for object segmentation based on the Statistical Dominance Algorithm, but also semi-automatic tools for object selection within a marked region of interest. For each image, a broad set of object parameters is computed, including shape features and optical and topographic characteristics, thus giving additional insight into data. Our solution for cell detection and analysis has been verified by microscopic data and its application in the annotation of the lateral geniculate nucleus has been examined in a case study.

PCA Application in Classification of Smiling and Neutral Facial Displays

Psychologists claim that the majority of inter-human communication is transferred non-verbally. The face and its expressions are the most significant channel of this kind of communication. In this research the problem of recognition between smiling and neutral facial display is investigated. The set-up consisting of proper local binary pattern operator supported with an image division schema and PCA for feature vector length diminishing is presented. The achieved results using k-nearest neighbourhood classifier are compared on a couple of image datasets to prove successful application of this approach.

Power LBP: A Novel Texture Operator for Smiling and Neutral Facial Display Classification☆

Abstract Texture operators are commonly used to describe image content for many purposes. Recently they found its application in the task of emotion recognition, especially using local binary patterns method, LBP. This paper introduces a novel texture operator called power LBP, which defines a new ordering schema based on absolute intensity differences. Its definition as well as interpretation are given. The performance of suggested solution is evaluated on the problem of smiling and neutral facial display recognition. In order to evaluate the power LBP operator accuracy, its discriminative capacity is compared to several members of the LBP family. Moreover, the influence of applied classification approach is also considered, by presenting results for k-nearest neighbour, support vector machine, and template matching classifiers. Furthermore, results for several databases are compared.

In Search of Truth: Analysis of Smile Intensity Dynamics to Detect Deception

Detection of deceptive facial expressions, including estimating smile genuineness, is an important and challenging research topic that draws increasing attention from the computer vision and pattern recognition community. The state-of-the-art methods require localizing a number of facial landmarks to extract sophisticated facial characteristics. In this paper, we explore how to exploit fast smile intensity detectors to extract temporal features. This allows for real-time discrimination between posed and spontaneous expressions at the early smile onset phase. We report the results of experimental validation, which indicate high competitiveness of our method for the UvA-NEMO benchmark database.

Database Application in Visualization of Process Data

An information system for visualization of Underground Coal Gasification (UCG) has been developed. The main goal for the system is to provide means for better understanding and control of the process. The system uses mainly data generated during UCG process numerical simulation. Apart from that, the system is capable of storing and visualizing measurement georadar data, gathered during in-situ processes. Development software consists of three modules: the data acquisition, data preprocessing, and data visualization. All modules communicate through a database, the central point in the system architecture. Data from modeling or measurements are stored in database in raw format. Then the preprocessing module converts them for visualization purposes. The visualization module shows preprocessed data to the user using 3D graphics. The visualization is dynamic (visualized data changes in time) and allows observing several properties of the process - gases concentration, gasification cavern development, and so on. The software was developed using .NET framework and XNA Game Studio 4.0 library.

Grey Scale Texture Classification Method Comparison Considering Object and Lighting Rotation

This study addresses the statistical texture features as methods for texture classification. It compares its performance on two benchmark data sets: Brodatz and PID, which permit to gain better understanding how those methods deal with texture rotation and lighting changes. Moreover, few simple feature techniques are introduced in order to compare their performance with those already known (e.g. first order features, co-occurrence matrix, run length matrix, grey-tone difference matrix, local binary pattern). Finally, exploiting structures designed in methods like co-occurrence matrices as a feature vector is suggested. The gathered results show the correct classification ratio in range of 92-100%. However, worse performance is noticeable on data set with changing lighting conditions. Moreover, the experiments prove that the introduced simple techniques classify with similar accuracy as well known methods. It is also interesting that exploiting the structures as feature vector proved to improve the classification results. Additionally, due to lower classification calculation complexity the feature vectors length have been diminished with the application of principal component analysis. This experiments showed that exploiting 95% of original information considerably reduces the feature vector length and do not influences the correct classification ratio of all tested methods.

A Machine-Learning Approach to the Automated Assessment of Joint Synovitis Activity

Medical ultrasound imaging is an important tool in diagnosing and monitoring synovitis, which is an inflammation of the synovial membrane that surrounds a joint. Ultrasound images are examined by medical experts to assess the presence and progression of synovitis. Automating image analysis reduces the costs and increases the availability of the ultrasound diagnosis of synovitis and diminishes or eliminates subjective discrepancies. This article describes research that is concerned with the problem of the automatic estimation of the state of the activity of finger joint inflammation using the information that is present in ultrasonography imaging.

Advanced Oblique Rule Generating Based on PCA

In the paper a new algorithm of oblique decision rule induction is presented. It starts from dividing classes into subclasses which is a clustering problem. Then, around each subclass the hyperrectangle is built, which edges are parallel to PCA determined directions. Each hyperrectangle represents single decision rule which conditions are hyperplanes containing hyperrectangle sides. In order to simplify the obtained model less important conditions are removed from the rule and then less important variables are also eliminated from the hyperplane equations. The algorithm was applied for real and artificial datasets.