Anna Wróblewska

A Two-Step Method for Detection of Architectural Distortions in Mammograms

Early detection of breast cancer is very important. It increases breast cancer treatment and reduces mortality rates by 30 - 70%. Architectural distortion (AD) is one of the commonly missed signs of breast cancer. It is estimated that 12 - 45% of missed breast cancer in mammography are ADs. Our ultimate goal is to develop a CAD module through creating of ADs detection method (ArDist method). We rely on fact that AD is a group of line structures of different orientation. The ArDist method consists of two stages: detection of ROI with potential ADs based on analysis with Gabor filters (GF method) and recognition of ADs using 2D Fourier transform in polar coordinates (DD method). The method was tested with 33 mammograms containing ADs, from the database DDSM. Experimental results are promising in comparison with the results of the model method and the efficiency of commercial CAD systems. The sensitivity of ArDist method amounts to 68% with 0.86 false positives per image.

Semantic rules representation in controlled natural language in FluentEditor

This paper presents a way of representation of semantic rules (SWRL) in controlled English in order to facilitate understanding the rules by humans interacting with a machine. This approach (implemented in FluentEditor) may be applied in many domains, where the understandability of the rules used to support a decision process is of great importance.

SYNAT System Ontology: Design Patterns Applied to Modeling of Scientific Community, Preliminary Model Evaluation

The paper presents the extended version of the SYNAT system ontology, used design patterns, modeling choices and preliminary evaluation of the model. SYNAT system ontology was designed to define semantic scope of the SYNAT platform. It covers concepts related to scientific community and its activities i.e.: people in science and their activities, scientific and sciencerelated documents, academic and non-academic organizations, scientific events and data resources, geographic notions necessary to characterize facts about science as well as classification of scientific topics. In its current version SYNAT system ontology counts 472 classes and 296 properties, its consistency was verified using Pellet and HermiT reasoners.

Methods and Tools for Ontology Building, Learning and Integration – Application in the SYNAT Project

One of the main goals of the SYNAT project is to equip scientific community with a knowledge-based infrastructure providing fast access to relevant scientific information. We have started building an experimental platform where different kinds of stored knowledge will be modeled with the use of ontologies, e.g. reference/system ontology, domain ontologies and auxiliary knowledge including lexical language ontology layers. In our platform we use system ontology defining “system domain” (a kind of meta knowledge) for the scientific community, covering concepts and activities related to the scientific life and domain ontologies dedicated to specific areas of science. Moreover the platform is supposed to include a wide range of tools for building and maintenance of ontologies throughout their life cycle as well as interoperation among the different introduced ontologies.

Associations between Texts and Ontology

Intelligent automatic text processing methods require linking between texts (written in natural languages) and concepts representing semantics of particular words and phrases. Concepts are supposed to be independent from languages and can be expressed as ontologies. The mapping of concepts to a specific language may be expressed as a linguistic layer of a given ontology. The paper presents a model of lexical layer that establishes relations between terms (words or phrases) and entities of a given ontology. Moreover, the layer contains representations not only of terms but also their contexts of usage. It also provides associations with commonly used lexical knowledge resources, such as WordNet, Wikipedia and DBPedia.

Ordering of diagnostic information in encoded medical images. Accuracy progression

A concept of diagnostic accuracy progression for embedded coding of medical images was presented. Implementation of JPEG2000 encoder with a modified PCRD optimization algorithm was realized and initially verified as a tool for accurate medical image streaming. Mean square error as a distortion measure was replaced by other numerical measures to revise quality progression according to diagnostic importance of successively encoded image information. A faster increment of image diagnostic importance during reconstruction of initial packets of code stream was reached. Modified Jasper code was initially tested on a set of mammograms containing clusters of microcalcifications and malignant masses, and other radiograms. Teleradiologic applications were considered as the first area of interests.

Statistical analysis of the reduction process of graphene oxide probed by Raman spectroscopy mapping

We propose a method for monitoring the large-scale homogeneity of the reduction process of graphene oxide. For this purpose, a Raman mapping technique is employed to probe the evolution of the phonon properties of two different graphene oxide (GO) thin films upon controllable thermal reduction. The reduction of GO is reflected by the upshift of the statistical distribution of the relative intensity ratio of the G and D peaks (I D/I G) of the Raman spectra and is consistent with the ratio obtained for chemically reduced GO. In addition, the shifts of the position distributions of the main Raman modes ([Formula: see text], [Formula: see text]) and their cross-correlation with the I D/I G ratio provides evidence of a change of the doping level, demonstrating the influence of reduction processes on GO films.

Automatic Clustering Methods of Offers in an E-Commerce Marketplace

This work describes fully automatic clustering methods of offers in an e-commerce marketplace. Three different grouping approaches are proposed. We also designed and applied quality measures of clustering based on user-generated events. We assessed the proposed methods of clustering and compared them.

Innovative UV sensor based on highly birefringent fiber covered by graphene oxide

The paper presents the way that colour can serve solving the problem of calibration points indexing in a camera geometrical calibration process. We propose a technique in which indexes of calibration points in a black-and-white chessboard are represented as sets of colour regions in the neighbourhood of calibration points. We provide some general rules for designing a colour calibration chessboard and provide a method of calibration image analysis. We show that this approach leads to obtaining better results than in the case of widely used methods employing information about already indexed points to compute indexes. We also report constraints concerning the technique. Nowadays we are witnessing an increasing need for camera geometrical calibration systems. They are vital for such applications as 3D modelling, 3D reconstruction, assembly control systems, etc. Wherever possible, calibration objects placed in the scene are used in a camera geometrical calibration process. This approach significantly increases accuracy of calibration results and makes the calibration data extraction process easier and universal. There are many geometrical camera calibration techniques for a known calibration scene [1]. A great number of them use as an input calibration points which are localised and indexed in the scene. In this paper we propose the technique of calibration points indexing which uses a colour chessboard. The presented technique was developed by solving problems we encountered during experiments with our earlier methods of camera calibration scene analysis [2]-[3]. In particular, the proposed technique increases the number of indexed points points in case of local lack of calibration points detection. At the beginning of the paper we present a way of designing a chessboard pattern. Then we describe a calibration point indexing method, and finally we show experimental results. A black-and-white chessboard is widely used in order to obtain sub-pixel accuracy of calibration points localisation [1]. Calibration points are defined as corners of chessboard squares. Assuming the availability of rough localisation of these points, the points can be indexed. Noting that differences in distances between neighbouring points in calibration scene images differ slightly, one of the local searching methods can be employed (e.g. [2]). Methods of this type search for a calibration point to be indexed, using a window of a certain size. The position of the window is determined by a vector representing the distance between two previously indexed points in the same row or column. However, experiments show that this approach has its disadvantages, as described below. * E-mail: A.Nowakowski@ire.pw.edu.pl Firstly, there is a danger of omitting some points during indexing in case of local lack of calibration points detection in a neighbourhood (e.g. caused by the presence of non-homogeneous light in the calibration scene). A particularly unfavourable situation is when the local lack of detection effects in the appearance of separated regions of detected calibration points. It is worth saying that such situations are likely to happen for calibration points situated near image borders. Such points are very important for the analysis of optical nonlinearities, and a lack of them can significantly influence the accuracy of distortion modelling. Secondly, such methods may give wrong results in the case of optical distortion with strong nonlinearities when getting information about the neighbouring index is not an easy task. Beside this, the methods are very sensitive to a single false localisation of a calibration point. Such a single false localisation can even result in false indexing of a big set of calibration points. To avoid the above-mentioned problems, we propose using a black-and-white chessboard which contains the coded index of a calibration point in the form of colour squares situated in the nearest neighbourhood of each point. The index of a certain calibration point is determined by colours of four nearest neighbouring squares (Fig.1). An order of squares in such foursome is important. Because the size of a colour square is determined only by the possibility of correct colour detection, the size of a colour square can be smaller than the size of a black or white square. The larger size of a black or white square is determined by the requirements of the exact localisation step which follows the indexing of calibration points [3]. In this step, edge information is extracted from a blackand-white chessboard. This edge information needs larger Artur Nowakowski, Wladyslaw Skarbek Institute of Radioelectronics, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warszawa, A.Nowakowski@ire.pw.edu.pl Received February 10, 2009; accepted March 27, 2009; published March 31, 2009 http://www.photonics.pl/PLP

Lexical ontology layer: a bridge between text and concepts

Intelligent methods for automatic text processing require linking between lexical resources (texts) and ontologies that define semantics. However, one of the main problems is that while building ontologies, the main effort is put to the construction of the conceptual part, whereas the lexical aspects of ontologies are usually diminished. Therefore, analyzing texts, it is usually difficult to map words to concepts from the ontology. Usually one should consider various linguistic relationships, such as homonymy, synonymy, etc. However, they are not clearly reflected in the conceptual part. We propose LEXO - a special lexical layer, which is thought as a bridge between text and the conceptual core of the ontology. LEXO is dedicated to storing linguistic relationships along with textual evidence for the relationships (as discovered in the text mining process). In addition, we present an algorithm based on LEXO for determining meaning of a given term in an analyzed text.