Microcalcification evaluation in computer assisted diagnosis for digital mammography

In order to develop applications for z;isual interpretation of medical images, the early detection and evaluation of microcalcifications in digital mammograms is verg important since their presence is oftenassociated with a high incidence of breast cancers. Accurate classification into benign and malignant groups would help improve diagnostic sensitivity as well as reduce the number of unnecessa y biopsies. The challenge here is the selection of the useful features to distinguish benign from malignant micro calcifications. Our purpose in this work is to analyse a microcalcification evaluation method based on a set of shapebased features extracted from the digitised mammography. The segmentation of the microcalcificationsis performed using a fixed-tolerance region growing method to extract boundaries of calcifications with manually selected seed pixels. Taking into account that shapes and sizes of clustered microcalcificationshave been associated with a high risk of carcinoma based on digerent subjective measures, such as whether or not the calcifications are irregular, linear, vermiform, branched, rounded or ring like, our efforts were addressed to obtain a feature set related to the shape. The identification of the pammeters concerning the malignant character of the microcalcifications was performed on a set of 146 mammograms with their real diagnosis known in advance from biopsies. This allowed identifying the following shape-based parameters as the relevant ones: Number of clusters, Number of holes, Area, Feret elongation, Roughness, and Elongation. Further experiments on a set of 70 new mammogmms showed that the performance of the classification scheme is close to the mean performance of three expert radiologists, which allows to consider the proposed method for assisting the diagnosis and encourages to continue the investigation in the senseof adding new features not only related to the shape

Using appearance and context for outdoor scene object classification

We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal

A concurrent region growing algorithm guided by circumscribed contours

Image segmentation of natural scenes constitutes a major problem in machine vision. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. This approach begins by detecting the main contours of the scene which are later used to guide a concurrent set of growing processes. A previous analysis of the seed pixels permits adjustment of the homogeneity criterion to the region's characteristics during the growing process. Since the high variability of regions representing outdoor scenes makes the classical homogeneity criteria useless, a new homogeneity criterion based on clustering analysis and convex hull construction is proposed. Experimental results have proven the reliability of the proposed approach

Copy number variations of colorectal cancer by whole exome sequencing data

Colorectal cancer (CRC) is the third most common cancer and the fourth leading cause of cancer death worldwide. About 85% of the cases of CRC are known to have chromosomal instability, an allelic imbalance at several chromosomal loci, and chromosome amplification and translocation. The aim of this study is to determine the recurrent copy number variant (CNV) regions present in stage II of CRC through whole exome sequencing, a rapidly developing targeted next-generation sequencing (NGS) technology that provides an accurate alternative approach for accessing genomic variations. 42 normal-tumor paired samples were sequenced by Illumina Genome Analyzer. Data was analyzed with Varscan2 and segmentation was performed with R package R-GADA. Summary of the segments across all samples was performed and the result was overlapped with DEG data of the same samples from a previous study in the group1. Major and more recurrent segments of CNV were: gain of chromosome 7pq(13%), 13q(31%) and 20q(75%) and loss of 8p(25%), 17p(23%), and 18pq(27%). This results are coincident with the known literature of CNV in CRC or other cancers, but our methodology should be validated by array comparative genomic hybridisation (aCGH) profiling, which is currently the gold standard for genetic diagnosis of CNV.

Asiakkuuksien strateginen arvottaminen ja luokittelu

Tämän tutkimuksen kohdeorganisaatio on suuren teollisuusyrityksen sisäinen raaka-aineen hankkija ja toimittaja. Tutkimuksessa selvitetään, mistä kohdeorganisaation hankinta-asiakkuuksien arvo muodostuu ja kuinka olemassa olevan liiketoimintadatan perusteella voidaan tutkia, arvioida ja luokitella kauppojen ja asiakkuuksien arvokkuutta aikaan sitomatta, objektiivisesti ja luotettavasti. Tutkimuksen teoriaosiossa esitellään lähestymistapoja ja menetelmiä, joiden avulla voidaan jalostaa olemassa olevasta datasta uutta sidosryhmätietämystä liiketoiminnan käyttöön, sekä tarkastellaan asiakaskannattavuusanalyysin, portfolioanalyysin, sekä asiakassegmentoinnin perusteita ja malleja. Näiden teorioiden ja mallien pohjalta rakennetaan kohdeorganisaatiolle räätälöity, indeksoituihin hinta-, määrä- ja kauppojen toistuvuus-muuttujiin perustuva, asiakkuuksien arvottamis- ja luokittelumalli. Arvottamis- ja luokittelumalli testataan vuosien 2003–2007 liiketoimintadatasta muodostetulla 389 336 kaupparivin otoksella, joka sisältää 42 186 arvioitavaa asiakkuussuhdetta. Merkittävin esille nouseva havainto on noin 5 000:n keskimääräistä selkeästi kalliimman asiakkuuden ryhmä. Aineisto ja sen poikkeavuudet testataan tilastollisin menetelmin, jotta saadaan selville asiakkuuden arvoon vaikuttavat ja arvoa selittävät tekijät. Lopuksi pohditaan arvottamismallin merkitystä analyyttisemman ostotoiminnan ja asiakkuudenhallinnan välineenä, sekä esitetään muutamia parannusehdotuksia.

IT-alan pk-yrityksen liiketoimintaympäristön analyysi ja markkinointistrategia

Taloudellisen laman aikana monet yritykset pyrkivät tehostamaan omaa toimintaa tarkastelemalla uudelleen itselleen tärkeitä liiketoiminnan prosesseja. Tässä diplomityössä suoritetaan IT-alan pk-yrityksen liiketoimintaympäristön analyysi käyttäen SWOT- työkalua ja Porterin viiden voiman mallia. Tämän jälkeen selvitetään Yritys Oy:n asiakastarpeita sekä yrityksen tarjoomaa. Tämän jälkeen suoritetaan potentiaalisten asiakkaiden segmentointi. Jokaiselle segmentille muodostetaan oma tarjooma ja asemoidaan se. Asemointi tapahtuu muodostamalla jokaiselle segmentille omaa sanomaa, joka perustuu segmentin tarjoomaan. Segmentointi pyritään toteuttamaan asiakaslähtöisesti. Työn lopussa muodostetaan Yritys Oy:lle markkinointistrategia.

Hankinnan ja toimittajien suorituskyvyn kehittäminen

Tutkimuksen tavoitteena on luoda yksinkertaistettu malli, jota voidaan hyödyntää olemassa olevien toimittajien suorituskyvyn analysoinnissa ja sen perusteella tehtävässä kehitystyön pohjana. Tutkimuksen case-osuuden tavoitteena on testata mallin toimivuus ja käyttää sitä toimittajayhteistyön kehittämisessä. Tutkimuksen teoriaosuudessa esitellään ennustamiseen, hankintaan, hankintaprosessiin, toimittajahallintaan, suorituskykyyn ja toimittajayhteistyöhön liittyviä näkökohtia ja menetelmiä. Toimittajahallinnan tärkeänä näkökohtana on toimittajien analysointi ja segmentointi niiden tärkeyden ja kriittisyyden perusteella. Hankinnan suorituskyvyllä on tärkeä merkitys yrityksen tavoitteiden saavuttamisessa. Toimittajien suorituskyky vaikuttaa hankinnan suorituskykyyn ja luo tarpeen toimittajayhteistyön kehittämiselle. Kommunikointi ja sitoutuminen ovat tärkeät näkökohdat hankinnan ja toimittajan välisen yhteistyön kehittämisessä. Yhteistyötä voidaan kehittää tutkimuksessa luodun toimittajayhteistyön analysoinnin ja kehittämisen –mallin avulla. Johtopäätöksenä voidaan todeta, että yritykselle on erittäin tärkeää tehdä yhteistyötä tärkeiden ja kriittisten toimittajien kanssa suorituskyvyn parantamiseksi. Hankinta voi saavuttaa omat tavoitteensa tekemällä yhteistyötä toimittajiensa kanssa ja tällöin se edesauttaa koko yritystä saavuttamaan tavoitteensa. Toimittajayhteistyön analysoinnin ja kehittämisen -mallin avulla kehitystyöstä tulee järjestelmällistä ja vaikuttavaa. Se auttaa hankintaa ja toimittajaa ymmärtämään kehitystyön tärkeys ja merkitys.

Data mining and mall users profile

Marketing scholars have suggested a need for more empirical research on consumer response to malls, in order to have a better understanding of the variables that explain the behavior of the consumers. The segmentation methodology CHAID (Chi-square automatic interaction detection) was used in order to identify the profiles of consumers with regard to their activities at malls, on the basis of socio-demographic variables and behavioral variables (how and with whom they go to the malls). A sample of 790 subjects answered an online questionnaire. The CHAID analysis of the results was used to identify the profiles of consumers with regard to their activities at malls. In the set of variables analyzed the transport used in order to go shopping and the frequency of visits to centers are the main predictors of behavior in malls. The results provide guidelines for the development of effective strategies to attract consumers to malls and retain them there.

The economic value of reciprocal bilingualism

Some bilingual societies exhibit a distribution of language skills that can- not be explained by economic theories that portray languages as pure commu- nication devices. Such distribution of skills are typically the result of public policies that promote bilingualism among members of both speech commu- nities (reciprocal bilingualism). In this paper I argue that these policies are likely to increase social welfare by diminishing economic and social segmenta- tion between the two communities. However, these gains tend to be unequally distributed over the two communities. As a result, in a large range of circum- stances these policies might not draw su¢ cient support. The model is built upon the communicative value of languages, but also emphasizes the role of linguistic preferences in the behavior of bilingual individuals.

Reconeixement de gestos de la mà amb el sensor Kinect

El reconeixement dels gestos de la mà (HGR, Hand Gesture Recognition) és actualment un camp important de recerca degut a la varietat de situacions en les quals és necessari comunicar-se mitjançant signes, com pot ser la comunicació entre persones que utilitzen la llengua de signes i les que no. En aquest projecte es presenta un mètode de reconeixement de gestos de la mà a temps real utilitzant el sensor Kinect per Microsoft Xbox, implementat en un entorn Linux (Ubuntu) amb llenguatge de programació Python i utilitzant la llibreria de visió artifical OpenCV per a processar les dades sobre un ordinador portàtil convencional. Gràcies a la capacitat del sensor Kinect de capturar dades de profunditat d’una escena es poden determinar les posicions i trajectòries dels objectes en 3 dimensions, el que implica poder realitzar una anàlisi complerta a temps real d’una imatge o d’una seqüencia d’imatges. El procediment de reconeixement que es planteja es basa en la segmentació de la imatge per poder treballar únicament amb la mà, en la detecció dels contorns, per després obtenir l’envolupant convexa i els defectes convexos, que finalment han de servir per determinar el nombre de dits i concloure en la interpretació del gest; el resultat final és la transcripció del seu significat en una finestra que serveix d’interfície amb l’interlocutor. L’aplicació permet reconèixer els números del 0 al 5, ja que s’analitza únicament una mà, alguns gestos populars i algunes de les lletres de l’alfabet dactilològic de la llengua de signes catalana. El projecte és doncs, la porta d’entrada al camp del reconeixement de gestos i la base d’un futur sistema de reconeixement de la llengua de signes capaç de transcriure tant els signes dinàmics com l’alfabet dactilològic.

Machine Learning and Clinical Text. Supporting Health Information Flow

Fluent health information flow is critical for clinical decision-making. However, a considerable part of this information is free-form text and inabilities to utilize it create risks to patient safety and cost-­effective hospital administration. Methods for automated processing of clinical text are emerging. The aim in this doctoral dissertation is to study machine learning and clinical text in order to support health information flow.First, by analyzing the content of authentic patient records, the aim is to specify clinical needs in order to guide the development of machine learning applications.The contributions are a model of the ideal information flow,a model of the problems and challenges in reality, and a road map for the technology development. Second, by developing applications for practical cases,the aim is to concretize ways to support health information flow. Altogether five machine learning applications for three practical cases are described: The first two applications are binary classification and regression related to the practical case of topic labeling and relevance ranking.The third and fourth application are supervised and unsupervised multi-class classification for the practical case of topic segmentation and labeling.These four applications are tested with Finnish intensive care patient records.The fifth application is multi-label classification for the practical task of diagnosis coding. It is tested with English radiology reports.The performance of all these applications is promising. Third, the aim is to study how the quality of machine learning applications can be reliably evaluated.The associations between performance evaluation measures and methods are addressed,and a new hold-out method is introduced.This method contributes not only to processing time but also to the evaluation diversity and quality. The main conclusion is that developing machine learning applications for text requires interdisciplinary, international collaboration. Practical cases are very different, and hence the development must begin from genuine user needs and domain expertise. The technological expertise must cover linguistics,machine learning, and information systems. Finally, the methods must be evaluated both statistically and through authentic user-feedback.

Production Cycle Optimization: Defining the Market Potential for a New Planning Tool

An optimization tool has been developed to help companies to optimize their production cycles and thus improve their overall supply chain management processes. The application combines the functionality that traditional APS (Advanced Planning System) and ARP (Automatic Replenishment Program) systems provide into one optimization run. A qualitative study was organized to investigate opportunities to expand the product’s market base. Twelve personal interviews were conducted and the results were collected in industry specific production planning analyses. Five process industries were analyzed to identify the product’s suitability to each industry sector and the most important product development areas. Based on the research the paper and the plastic film industries remain the most potential industry sectors at this point. To be successful in other industry sectors some product enhancements would be required, including capabilities to optimize multiple sequential and parallel production cycles, handle sequencing of complex finishing operations and to include master planning capabilities to support overall supply chain optimization. In product sales and marketing processes the key to success is to find and reach the people who are involved directly with the problems that the optimization tool can help to solve.

Teknisen auringonsuojauksen tukikonseptin liiketoimintalähtöinen arviointi

Työssä tutkitaan erään tekniseen auringonsuojaukseen keskittyneen yrityksen päätöstä tuottaa aloittelevia yrityksiä palveleva tukikonsepti. Konsepti sisältää mitoitusohjelmia, kokoonpano-ohjeita sekä markkinointimateriaalia. Oleellista on selvittää kannattaako yrityksen keskittyä kyseisiin asiakkaisiin, sekä onko konsepti oikea ratkaisu tälle segmentille. Tätä varten työssä tutustuttiin toimiala-analyysiin, strategiaan, segmentointiin sekä differointiin. Työssä selvitettiin, että Porterin viiden voiman malli on yhä toimiva aloitus toimiala-analyysin laatimiseen. Strategiaan liittyviä koulukuntia löytyy kirjallisuudesta useita erilaisia, oleellista on löytää oikea lähestymistapa kuhunkin tilanteeseen. Segmentointi on tärkeä vaihe, koska sen avulla selvitetään yrityksen eri asiakasryhmiä. Differointi on usein suositeltu keino kilpailuedun tavoittelemiseen. Työtä varten laadittiin yritykselle toimiala-analyysi. Lisäksi yritykselle etsittiin segmenttejä sekä pohdittiin sen mahdollisuuksia differointiin. Segmenttejä löydettiin kolme erilaista. Samalla todettiin, että paras tapa differoida yrityksen tuotteita on keskittyä tuotteiden aineettomiin ominaisuuksiin. Toimiala-analyysin, segmenttien sekä differointimahdollisuuksien perusteella päädyttiin siihen, että yrityksen päätös keskittyä enemmän uusiin ja aloitteleviin asiakkaisiin vaikuttaa järkevältä. Konseptin ominaisuuksien perusteella myös konsepti vaikuttaa hyvältä tavalta palvella kyseistä asiakassegmenttiä.

Comparison Structure Analysis

This study presents an automatic, computer-aided analytical method called Comparison Structure Analysis (CSA), which can be applied to different dimensions of music. The aim of CSA is first and foremost practical: to produce dynamic and understandable representations of musical properties by evaluating the prevalence of a chosen musical data structure through a musical piece. Such a comparison structure may refer to a mathematical vector, a set, a matrix or another type of data structure and even a combination of data structures. CSA depends on an abstract systematic segmentation that allows for a statistical or mathematical survey of the data. To choose a comparison structure is to tune the apparatus to be sensitive to an exclusive set of musical properties. CSA settles somewhere between traditional music analysis and computer aided music information retrieval (MIR). Theoretically defined musical entities, such as pitch-class sets, set-classes and particular rhythm patterns are detected in compositions using pattern extraction and pattern comparison algorithms that are typical within the field of MIR. In principle, the idea of comparison structure analysis can be applied to any time-series type data and, in the music analytical context, to polyphonic as well as homophonic music. Tonal trends, set-class similarities, invertible counterpoints, voice-leading similarities, short-term modulations, rhythmic similarities and multiparametric changes in musical texture were studied. Since CSA allows for a highly accurate classification of compositions, its methods may be applicable to symbolic music information retrieval as well. The strength of CSA relies especially on the possibility to make comparisons between the observations concerning different musical parameters and to combine it with statistical and perhaps other music analytical methods. The results of CSA are dependent on the competence of the similarity measure. New similarity measures for tonal stability, rhythmic and set-class similarity measurements were proposed. The most advanced results were attained by employing the automated function generation – comparable with the so-called genetic programming – to search for an optimal model for set-class similarity measurements. However, the results of CSA seem to agree strongly, independent of the type of similarity function employed in the analysis.

Country Risk and Market Reactions to Earnings Announcements: Evidence from Finnish Stock Markets

The objective of this thesis is to examine the market reaction around earnings announcements in Finnish stock markets. The aim is to find out whether the extreme market conditions during the financial crisis are reflected in stock prices as a stronger reaction. In addition to this, the purpose is to investigate how extensively Finnish listed companies report the country segmentation of revenues in their interim reports and whether the country risk is having a significant impact on perceived market reaction. The sample covers all companies listed in Helsinki stock exchange at 1.1.2010 and these companies’ interim reports from the first quarter of 2008 to last quarter of 2009. Final sample consists of 81 companies and 630 firm-quarter observations. The data sample has been divided in two parts, of which country risk sample contains 17 companies and 127 observations and comparison sample covers 66 companies and 503 observations. Research methodologies applied in this thesis are event study and cross-sectional regression analysis. Empirical results indicate that the market reaction occurs mainly during the announcement day and is slightly stronger in case of positive earnings surprises than the reactions observed in previous studies. In case of negative earnings surprises no significant differences can be observed. In case of country risk sample and negative earnings surprise market reaction is negative already in advance of the disclosure contrary to comparison sample. In case of positive surprise no differences can be observed. Country risk variable developed during this study seems to explain only minor part of the market reaction.