Identification of tumor epithelium and stroma in tissue microarrays using texture analysis

<p>Abstract</p> <p>Background</p> <p>The aim of the study was to assess whether texture analysis is feasible for automated identification of epithelium and stroma in digitized tumor tissue microarrays (TMAs). Texture analysis based on local binary patterns (LBP) has previously been used successfully in applications such as face recognition and industrial machine vision. TMAs with tissue samples from 643 patients with colorectal cancer were digitized using a whole slide scanner and areas representing epithelium and stroma were annotated in the images. Well-defined images of epithelium (n = 41) and stroma (n = 39) were used for training a support vector machine (SVM) classifier with LBP texture features and a contrast measure C (LBP/C) as input. We optimized the classifier on a validation set (n = 576) and then assessed its performance on an independent test set of images (n = 720). Finally, the performance of the LBP/C classifier was evaluated against classifiers based on Haralick texture features and Gabor filtered images.</p> <p>Results</p> <p>The proposed approach using LPB/C texture features was able to correctly differentiate epithelium from stroma according to texture: the agreement between the classifier and the human observer was 97 per cent (kappa value = 0.934, <it>P </it>< 0.0001) and the accuracy (area under the ROC curve) of the LBP/C classifier was 0.995 (CI95% 0.991-0.998). The accuracy of the corresponding classifiers based on Haralick features and Gabor-filter images were 0.976 and 0.981 respectively.</p> <p>Conclusions</p> <p>The method illustrates the capability of automated segmentation of epithelial and stromal tissue in TMAs based on texture features and an SVM classifier. Applications include tissue specific assessment of gene and protein expression, as well as computerized analysis of the tumor microenvironment.</p> <p>Virtual slides</p> <p>The virtual slide(s) for this article can be found here: <url>http://www.diagnosticpathology.diagnomx.eu/vs/4123422336534537</url></p

Automatic delimitation of the clinical region of interest in ultra-wide field of view images of the retina

Retinal ultra-wide field of view images (fundus images) provides the visu-alization of a large part of the retina though, artifacts may appear in those images. Eyelashes and eyelids often cover the clinical region of interest and worse, eye-lashes can be mistaken with arteries and/or veins when those images are put through automatic diagnosis or segmentation software creating, in those cases, the appearance of false positives results. Correcting this problem, the first step in the development of qualified auto-matic diseases diagnosis programs can be done and in that way the development of an objective tool to assess diseases eradicating the human error from those processes can also be achieved. In this work the development of a tool that automatically delimitates the clinical region of interest is proposed by retrieving features from the images that will be analyzed by an automatic classifier. This automatic classifier will evaluate the information and will decide which part of the image is of interest and which part contains artifacts. The results were validated by implementing a software in C# language and validated through a statistical analysis. From those results it was confirmed that the methodology presented is capable of detecting artifacts and selecting the clin-ical region of interest in fundus images of the retina

Automated Image Analysis of Cancer Tissue Adapted for Virtual Microscopy

Emerging large-scale digitization of microscopic tissue samples (i.e. virtual microscopy) in biomarker research and clinical pathology enables rapid, objective and repeatable computational analysis of the images. Automated image analysis is likely to be especially useful in personalized medicine, where high-throughput analysis is required for risk prediction, advanced diagnostics and targeted treatment of patients. Malignant tumors are profiled in detail to identify clinically relevant mutations and aberrant protein expression levels. Human observers are still predominantly visually interpreting the increasing number of biomarker assays with fluorescence in situ hybridization (FISH) and immunohistochemical (IHC) stainings. To aid in these quantification tasks, novel applications for automated image analysis of cancer tissues are needed. Virtual microscopy samples require large digital storage space, and image size reduction techniques should be addressed prior to archiving of the images. In this thesis, tools for high-throughput biomarker research in a digital microscopy environment were developed, assessed and adapted to a virtual microscopy setting. The first algorithm developed is intended for automated quantitative assessment of FISH signals to determine the HER2 gene amplification status in breast cancer tissue images, and proved to be comparable to visual scoring. The extent of Ki-67 staining determined in breast cancer tissue images by the second automated algorithm was a significant predictor of patient outcome in both uni- and multivariate analyses. The third algorithm for automated segmentation of tissue images divided the colorectal cancer images into epithelial and stromal compartments with high accuracy. In addition, image compression and scaling led to significant reductions in image sizes without compromising the results of the second and third algorithms introduced previously. The algorithms developed in this thesis are freely accessible to be used by the research community, facilitating external validation of the algorithms. After further validation studies, the algorithms can potentially be applied in clinical pathology especially within diagnostics, risk prediction and targeted treatment of cancer patients in a personalized medicine setting.Patologin työ sisältää runsaasti mikroskooppinäytteiden tulkintaa. Perinteisesti näytteitä on katsottu tavallisella mikroskoopilla, ja näytteistä tarvittavat merkkiaineiden laskennat on tehty silmämääräisesti. Varsinkin laajojen tutkimusaineistojen tulkintaan tarvitaan vaihtoehtoinen menetelmä, sillä työ vaatii paljon aikaa, ja patologien määrä on rajallinen. Tietokoneiden ja digitaalisen kuvantamisen kehittyminen on mahdollistanut virtuaalimikroskopian eli mikroskooppinäytteiden kuvaamisen kokonaisuudessaan suurella tarkkuudella. Kuvattuja näytteitä voidaan katsella internetin välityksellä tavallisella verkkoselaimella. Digitoidut mikroskooppinäytteet mahdollistavat myös kuvien automaattisen tulkinnan tietokoneavusteisella konenäöllä. Yksi virtuaalimikroskopian suurimmista haasteista on kuvien vaatima suuri tallennustila. Tässä väitöskirjassa kehitettiin kolme erilaista menetelmää digitoitujen mikroskooppinäytteiden automaattiseen tulkintaan. Tavoitteena oli päästä patologin tulkintaa vastaaviin tuloksiin. Näytteet olivat peräisin laajoista rinta- ja paksusuolisyöpäsarjoista, ja potilaista oli saatavilla taustatiedot pitkältä seuranta-ajalta. Patologin tekemiä laskentoja verrattiin tilastollisesti konenäön tuottamiin tuloksiin. Väitöskirjatyössä tutkittiin myös kuvapakkauksen ja -skaalauksen vaikutusta konenäön antamiin tuloksiin. Kehitetyt konenäkömenetelmät kykenivät toistamaan luotettavasti patologin antamien laskentojen tulokset. Yksi menetelmistä kykeni jopa ennustamaan potilaan selviytymistä paremmin kuin patologin laskennan perusteella voitiin ennustaa. Konenäkömenetelmät sietivät kohtalaisen suurta kuvapakkausta ja -skaalausta ilman menetelmien tuottamien tulosten heikkenemistä. Väitöskirja osoittaa, että esitetyt automaattiset konenäkömenetelmät ovat luotettavia ja niillä voidaan korvata työläitä silmämääräisiä laskentoja tutkimuskäytössä. Menetelmät pitää vielä luotettavasti validoida jatkotutkimuksissa, jotta niitä voitaisiin hyödyntää myös potilastyössä. Menetelmien avulla voidaan kohdentaa rajallisia patologiresursseja työläistä laskennoista muualle. Konenäkö työskentelee väsymättömästi, joten esimerkiksi yöajat voidaan hyödyntää mikroskooppikuvien automaattiseen tulkintaan. Virtuaalimikroskopian vaatimaa tallennustilaa tietokoneilla voidaan vähentää käyttämällä kuvapakkausta ja -skaalausta, joiden ei todettu heikentävän tässä väitöskirjassa esiteltyjen konenäkömenetelmien toimintaa

Pupil and iris center detection

Tato práce se zabývá zpracováním šedotónových snímků oka za účelem nalezení středu zornice a duhovky, pupilární a limbické hranice. Úvodní část je rešerší na téma pupila a duhovka a změna rozměrů pupily. Jsou tu uvedeny články zabývající se problematikou detekce středu pupily a duhovky, které byly použity jako inspirace pro realizaci vlastních metod v této práci. Úvodní část končí popisem navržených metod, které detekovaly parametry pupily a duhovky ve snímcích databáze CASIA-Iris-Interval. Druhá, praktická část práce, je věnována popisu snímacího systému, který byl sestaven pro získání vlastních snímků oka. Následuje rozbor jednotlivých metod, které byly použity pro rozměření parametrů pupily a duhovky. Jedná se celkem o dvě metody detekce středu pupily a pupilární hranice, jednu metodu určující střed duhovky a limbickou hranici a jednu metodu, která detekuje paramtry pupily a duhovky zároveň. Konec práce je věnován rozboru výsledků jednotlivých metod včetně úspěšnosti detekce a statistické analýzy. Součástí rozboru je také zkoumání změny pupilárního poloměru v závislosti na světelných podmínkách a změny pozice středu pupily při konstrikci a dilataci pupily.This thesis is aimed at image processing to obtain informations about the center of pupil and iris,the pupillar and limbic boundary from grayscale images of the eye. An introduction describes general research in topic of pupil, iris and changes in size of pupil. There are mentioned articles dealing with the detection of the center of pupil and iris. These articles were used as an inspiration for proposal of detection methods used in this work. The introduction ends with description of designed approaches of detection of parameters of pupil and iris in images of CASIA-Iris-Interval database. The second, practical part of this work, is dedicated to description of acquisition system, which was compiled to obtain proper images of eye. Individual analysis of methods used to obtain parameters of pupil and iris follows after that. There are two methods of detection of center of pupil and pupillar boundary, one method determines center of iris and limbic boundary. Last method detects centers and boundaries of pupil and iris at one time. The end of this work is dedicated to analysis of results of designed methods. Analysis includes score of successfulness of detection, statistical analysis, discussion of changes in pupillary diameter depending on the light conditions and changes in position of center of pupil during constriction and dilatation of pupil.

Human-Centric Machine Vision

Recently, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, and human machine interface. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans

Análisis comparativo de algoritmos en segmentación de iris

El objetivo principal de este proyecto es la consecución de un análisis comparativo, realizado a partir de un conjunto de algoritmos implementados expresamente para su comparación y extracción de conclusiones a partir de los resultados obtenidos. Para ello, el objetivo principal debe cumplir otros objetivos secundarios que permitan la consecución del primero, estos serán: Realizar un diseño software que facilite la implementación de los algoritmos; Implementar los distintos algoritmos propuestos; Comprobar la fiabilidad y calidad de cada algoritmo; Extraer conclusiones a partir de los datos obtenidos de la evaluación de cada algoritmo. El proyecto se centrará en la segmentación de iris, resaltando la importancia de esta fase en el reconocimiento de iris y explicando la necesidad de su utilización. Tomará un conjunto de algoritmos y los descompondrá, realizando un estudio pormenorizado de cada uno de ellos, comparándolos para resaltar sus principales virtudes y defectos.Ingeniería en Informátic