89 research outputs found

    Human Metaphase Chromosome Analysis using Image Processing

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    Development of an effective human metaphase chromosome analysis algorithm can optimize expert time usage by increasing the efficiency of many clinical diagnosis processes. Although many methods exist in the literature, they are only applicable for limited morphological variations and are specific to the staining method used during cell preparation. They are also highly influenced by irregular chromosome boundaries as well as the presence of artifacts such as premature sister chromatid separation. Therefore an algorithm is proposed in this research which can operate with any morphological variation of the chromosome across images from multiple staining methods. The proposed algorithm is capable of calculating the segmentation outline, the centerline (which gives the chromosome length), partitioning of the telomere regions and the centromere location of a given chromosome. The algorithm also detects and corrects for the sister chromatid separation artifact in metaphase cell images. A metric termed the Candidate Based Centromere Confidence (CBCC) is proposed to accompany each centromere detection result of the proposed method, giving an indication of the confidence the algorithm has on a given localization. The proposed method was first tested for the ability of calculating an accurate width profile against a centerline based method [1] using 226 chromosomes. A statistical analysis of the centromere detection error values proved that the proposed method can accurately locate centromere locations with statistical significance. Furthermore, the proposed method performed more consistently across different staining methods in comparison to the centerline based approach. When tested with a larger data set of 1400 chromosomes collected from a set of DAPI (4\u27,6-diamidino-2-phenylindole) and Giemsa stained cell images, the proposed candidate based centromere detection algorithm was able to accurately localize 1220 centromere locations yielding a detection accuracy of 87%

    Constrained Delaunay triangulation for diagnosis and grading of colon cancer

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    Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 93-107.In our century, the increasing rate of cancer incidents makes it inevitable to employ computerized tools that aim to help pathologists more accurately diagnose and grade cancerous tissues. These mathematical tools offer more stable and objective frameworks, which cause a reduced rate of intra- and inter-observer variability. There has been a large set of studies on the subject of automated cancer diagnosis/grading, especially based on textural and/or structural tissue analysis. Although the previous structural approaches show promising results for different types of tissues, they are still unable to make use of the potential information that is provided by tissue components rather than cell nuclei. However, this additional information is one of the major information sources for the tissue types with differentiated components including luminal regions being useful to describe glands in a colon tissue. This thesis introduces a novel structural approach, a new type of constrained Delaunay triangulation, for the utilization of non-nuclei tissue components. This structural approach first defines two sets of nodes on cell nuclei and luminal regions. It then constructs a constrained Delaunay triangulation on the nucleus nodes with the lumen nodes forming its constraints. Finally, it classifies the tissue samples using the features extracted from this newly introduced constrained Delaunay triangulation. Working with 213 colon tissues taken from 58 patients, our experiments demonstrate that the constrained Delaunay triangulation approach leads to higher accuracies of 87.83 percent and 85.71 percent for the training and test sets, respectively. The experiments also show that the introduction of this new structural representation, which allows definition of new features, provides a more robust graph-based methodology for the examination of cancerous tissues and better performance than its predecessors.Erdoğan, Süleyman TuncerM.S

    Image analysis for the study of chromatin distribution in cell nuclei with application to cervical cancer screening

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    Análise automática de características não métricas de crânios baseada em modelos 3D

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    Mestrado em Engenharia de Computadores e TelemáticaO propósito desta dissertação é a melhoria da aplicação CraMs e a análise craniométrica de modelos 3D através da quantificação e classificação de estruturas e características morfológicas. Uma oportunidade para o desenvolvimento deste projeto apresentou-se no ano de 2012 numa tentativa de colaboração com antropólogos, para criar uma aplicação que os ajudasse e facilitasse a realização de medições craniométricas e no processo de marcação de pontos. A aplicação permite ultrapassar alguns dos problemas existentes com os métodos manuais utilizados pelos antropólogos que podem criar resultados irregulares em medições e danificar os espécimes no seu manuseamento. Esta ideia levou ao desenvolvimento de um programa de computador, CraMs, no âmbito de duas dissertações de mestrado nos anos letivos de 2012-2014. Esta nova abordagem baseia-se na aquisição de modelos craniométricos usando um scanner 3D que depois, serão usados para fazer medições e análises normalizadas. O trabalho desenvolvido foca-se na abordagem dos problemas identificados pelos especialistas e na expansão das funcionalidades existentes a fim de criar novos métodos e melhorar a sua usabilidade. Os métodos acima mencionados centram-se na análise da morfologia das amostras e na extração das estruturas de forma uniforme, nomeadamente, a forma da abertura nasal, a depressão pós-bregmática, a espinha nasal anterior e a forma craniana para uma classificação padrão, com o objetivo de identificar a ascendência do indivíduo e o seu género.The purpose of this dissertation work is to improve the CraMs application and the craniometric analysis of 3D models through the quantification and classification of structures and morphological characteristics. An opportunity for the development of this project presented itself in the year of 2012 in a collaboration with anthropologists, to create an application that would assist those performing craniometric measurements and in the process of marking points. The use of an application can improve some of the problems that exist with the manual methods used by anthropologists, that can create irregular results in measurements and can damage the specimens, while handling them. This idea led to the development of a computer application, CraMs, in the scope of two Master dissertations in the academic years 2012-2014. This new approach relies on the acquisition of craniums using a 3D scanner which will, afterwards, be used to make standardized measurements and analysis. The work developed concentrate in the issues identified by the specialists and in the expansion of the functionalities in order to create new methods and improve the usability. The methods mentioned above focus on the morphology analysis of the specimens and on extraction of the structures uniformly, namely the nasal aperture width, the anterior nasal spine, the postbregmatic depression and the cranial shape, for a standard classification with the purpose of identifying the individual ancestry and gender

    Computerized cancer malignancy grading of fine needle aspirates

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    According to the World Health Organization, breast cancer is a leading cause of death among middle-aged women. Precise diagnosis and correct treatment significantly reduces the high number of deaths caused by breast cancer. Being successful in the treatment strictly relies on the diagnosis. Specifically, the accuracy of the diagnosis and the stage at which a cancer was diagnosed. Precise and early diagnosis has a major impact on the survival rate, which indicates how many patients will live after the treatment. For many years researchers in medical and computer science fields have been working together to find the approach for precise diagnosis. For this thesis, precise diagnosis means finding a cancer at as early a stage as possible by developing new computer aided diagnostic tools. These tools differ depending on the type of cancer and the type of the examination that is used for diagnosis. This work concentrates on cytological images of breast cancer that are produced during fine needle aspiration biopsy examination. This kind of examination allows pathologists to estimate the malignancy of the cancer with very high accuracy. Malignancy estimation is very important when assessing a patients survival rate and the type of treatment. To achieve precise malignancy estimation, a classification framework is presented. This framework is able to classify breast cancer malignancy into two malignancy classes and is based on features calculated according to the Bloom-Richardson grading scheme. This scheme is commonly used by pathologists when grading breast cancer tissue. In Bloom-Richardson scheme two types of features are assessed depending on the magnification. Low magnification images are used for examining the dispersion of the cells in the image while the high magnification images are used for precise analysis of the cells' nuclear features. In this thesis, different types of segmentation algorithms were compared to estimate the algorithm that allows for relatively fast and accurate nuclear segmentation. Based on that segmentation a set of 34 features was extracted for further malignancy classification. For classification purposes 6 different classifiers were compared. From all of the tests a set of the best preforming features were chosen. The presented system is able to classify images of fine needle aspiration biopsy slides with high accurac
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