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  • 1. Astruc, Marine
    et al.
    Malm, Patrik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Kumar, Rajesh
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Cluster detection and field-of-view quality rating: Applied to automated Pap-smear analysis2013In: Proc. 2nd International Conference on Pattern Recognition Applications and Methods, SciTePress, 2013, p. 355-364Conference paper (Refereed)
    Abstract [en]

    Automated cervical cancer screening systems require high resolution analysis of a large number of epithelial cells, involving complex algorithms, mainly analysing the shape and texture of cell nuclei. This can be a very time consuming process. An initial selection of relevant fields-of-view in low resolution images could limit the number of fields to be further analysed at a high resolution. In particular, the detection of cell clusters is of interest for nuclei segmentation improvement, and for diagnostic purpose, malignant and endometrial cells being more prone to stick together in clusters than other cells. In this paper, we propose methods aiming at evaluating the quality of fields-of-view in bright-field microscope images of cervical cells. The approach consists in the construction of neighbourhood graphs using the nuclei as the set of vertices. Transformations are then applied on such graphs in order to highlight the main structures in the image. The methods result in the delineation of regions with varying cell density and the identification of cell clusters. Clustering methods are evaluated using a dataset of manually delineated clusters and compared to a related work.

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  • 2.
    Bengtsson, Ewert
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Malm, Patrik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Screening for Cervical Cancer Using Automated Analysis of PAP-Smears2014In: Computational & Mathematical Methods in Medicine, ISSN 1748-670X, E-ISSN 1748-6718, Vol. 2014, p. 842037:1-12Article, review/survey (Refereed)
    Abstract [en]

    Cervical cancer is one of the most deadly and common forms of cancer among women if no action is taken to prevent it, yet it is preventable through a simple screening test, the so-called PAP-smear. This is the most effective cancer prevention measure developed so far. But the visual examination of the smears is time consuming and expensive and there have been numerous attempts at automating the analysis ever since the test was introduced more than 60 years ago. The first commercial systems for automated analysis of the cell samples appeared around the turn of the millennium but they have had limited impact on the screening costs. In this paper we examine the key issues that need to be addressed when an automated analysis system is developed and discuss how these challenges have been met over the years. The lessons learned may be useful in the efforts to create a cost-effective screening system that could make affordable screening for cervical cancer available for all women globally, thus preventing most of the quarter million annual unnecessary deaths still caused by this disease.

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  • 3. Bujy, Balakrishnan
    et al.
    Sujathan, Vilayil
    Malm, Patrik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Kumar, Rajesh
    A fast and reliable approach to cell nuclei segmentation in PAP stained cervical smears2013In: CSI Transactions on ICT, ISSN 2277-9078Article in journal (Refereed)
    Abstract [en]

    Fast and reliable segmentation of cervical cellnuclei is one of the crucial steps of an automated screeningsystem that aims early detection of cervical cancer. In thispaper, we propose an edge based approach using custom-ized Laplacian of Gaussian (LoG) filter to segment freelying cell nuclei in bright-field microscope images of Papsmear. The LoG is generally employed as a second orderedge detector in image processing. The images may havethe challenges of inconsistent staining, overlapping andfolded cells. Experimenting proposed method over all typesof cervical images including sufficient number of highgrade lesions of cervical cancer shows that our methodperforms well for stain varied images containing focusednuclei.

  • 4. Chandran, P. S.
    et al.
    Byju, N. B.
    Deepak, R. U.
    Rajesh Kumar, R.
    Sudhamony, S.
    Malm, Patrik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Cluster detection in cytology images using the cellgraph method2012In: Information Technology in Medicine and Education (ITME), 2012 International Symposium, 2012, p. 923-927Conference paper (Refereed)
    Abstract [en]

    Automated cervical cancer detection system is primarily based on delineating the cell nuclei and analyzing their textural and morphometric features for malignant characteristics. The presence of cell clusters in the slides have diagnostic value, since malignant cells have a greater tendency to stick together forming clusters than normal cells. However, cell clusters pose difficulty in delineating nucleus and extracting features reliably for malignancy detection in comparison to free lying cells. LBC slide preparation techniques remove biological artifacts and clustering to some extent but not completely. Hence cluster detection in automated cervical cancer screening becomes significant. In this work, a graph theoretical technique is adopted which can identify and compute quantitative metrics for this purpose. This method constructs a cell graph of the image in accordance with the Waxman model, using the positional coordinates of cells. The computed graph metrics from the cell graphs are used as the feature set for the classifier to deal with cell clusters. It is a preliminary exploration of using the topological analysis of the cellgraph to cytological images and the accuracy of classification using SVM showed that the results are well suited for cluster detection.

  • 5. García-Olalla, Oscar
    et al.
    Alegre, Enrique
    Fernández-Robles, Laura
    Malm, Patrik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Acrosome integrity assessment of boar spermatozoa images using an early fusion of texture and contour descriptors2015In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 120, no 1, p. 49-64Article in journal (Refereed)
  • 6. Lindblad, Joakim
    et al.
    Sladoje, Natasa
    Malm, Patrik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Moshavegh, Ramin
    Mehnert, Andrew
    Optimizing optics and imaging for pattern recognition based screening tasks2014In: Proc. 22nd International Conference on Pattern Recognition, IEEE Computer Society, 2014, p. 3333-3338Conference paper (Refereed)
    Abstract [en]

    We present a method for simulating lower quality images starting from higher quality ones, based on acquired image pairs from different optical setups. The method does not require estimates of point (or line) spread functions of the system, but utilizes the relative transfer function derived from images of real specimen of interest in the observed application. Thanks to the use of a larger number of real specimen, excellent stability and robustness of the method is achieved. The intended use is exploring the influence of image quality on features and classification accuracy in pattern recognition based screening tasks. Visual evaluation of the obtained images strongly confirms usefulness of the method. The approach is quantitatively evaluated by observing stability of feature values, proven useful for PAP-smear classification, between synthetic and real images from seven different microscope setups. The evaluation shows that features from the synthetically generated lower resolution images are as similar to features from real images at that resolution, as features from two different images of the same specimen, taken at the same low resolution, are to each other.

  • 7.
    Luengo Hendriks, Cris L.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Malm, Patrik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Rapid prototyping of image analysis applications2011In: Medical Image Processing: Techniques and Applications / [ed] G. Dougherty, Springer , 2011, p. 5-25Chapter in book (Refereed)
  • 8. Maddah, Farzaneh
    et al.
    Soeria-Atmadja, Daniel
    Malm, Patrik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Gustafsson, Mats G.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    Hammerling, Ulf
    Interrogating health-related public databases from a food toxicology perspective: Computational analysis of scoring data2011In: Food and Chemical Toxicology, ISSN 0278-6915, E-ISSN 1873-6351, Vol. 49, no 11, p. 2830-2840Article in journal (Refereed)
    Abstract [en]

    Over the last 15 years, an expanding number of databases with information on noxious effects of substances on mammalian organisms and the environment have been made available on the Internet. This set of databases is a key source of information for risk assessment within several areas of toxicology. Here we present features and relationships across a relatively wide set of publicly accessible databases broadly within toxicology, in part by clustering multi-score representations of such repositories, to support risk assessment within food toxicology. For this purpose 36 databases were each scrutinized, using 18 test substances from six different categories as probes. Results have been analyzed by means of various uni- and multi-variate statistical operations. The former included a special index devised to afford context-specific rating of databases across a highly heterogeneous data matrix, whereas the latter involved cluster analysis, enabling the identification of database assemblies with overall shared characteristics. One database – HSDB – was outstanding due to rich and qualified information for most test substances, but an appreciable fraction of the interrogated repositories showed good to decent scoring. Among the six chosen substance groups, Food contact materials had the most comprehensive toxicological information, followed by the Pesticides category.

  • 9.
    Malm, Patrik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Image Analysis in Support of Computer-Assisted Cervical Cancer Screening2013Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Cervical cancer is a disease that annually claims the lives of over a quarter of a million women. A substantial number of these deaths could be prevented if population wide cancer screening, based on the Papanicolaou test, were globally available. The Papanicolaou test involves a visual review of cellular material obtained from the uterine cervix. While being relatively inexpensive from a material standpoint, the test requires highly trained cytology specialists to conduct the analysis. There is a great shortage of such specialists in developing countries, causing these to be grossly overrepresented in the mortality statistics. For the last 60 years, numerous attempts at constructing an automated system, able to perform the screening, have been made. Unfortunately, a cost-effective, automated system has yet to be produced.

    In this thesis, a set of methods, aimed to be used in the development of an automated screening system, are presented. These have been produced as part of an international cooperative effort to create a low-cost cervical cancer screening system. The contributions are linked to a number of key problems associated with the screening: Deciding which areas of a specimen that warrant analysis, delineating cervical cell nuclei, rejecting artefacts to make sure that only cells of diagnostic value are included when drawing conclusions regarding the final diagnosis of the specimen. Also, to facilitate efficient method development, two methods for creating synthetic images that mimic images acquired from specimen are described.

    List of papers
    1. Closing Curves with Riemannian Dilation: Application to Segmentation in Automated Cervical Cancer Screening
    Open this publication in new window or tab >>Closing Curves with Riemannian Dilation: Application to Segmentation in Automated Cervical Cancer Screening
    2009 (English)In: Advances in Visual Computing / [ed] George Bebis et al., Berlin / Heidelberg: Springer , 2009, p. 337-346Conference paper, Published paper (Refereed)
    Abstract [en]

    In this paper, we describe a nuclei segmentation algorithm for Pap smears that uses anisotropic dilation for curve closing. Edge detection methods often return broken edges that need to be closed to achieve a proper segmentation. Our method performs dilation using Riemannian distance maps that are derived from the local structure tensor field in the image. We show that our curve closing improve the segmentation along weak edges and significantly increases the overall performance of segmentation. This is validated in a thorough study on realistic synthetic cell images from our Pap smear simulator. The algorithm is also demonstrated on bright-field microscope images of real Pap smears from cervical cancer screening.

    Place, publisher, year, edition, pages
    Berlin / Heidelberg: Springer, 2009
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743 ; 5875
    Keywords
    Pap-smears, Riemannian dilation, Curve closing, Anisotropic dilation, Cell segmentation
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Research subject
    Computerized Image Analysis
    Identifiers
    urn:nbn:se:uu:diva-111501 (URN)10.1007/978-3-642-10331-5_32 (DOI)978-3-642-10330-8 (ISBN)
    Conference
    ISVC
    Available from: 2009-12-16 Created: 2009-12-16 Last updated: 2022-01-28Bibliographically approved
    2. PAPSYNTH: Simulated Bright-field Images of Cervical Smears
    Open this publication in new window or tab >>PAPSYNTH: Simulated Bright-field Images of Cervical Smears
    2010 (English)In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2010Conference paper, Published paper (Refereed)
    Abstract [en]

    In this paper, we present a simulator for bright-field microscope imagesof ”Pap-smears”, which is the most common technique usedtoday for cervical cancer screening. Lacking a ground truth for realimages, these realistic synthetic images may be used to tune and validateimage analysis and processing algorithms. We demonstrate thisfor two tasks: uncorrelated noise removal and nucleus segmentation.The simulator is a part of a larger project, aiming at automatic, costefficient screening for cervical cancer in developing countries.In this paper, we present a simulator for bright-field microscope imagesof ”Pap-smears”, which is the most common technique usedtoday for cervical cancer screening. Lacking a ground truth for realimages, these realistic synthetic images may be used to tune and validateimage analysis and processing algorithms. We demonstrate thisfor two tasks: uncorrelated noise removal and nucleus segmentation.The simulator is a part of a larger project, aiming at automatic, costefficient screening for cervical cancer in developing countries.

    Series
    Biomedical Imaging: From Nano to Macro, ISSN 1945-7936 ; 7
    Keywords
    Synthetic cell images, bright-field, cervical cancer
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Research subject
    Computerized Image Analysis
    Identifiers
    urn:nbn:se:uu:diva-126066 (URN)978-1-4244-4126-6 (ISBN)
    Conference
    ISBI 2010
    Available from: 2010-06-02 Created: 2010-06-02 Last updated: 2022-01-28Bibliographically approved
    3. Debris removal in Pap-smear images
    Open this publication in new window or tab >>Debris removal in Pap-smear images
    Show others...
    2013 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 111, no 1, p. 128-138Article in journal (Refereed) Published
    Abstract [en]

    Since its introduction in the 1940s the Pap-smear test has helped reduce the incidence of cervical cancer dramatically in countries where regular screening is standard. The automation of this procedure is an open problem that has been ongoing for over fifty years without reaching satisfactory results. Existing systems are discouragingly expensive and yet they are only able to make a correct distinction between normal and abnormal samples in a fraction of cases. Therefore, they are limited to acting as support for the cytotechnicians as they perform their manual screening. The main reason for the current limitations is that the automated systems struggle to overcome the complexity of the cell structures. Samples are covered in artefacts such as blood cells, overlapping and folded cells, and bacteria, that hamper the segmentation processes and generate large number of suspicious objects. The classifiers designed to differentiate between normal cells and pre-cancerous cells produce unpredictable results when classifying artefacts. In this paper, we propose a sequential classification scheme focused on removing unwanted objects, debris, from an initial segmentation result, intended to be run before the actual normal/abnormal classifier. The method has been evaluated using three separate datasets obtained from cervical samples prepared using both the standard Pap-smear approach as well as the more recent liquid based cytology sample preparation technique. We show success in removing more than 99% of the debris without loosing more than around one percent of the epithelial cells detected by the segmentation process.

    Keywords
    Debris removal, Pap-smear, Cervical cancer screening, LBC
    National Category
    Medical Image Processing
    Identifiers
    urn:nbn:se:uu:diva-204092 (URN)10.1016/j.cmpb.2013.02.008 (DOI)000320346400013 ()
    Available from: 2013-07-22 Created: 2013-07-22 Last updated: 2017-12-06Bibliographically approved
    4. Cluster detection and field-of-view quality rating: Applied to automated Pap-smear analysis
    Open this publication in new window or tab >>Cluster detection and field-of-view quality rating: Applied to automated Pap-smear analysis
    2013 (English)In: Proc. 2nd International Conference on Pattern Recognition Applications and Methods, SciTePress, 2013, p. 355-364Conference paper, Published paper (Refereed)
    Abstract [en]

    Automated cervical cancer screening systems require high resolution analysis of a large number of epithelial cells, involving complex algorithms, mainly analysing the shape and texture of cell nuclei. This can be a very time consuming process. An initial selection of relevant fields-of-view in low resolution images could limit the number of fields to be further analysed at a high resolution. In particular, the detection of cell clusters is of interest for nuclei segmentation improvement, and for diagnostic purpose, malignant and endometrial cells being more prone to stick together in clusters than other cells. In this paper, we propose methods aiming at evaluating the quality of fields-of-view in bright-field microscope images of cervical cells. The approach consists in the construction of neighbourhood graphs using the nuclei as the set of vertices. Transformations are then applied on such graphs in order to highlight the main structures in the image. The methods result in the delineation of regions with varying cell density and the identification of cell clusters. Clustering methods are evaluated using a dataset of manually delineated clusters and compared to a related work.

    Place, publisher, year, edition, pages
    SciTePress, 2013
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Research subject
    Computerized Image Analysis
    Identifiers
    urn:nbn:se:uu:diva-212509 (URN)978-989-8565-41-9 (ISBN)
    Conference
    2nd International Conference on Pattern Recognition Applications and Methods (ICPRAM), February 15-18, 2013, Barcelona, Spain
    Available from: 2013-12-11 Created: 2013-12-11 Last updated: 2018-01-11Bibliographically approved
    5. Simulation of bright-field microscopy images depicting pap-smear specimen
    Open this publication in new window or tab >>Simulation of bright-field microscopy images depicting pap-smear specimen
    2015 (English)In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 87, no 3, p. 212-226Article in journal (Refereed) Published
    Abstract [en]

    As digital imaging is becoming a fundamental part of medical and biomedical research, the demand for computer-based evaluation using advanced image analysis is becoming an integral part of many research projects. A common problem when developing new image analysis algorithms is the need of large datasets with ground truth on which the algorithms can be tested and optimized. Generating such datasets is often tedious and introduces subjectivity and interindividual and intraindividual variations. An alternative to manually created ground-truth data is to generate synthetic images where the ground truth is known. The challenge then is to make the images sufficiently similar to the real ones to be useful in algorithm development. One of the first and most widely studied medical image analysis tasks is to automate screening for cervical cancer through Pap-smear analysis. As part of an effort to develop a new generation cervical cancer screening system, we have developed a framework for the creation of realistic synthetic bright-field microscopy images that can be used for algorithm development and benchmarking. The resulting framework has been assessed through a visual evaluation by experts with extensive experience of Pap-smear images. The results show that images produced using our described methods are realistic enough to be mistaken for real microscopy images. The developed simulation framework is very flexible and can be modified to mimic many other types of bright-field microscopy images.

    Keywords
    Synthetic image generation, Pap-smear, brightfield microscopy
    National Category
    Medical Image Processing
    Research subject
    Computerized Image Processing
    Identifiers
    urn:nbn:se:uu:diva-212514 (URN)10.1002/cyto.a.22624 (DOI)000349984200005 ()25573002 (PubMedID)
    Available from: 2015-01-08 Created: 2013-12-11 Last updated: 2017-12-06Bibliographically approved
    6. Multi-resolution Cervical Cell Dataset
    Open this publication in new window or tab >>Multi-resolution Cervical Cell Dataset
    2013 (English)Report (Other academic)
    Place, publisher, year, edition, pages
    Uppsala, Sweden: Centre for Image Analysis, Swedish University of Agricultural Sciences, 2013. p. 9
    Series
    External report (Blue series) ; 37
    Keywords
    Pap smear, multi-resolution, cervical cancer
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Research subject
    Computerized Image Analysis
    Identifiers
    urn:nbn:se:uu:diva-212505 (URN)
    Available from: 2013-12-11 Created: 2013-12-11 Last updated: 2018-01-11Bibliographically approved
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    fulltext
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    presentationsbild
  • 10.
    Malm, Patrik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Multi-resolution Cervical Cell Dataset2013Report (Other academic)
    Download full text (pdf)
    fulltext
  • 11.
    Malm, Patrik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Reviews of scientific papers on Automated cervical cancer screening through image analysis2008Report (Other academic)
    Download full text (pdf)
    FULLTEXT01
  • 12.
    Malm, Patrik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Balakrishnan, Byju N.
    Sujathan, Vilayil K.
    Kumar, Rajesh
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Debris removal in Pap-smear images2013In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 111, no 1, p. 128-138Article in journal (Refereed)
    Abstract [en]

    Since its introduction in the 1940s the Pap-smear test has helped reduce the incidence of cervical cancer dramatically in countries where regular screening is standard. The automation of this procedure is an open problem that has been ongoing for over fifty years without reaching satisfactory results. Existing systems are discouragingly expensive and yet they are only able to make a correct distinction between normal and abnormal samples in a fraction of cases. Therefore, they are limited to acting as support for the cytotechnicians as they perform their manual screening. The main reason for the current limitations is that the automated systems struggle to overcome the complexity of the cell structures. Samples are covered in artefacts such as blood cells, overlapping and folded cells, and bacteria, that hamper the segmentation processes and generate large number of suspicious objects. The classifiers designed to differentiate between normal cells and pre-cancerous cells produce unpredictable results when classifying artefacts. In this paper, we propose a sequential classification scheme focused on removing unwanted objects, debris, from an initial segmentation result, intended to be run before the actual normal/abnormal classifier. The method has been evaluated using three separate datasets obtained from cervical samples prepared using both the standard Pap-smear approach as well as the more recent liquid based cytology sample preparation technique. We show success in removing more than 99% of the debris without loosing more than around one percent of the epithelial cells detected by the segmentation process.

  • 13.
    Malm, Patrik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    The CerviSCAN project: Project description and current progress2011In: Proceedings SSBA 2011, 2011Conference paper (Other academic)
    Abstract [en]

    Cervical cancer is the second most common type of cancer among women in spite of the fact that it through screening easily can be detected and cured before it becomes invasive. Current screening procedures are too complex and costly for use in developing countries. TheCerviSCAN project is an attempt to create a automated cervical cancer screening system that will lower the cost and increase the throughput of samples. This paper accounts for the current progress of the project as well as some of the planned future work.

    Download full text (pdf)
    Malm2011
  • 14.
    Malm, Patrik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Brun, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Closing Curves with Riemannian Dilation: Application to Segmentation in Automated Cervical Cancer Screening2009In: Advances in Visual Computing / [ed] George Bebis et al., Berlin / Heidelberg: Springer , 2009, p. 337-346Conference paper (Refereed)
    Abstract [en]

    In this paper, we describe a nuclei segmentation algorithm for Pap smears that uses anisotropic dilation for curve closing. Edge detection methods often return broken edges that need to be closed to achieve a proper segmentation. Our method performs dilation using Riemannian distance maps that are derived from the local structure tensor field in the image. We show that our curve closing improve the segmentation along weak edges and significantly increases the overall performance of segmentation. This is validated in a thorough study on realistic synthetic cell images from our Pap smear simulator. The algorithm is also demonstrated on bright-field microscope images of real Pap smears from cervical cancer screening.

  • 15.
    Malm, Patrik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Brun, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Curve closing by gradient weighted distance transformation applied in automated cervical cancer screening2009In: Proceedings SSBA 2009: Symposium on Image Analysis / [ed] Josef Bigun and Antanas Verikas, 2009, p. 25-28Conference paper (Other academic)
    Abstract [en]

    In this paper, a nuclei segmentation algorithm that uses a gradient weighted distance transform for curve closing is described. Edge detection methods often return broken edges that need to be closed to achieve proper segmentation. Based n a Canny edge result, this method generates a distance map that is dependent on local gradient directions and magnitudes making it possible to promote propagation along edges rather than away from them. Through this procedure, which is a kind of adaptive morphology, it is possible to achieve curve closing even along very weak edges. The method is developed within a larger project  aimed at creating a cervical cancer screening system mainly intended to allow automated screening in developing countries.

  • 16.
    Malm, Patrik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Brun, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    PAPSYNTH: Simulated Bright-field Images of Cervical Smears2010In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2010Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a simulator for bright-field microscope imagesof ”Pap-smears”, which is the most common technique usedtoday for cervical cancer screening. Lacking a ground truth for realimages, these realistic synthetic images may be used to tune and validateimage analysis and processing algorithms. We demonstrate thisfor two tasks: uncorrelated noise removal and nucleus segmentation.The simulator is a part of a larger project, aiming at automatic, costefficient screening for cervical cancer in developing countries.In this paper, we present a simulator for bright-field microscope imagesof ”Pap-smears”, which is the most common technique usedtoday for cervical cancer screening. Lacking a ground truth for realimages, these realistic synthetic images may be used to tune and validateimage analysis and processing algorithms. We demonstrate thisfor two tasks: uncorrelated noise removal and nucleus segmentation.The simulator is a part of a larger project, aiming at automatic, costefficient screening for cervical cancer in developing countries.

  • 17.
    Malm, Patrik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Brun, Anders
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Simulation of bright-field microscopy images depicting pap-smear specimen2015In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 87, no 3, p. 212-226Article in journal (Refereed)
    Abstract [en]

    As digital imaging is becoming a fundamental part of medical and biomedical research, the demand for computer-based evaluation using advanced image analysis is becoming an integral part of many research projects. A common problem when developing new image analysis algorithms is the need of large datasets with ground truth on which the algorithms can be tested and optimized. Generating such datasets is often tedious and introduces subjectivity and interindividual and intraindividual variations. An alternative to manually created ground-truth data is to generate synthetic images where the ground truth is known. The challenge then is to make the images sufficiently similar to the real ones to be useful in algorithm development. One of the first and most widely studied medical image analysis tasks is to automate screening for cervical cancer through Pap-smear analysis. As part of an effort to develop a new generation cervical cancer screening system, we have developed a framework for the creation of realistic synthetic bright-field microscopy images that can be used for algorithm development and benchmarking. The resulting framework has been assessed through a visual evaluation by experts with extensive experience of Pap-smear images. The results show that images produced using our described methods are realistic enough to be mistaken for real microscopy images. The developed simulation framework is very flexible and can be modified to mimic many other types of bright-field microscopy images.

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  • 18. Mehnert, Andrew
    et al.
    Moshavegh, Ramin
    Sujathan, Vilayil K.
    Malm, Patrik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    A Structural Texture Approach for Characterising Malignancy Associated Changes in Pap Smears Based on Mean-Shift and the Watershed Transform2014In: Proc. 22nd International Conference on Pattern Recognition, IEEE Computer Society, 2014, p. 1189-1193Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel structural approach to quantitatively characterising nuclear chromatin texture in light microscope images of Pap smears. The approach is based on segmenting the chromatin into blob-like primitives and characterising their properties and arrangement. The segmentation approach makes use of multiple focal planes. It comprises two basic steps: (i) mean-shift filtering in the feature space formed by concatenating pixel spatial coordinates and intensity values centred around the best all-in-focus plane; and (ii) hierarchical marker-based watershed segmentation. The paper also presents an empirical evaluation of the approach based on the classification of 43 routine clinical Pap smears. Two variants of the approach were compared to a reference approach (employing extended depth-of-field rather than mean-shift) in a feature selection/classification experiment, involving 138 segmentation-based features, for discriminating normal and abnormal slides. The results demonstrate improved performance over the reference approach. The results of a second feature selection/classification experiment, including additional classes of features from the literature, show that a combination of the proposed structural and conventional features yields a classification performance of 0.919 +/- 0.015 (AUC +/- Std.Dev.). Overall the results demonstrate the efficacy of the proposed structural approach and confirm that it is indeed possible to detect malignancy associated changes (MACs) in conventional Papanicolaou stain.

  • 19. Moshavegh, R.
    et al.
    Bejnordi, B. E.
    Mehnert, A.
    Sujathan, K.
    Malm, Patrik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Automated segmentation of free-lying cell nuclei in Pap smears for malignancy-associated change analysis2012In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2012, p. 5372-5375Conference paper (Refereed)
    Abstract [en]

    This paper presents an automated algorithm for robustly detecting and segmenting free-lying cell nuclei in bright-field microscope images of Pap smears. This is an essential initial step in the development of an automated screening system for cervical cancer based on malignancy associated change (MAC) analysis. The proposed segmentation algorithm makes use of gray-scale annular closings to identify free-lying nuclei-like objects together with marker-based watershed segmentation to accurately delineate the nuclear boundaries. The algorithm also employs artifact rejection based on size, shape, and granularity to ensure only the nuclei of intermediate squamous epithelial cells are retained. An evaluation of the performance of the algorithm relative to expert manual segmentation of 33 fields-of-view from 11 Pap smear slides is also presented. The results show that the sensitivity and specificity of nucleus detection is 94.71% and 85.30% respectively, and that the accuracy of segmentation, measured using the Dice coefficient, of the detected nuclei is 97.30±1.3%.

  • 20.
    Soeria-Atmadja, Daniel
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    Malm, Patrik
    Larsson, Rolf
    Hammerling, Ulf
    Gustafsson, Mats
    Framework for optimal multi-branching hierarchical clustering rapidly reveals relevant substructures in multivariate tumor biology and allergy dataManuscript (Other academic)
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