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Malm, Patrik
Publications (10 of 20) Show all publications
García-Olalla, O., Alegre, E., Fernández-Robles, L., Malm, P. & Bengtsson, E. (2015). Acrosome integrity assessment of boar spermatozoa images using an early fusion of texture and contour descriptors. Computer Methods and Programs in Biomedicine, 120(1), 49-64
Open this publication in new window or tab >>Acrosome integrity assessment of boar spermatozoa images using an early fusion of texture and contour descriptors
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2015 (English)In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 120, no 1, p. 49-64Article in journal (Refereed) Published
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-255045 (URN)10.1016/j.cmpb.2015.03.005 (DOI)000353774600006 ()25887848 (PubMedID)
Available from: 2015-03-23 Created: 2015-06-12 Last updated: 2017-12-04Bibliographically approved
Malm, P., Brun, A. & Bengtsson, E. (2015). Simulation of bright-field microscopy images depicting pap-smear specimen. Cytometry Part A, 87(3), 212-226
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
Mehnert, A., Moshavegh, R., Sujathan, V. K., Malm, P. & Bengtsson, E. (2014). A Structural Texture Approach for Characterising Malignancy Associated Changes in Pap Smears Based on Mean-Shift and the Watershed Transform. In: Proc. 22nd International Conference on Pattern Recognition: . Paper presented at ICPR 2014, August 24–28, Stockholm, Sweden (pp. 1189-1193). IEEE Computer Society
Open this publication in new window or tab >>A Structural Texture Approach for Characterising Malignancy Associated Changes in Pap Smears Based on Mean-Shift and the Watershed Transform
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2014 (English)In: Proc. 22nd International Conference on Pattern Recognition, IEEE Computer Society, 2014, p. 1189-1193Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE Computer Society, 2014
Series
International Conference on Pattern Recognition, ISSN 1051-4651
National Category
Medical Image Processing
Identifiers
urn:nbn:se:uu:diva-239622 (URN)10.1109/ICPR.2014.214 (DOI)000359818001052 ()978-1-4799-5208-3 (ISBN)
Conference
ICPR 2014, August 24–28, Stockholm, Sweden
Available from: 2014-08-28 Created: 2014-12-29 Last updated: 2017-02-08Bibliographically approved
Lindblad, J., Sladoje, N., Malm, P., Bengtsson, E., Moshavegh, R. & Mehnert, A. (2014). Optimizing optics and imaging for pattern recognition based screening tasks. In: Proc. 22nd International Conference on Pattern Recognition: . Paper presented at ICPR 2014, August 24–28, Stockholm, Sweden (pp. 3333-3338). IEEE Computer Society
Open this publication in new window or tab >>Optimizing optics and imaging for pattern recognition based screening tasks
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2014 (English)In: Proc. 22nd International Conference on Pattern Recognition, IEEE Computer Society, 2014, p. 3333-3338Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE Computer Society, 2014
Series
International Conference on Pattern Recognition, ISSN 1051-4651
National Category
Medical Image Processing
Identifiers
urn:nbn:se:uu:diva-239584 (URN)10.1109/ICPR.2014.572 (DOI)000359818003078 ()978-1-4799-5208-3 (ISBN)
Conference
ICPR 2014, August 24–28, Stockholm, Sweden
Available from: 2014-08-28 Created: 2014-12-29 Last updated: 2018-08-24Bibliographically approved
Bengtsson, E. & Malm, P. (2014). Screening for Cervical Cancer Using Automated Analysis of PAP-Smears. Computational & Mathematical Methods in Medicine, 2014, 842037:1-12
Open this publication in new window or tab >>Screening for Cervical Cancer Using Automated Analysis of PAP-Smears
2014 (English)In: Computational & Mathematical Methods in Medicine, ISSN 1748-670X, E-ISSN 1748-6718, Vol. 2014, p. 842037:1-12Article, review/survey (Refereed) Published
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.

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2014
National Category
Medical Image Processing
Identifiers
urn:nbn:se:uu:diva-224662 (URN)10.1155/2014/842037 (DOI)000333640700001 ()
Available from: 2014-03-20 Created: 2014-05-16 Last updated: 2017-12-05Bibliographically approved
Bujy, B., Sujathan, V., Malm, P. & Kumar, R. (2013). A fast and reliable approach to cell nuclei segmentation in PAP stained cervical smears. CSI Transactions on ICT
Open this publication in new window or tab >>A fast and reliable approach to cell nuclei segmentation in PAP stained cervical smears
2013 (English)In: CSI Transactions on ICT, ISSN 2277-9078Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Springer India, 2013
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-210422 (URN)10.1007/s40012-013-0028-y (DOI)
Available from: 2013-11-07 Created: 2013-11-07 Last updated: 2013-12-04Bibliographically approved
Astruc, M., Malm, P., Kumar, R. & Bengtsson, E. (2013). Cluster detection and field-of-view quality rating: Applied to automated Pap-smear analysis. In: Proc. 2nd International Conference on Pattern Recognition Applications and Methods: . Paper presented at 2nd International Conference on Pattern Recognition Applications and Methods (ICPRAM), February 15-18, 2013, Barcelona, Spain (pp. 355-364). SciTePress
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
Malm, P., Balakrishnan, B. N., Sujathan, V. K., Kumar, R. & Bengtsson, E. (2013). Debris removal in Pap-smear images. Computer Methods and Programs in Biomedicine, 111(1), 128-138
Open this publication in new window or tab >>Debris removal in Pap-smear images
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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
Malm, P. (2013). Image Analysis in Support of Computer-Assisted Cervical Cancer Screening. (Doctoral dissertation). Uppsala: Acta Universitatis Upsaliensis
Open this publication in new window or tab >>Image Analysis in Support of Computer-Assisted Cervical Cancer Screening
2013 (English)Doctoral 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.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2013. p. 95
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1106
Keywords
Image analysis, cervical cancer, pap-smear, synthetic images, screening, image processing, cytometry
National Category
Medical Image Processing
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-212518 (URN)978-91-554-8828-4 (ISBN)
Public defence
2014-02-07, Room 2446, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 13:15 (English)
Opponent
Supervisors
Funder
Vinnova, 2008-01712Swedish Research Council, 2008-2738
Available from: 2014-01-16 Created: 2013-12-11 Last updated: 2014-07-21
Malm, P. (2013). Multi-resolution Cervical Cell Dataset. Uppsala, Sweden: Centre for Image Analysis, Swedish University of Agricultural Sciences
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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