36 results on '"Marcin Kociolek"'
Search Results
2. Skin Lesion Matching Algorithm for Application in Full Body Imaging Systems.
- Author
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Maria Strakowska and Marcin Kociolek
- Published
- 2022
- Full Text
- View/download PDF
3. Functional Kidney Analysis Based on Textured DCE-MRI Images.
- Author
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Marcin Kociolek, Michal Strzelecki, and Artur Klepaczko
- Published
- 2019
- Full Text
- View/download PDF
4. On the Influence of Image Features Wordlength Reduction on Texture Classification.
- Author
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Michal Strzelecki, Marcin Kociolek, and Andrzej Materka
- Published
- 2018
- Full Text
- View/download PDF
5. Interpolation-Based Gray-Level Co-Occurrence Matrix Computation for Texture Directionality Estimation.
- Author
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Marcin Kociolek, Peter Bajcsy, Mary Brady, and Antonio Cardone
- Published
- 2018
- Full Text
- View/download PDF
6. On the influence of the image normalization scheme on texture classification accuracy.
- Author
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Marcin Kociolek, Michal Strzelecki, and Szvmon Szymajda
- Published
- 2018
- Full Text
- View/download PDF
7. Lytic Region Recognition in Hip Radiograms by Means of Statistical Dominance Transform.
- Author
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Marcin Kociolek, Adam Piórkowski, Rafal Obuchowicz, Pawel Kaminski, and Michal Strzelecki
- Published
- 2018
- Full Text
- View/download PDF
8. A Convolutional Neural Networks-Based Approach for Texture Directionality Detection.
- Author
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Marcin Kociolek, Michal Kozlowski, and Antonio Cardone
- Published
- 2022
- Full Text
- View/download PDF
9. Preprocessing of barley grain images for defect identification.
- Author
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Marcin Kociolek, Piotr M. Szczypinski, and Artur Klepaczko
- Published
- 2017
- Full Text
- View/download PDF
10. QMaZda - Software tools for image analysis and pattern recognition.
- Author
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Piotr M. Szczypinski, Artur Klepaczko, and Marcin Kociolek
- Published
- 2017
- Full Text
- View/download PDF
11. Barley defects identification.
- Author
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Piotr M. Szczypinski, Artur Klepaczko, and Marcin Kociolek
- Published
- 2017
- Full Text
- View/download PDF
12. Blue Whitish Veil, Atypical Vascular Pattern and Regression Structures Detection in Skin Lesions Images.
- Author
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Karol Kropidlowski, Marcin Kociolek, Michal Strzelecki, and Dariusz Czubinski
- Published
- 2016
- Full Text
- View/download PDF
13. Nevus atypical pigment network distinction and irregular streaks detection in skin lesions images.
- Author
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Karol Kropidlowski, Marcin Kociolek, Michal Strzelecki, and Dariusz Czubinski
- Published
- 2015
- Full Text
- View/download PDF
14. Skin Lesion Detection Algorithms in Whole Body Images.
- Author
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Michal Strzelecki, Maria Strakowska, Michal Kozlowski, Tomasz Urbanczyk, Dorota Wielowieyska-Szybinska, and Marcin Kociolek
- Published
- 2021
- Full Text
- View/download PDF
15. Model Based Approach for Melanoma Segmentation.
- Author
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Karol Kropidlowski, Marcin Kociolek, Michal Strzelecki, and Dariusz Czubinski
- Published
- 2014
- Full Text
- View/download PDF
16. Does image normalization and intensity resolution impact texture classification?
- Author
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Marcin Kociolek, Michal Strzelecki, and Rafal Obuchowicz
- Published
- 2020
- Full Text
- View/download PDF
17. Survey statistics of automated segmentations applied to optical imaging of mammalian cells.
- Author
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Peter Bajcsy, Antonio Cardone, Joe Chalfoun, Michael Halter, Derek Juba, Marcin Kociolek, Michael Majurski, Adele P. Peskin, Carl G. Simon, Mylene Simon, Antoine Vandecreme, and Mary Brady
- Published
- 2015
- Full Text
- View/download PDF
18. Contributors
- Author
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Peter Bajcsy, Mary Brady, Weidong Cai, Joe Chalfoun, Mei Chen, Subhajyoti De, Daniel J. Hoeppner, Qiaoying Huang, Marcin Kociolek, An-An Liu, Yao Lu, Dimitris Metaxas, Hui Qu, Nisha Ramesh, Gregory Riedlinger, Mylene Simon, Yang Song, Hang Su, Yu-Ting Su, Tolga Tasdizen, Pengxiang Wu, Jingru Yi, and Zhaozheng Yin
- Published
- 2021
19. Skin lesion detection algorithms in whole body images
- Author
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Dorota Wielowieyska-Szybińska, Marcin Kociolek, Michał Kozłowski, M. Strąkowska, T. Urbańczyk, and Michal Strzelecki
- Subjects
Skin Neoplasms ,Computer science ,skin lesion detection ,TP1-1185 ,Skin Diseases ,Biochemistry ,Article ,Analytical Chemistry ,algorithm fusion ,Body Image ,Humans ,Segmentation ,Sensitivity (control systems) ,Electrical and Electronic Engineering ,Melanoma ,Instrumentation ,business.industry ,Deep learning ,Chemical technology ,whole body system ,Atomic and Molecular Physics, and Optics ,Correlation method ,Artificial intelligence ,Whole body ,business ,Skin lesion ,Algorithm ,Algorithms - Abstract
Melanoma is one of the most lethal and rapidly growing cancers, causing many deaths each year. This cancer can be treated effectively if it is detected quickly. For this reason, many algorithms and systems have been developed to support automatic or semiautomatic detection of neoplastic skin lesions based on the analysis of optical images of individual moles. Recently, full-body systems have gained attention because they enable the analysis of the patient’s entire body based on a set of photos. This paper presents a prototype of such a system, focusing mainly on assessing the effectiveness of algorithms developed for the detection and segmentation of lesions. Three detection algorithms (and their fusion) were analyzed, one implementing deep learning methods and two classic approaches, using local brightness distribution and a correlation method. For fusion of algorithms, detection sensitivity = 0.95 and precision = 0.94 were obtained. Moreover, the values of the selected geometric parameters of segmented lesions were calculated and compared for all algorithms. The obtained results showed a high accuracy of the evaluated parameters (error of area estimation <, 10%), especially for lesions with dimensions greater than 3 mm, which are the most suspected of being neoplastic lesions.
- Published
- 2021
20. Object measurements from 2D microscopy images
- Author
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Mary Brady, Joe Chalfoun, Marcin Kociolek, Mylene Simon, and Peter Bajcsy
- Subjects
Java ,Computer science ,business.industry ,Pattern recognition ,Python (programming language) ,Object (computer science) ,Image (mathematics) ,Feature (computer vision) ,Microscopy ,Digital image processing ,Noise (video) ,Artificial intelligence ,business ,computer ,computer.programming_language - Abstract
This chapter addresses object measurements from 2D microscopy images. Our motivation is to introduce readers to image-based object measurements and to quantify numerical variability of image features and feature-based classification outcomes. The objective is to highlight the sources of digital image processing variability when deriving image-based scientific conclusions. By characterizing feature variations across Python scikit-image, CellProfiler, MaZda, ImageJ, and in-house Java libraries, we concluded 15.6% of 32 intensity features, 47.9% of 71 shape features, and 88.2% of 68 textural features differ in values. Shape and textural feature variations had a negative impact on classification outcomes in 52.9% and 97.1% of all single feature-based classifications, respectively. All these numerical results are available at https://isg.nist.gov/deepzoomweb/resources/featureVariability/index.html and are traceable to image objects. They document the amount of digital image processing noise and the importance of gathering computational provenance information.
- Published
- 2021
21. A Multi-Layer Perceptron Network for Perfusion Parameter Estimation in DCE-MRI Studies of the Healthy Kidney
- Author
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Arvid Lundervold, Eli Eikefjord, Michal Strzelecki, Marcin Kociolek, and Artur Klepaczko
- Subjects
Renal function ,Value (computer science) ,pharmacokinetic modeling ,lcsh:Technology ,030218 nuclear medicine & medical imaging ,Set (abstract data type) ,multi-layer perceptron ,lcsh:Chemistry ,03 medical and health sciences ,0302 clinical medicine ,General Materials Science ,skin and connective tissue diseases ,Instrumentation ,dynamic contrast-enhanced MRI ,lcsh:QH301-705.5 ,Mathematics ,Fluid Flow and Transfer Processes ,glomerular filtration rate ,Artificial neural network ,Estimation theory ,business.industry ,lcsh:T ,Process Chemistry and Technology ,General Engineering ,Pattern recognition ,Perceptron ,equipment and supplies ,lcsh:QC1-999 ,Computer Science Applications ,lcsh:Biology (General) ,lcsh:QD1-999 ,lcsh:TA1-2040 ,Multilayer perceptron ,Dynamic contrast-enhanced MRI ,Artificial intelligence ,business ,parameter estimation ,lcsh:Engineering (General). Civil engineering (General) ,human activities ,kidney perfusion ,030217 neurology & neurosurgery ,lcsh:Physics - Abstract
Background: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an imaging technique which helps in visualizing and quantifying perfusion&mdash, one of the most important indicators of an organ&rsquo, s state. This paper focuses on perfusion and filtration in the kidney, whose performance directly influences versatile functions of the body. In clinical practice, kidney function is assessed by measuring glomerular filtration rate (GFR). Estimating GFR based on DCE-MRI data requires the application of an organ-specific pharmacokinetic (PK) model. However, determination of the model parameters, and thus the characterization of GFR, is sensitive to determination of the arterial input function (AIF) and the initial choice of parameter values. Methods: This paper proposes a multi-layer perceptron network for PK model parameter determination, in order to overcome the limitations of the traditional model&rsquo, s optimization techniques based on non-linear least-squares curve-fitting. As a reference method, we applied the trust-region reflective algorithm to numerically optimize the model. The effectiveness of the proposed approach was tested for 20 data sets, collected for 10 healthy volunteers whose image-derived GFR scores were compared with ground-truth blood test values. Results: The achieved mean difference between the image-derived and ground-truth GFR values was 2.35 mL/min/1.73 m2, which is comparable to the result obtained for the reference estimation method (&minus, 5.80 mL/min/1.73 m2). Conclusions: Neural networks are a feasible alternative to the least-squares curve-fitting algorithm, ensuring agreement with ground-truth measurements at a comparable level. The advantages of using a neural network are twofold. Firstly, it can estimate a GFR value without the need to determine the AIF for each individual patient. Secondly, a reliable estimate can be obtained, without the need to manually set up either the initial parameter values or the constraints thereof.
- Published
- 2020
22. Does image normalization and intensity resolution impact texture classification?
- Author
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Rafał Obuchowicz, Marcin Kociolek, and Michal Strzelecki
- Subjects
Brightness ,Scanner ,Computer science ,Normalization (image processing) ,Health Informatics ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Image texture ,Image Processing, Computer-Assisted ,medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,Analysis method ,Radiological and Ultrasound Technology ,medicine.diagnostic_test ,business.industry ,Magnetic resonance imaging ,Pattern recognition ,Image Enhancement ,Magnetic Resonance Imaging ,Computer Graphics and Computer-Aided Design ,Intensity normalization ,Computer Vision and Pattern Recognition ,Artificial intelligence ,Artifacts ,Tomography, X-Ray Computed ,business ,030217 neurology & neurosurgery ,Coding (social sciences) - Abstract
Image texture is a very important component in many types of images, including medical images. Medical images are often corrupted by noise and affected by artifacts. Some of the texture-based features that should describe the structure of the tissue under examination may also reflect, for example, the uneven sensitivity of the scanner within the tissue region. This in turn may lead to an inappropriate description of the tissue or incorrect classification. To limit these phenomena, the analyzed regions of interest are normalized. In texture analysis methods, image intensity normalization is usually followed by a reduction in the number of levels coding the intensity. The aim of this work was to analyze the impact of different image normalization methods and the number of intensity levels on texture classification, taking into account noise and artifacts related to uneven background brightness distribution. Analyses were performed on four sets of images: modified Brodatz textures, kidney images obtained by means of dynamic contrast-enhanced magnetic resonance imaging, shoulder images acquired as T2-weighted magnetic resonance images and CT heart and thorax images. The results will be of use for choosing a particular method of image normalization, based on the types of noise and distortion present in the images.
- Published
- 2020
23. On the influence of the image normalization scheme on texture classification accuracy
- Author
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Michal Strzelecki, Marcin Kociolek, and Szvmon Szymajda
- Subjects
Computer science ,business.industry ,Normalization (image processing) ,020207 software engineering ,Pattern recognition ,02 engineering and technology ,030218 nuclear medicine & medical imaging ,Gray level ,03 medical and health sciences ,symbols.namesake ,0302 clinical medicine ,Gaussian noise ,0202 electrical engineering, electronic engineering, information engineering ,Medical imaging ,symbols ,Rician noise ,Artificial intelligence ,business - Abstract
Texture can be a very rich source of information about the image. Texture analysis finds applications, among other things, in biomedical imaging. One of the widely used methods of texture analysis is the Gray Level Co-occurrence Matrix (GLCM). Texture analysis using the GLCM method is most often carried out in several stages: determination of areas of interest, normalization, calculation of the GLCM, extraction of features, and finally, the classification. Values of the GLCM based features depend on the choice of the normalization method, which was examined in this work. The normalization is necessary, since acquired images often suffer from noise and intensity artifacts. Certainly, the normalization will not eliminate these two effects, however it was demonstrated, that its application improves texture analysis accuracy. The aim of the work was to analyze the influence of different normalization methods on the discriminating ability of features estimated from the GLCM. The analysis was performed both for Brodatz textures and real magnetic resonance data. Brodatz textures were corrupted by three types of distortion: intensity nonuniformity, Gaussian noise and Rician Noise. Three types of normalizations were tested: min- max, 1–99% and $+/-3\sigma$ .
- Published
- 2018
24. Interpolation-Based Gray-Level Co-Occurrence Matrix Computation for Texture Directionality Estimation
- Author
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Mary Brady, Antonio Cardone, Peter Bajcsy, and Marcin Kociolek
- Subjects
0301 basic medicine ,Source code ,Mean squared error ,Computer science ,media_common.quotation_subject ,Computation ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,020207 software engineering ,02 engineering and technology ,03 medical and health sciences ,Co-occurrence matrix ,symbols.namesake ,030104 developmental biology ,Robustness (computer science) ,Gaussian noise ,Computer Science::Computer Vision and Pattern Recognition ,Bounded function ,0202 electrical engineering, electronic engineering, information engineering ,symbols ,Directionality ,Algorithm ,media_common - Abstract
A novel interpolation-based model for the computation of the Gray Level Co-occurrence Matrix (GLCM) is presented. The model enables GLCM computation for any real-valued angles and offsets, as opposed to the traditional, lattice-based model. A texture directionality estimation algorithm is defined using the GLCM-derived correlation feature. The robustness of the algorithm with respect to image blur and additive Gaussian noise is evaluated. It is concluded that directionality estimation is robust to image blur and low noise levels. For high noise levels, the mean error increases but remains bounded. The performance of the directionality estimation algorithm is illustrated on fluorescence microscopy images of fibroblast cells. The algorithm was implemented in C++ and the source code is available in an openly accessible repository.
- Published
- 2018
25. On the Influence of Image Features Wordlength Reduction on Texture Classification
- Author
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Andrzej Materka, Marcin Kociolek, and Michal Strzelecki
- Subjects
0301 basic medicine ,Pixel ,Computer science ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Normalization (image processing) ,Pattern recognition ,Image processing ,03 medical and health sciences ,030104 developmental biology ,0302 clinical medicine ,Region of interest ,Imaging diagnosis ,Segmentation ,Support system ,Artificial intelligence ,Texture feature ,business ,030217 neurology & neurosurgery - Abstract
Texture is present in a large number of medical images. Its structure codes selected properties of visualized organ and tissues so texture can be rich source of information regarding their condition. Quantitative texture analysis plays significant role in imaging diagnosis support systems, enabling segmentation of analyzed organs, detection of lesions, and assessment of the degree of their pathological change. Unfortunately, medical images are often corrupted by noise which affect texture based image features. One of the steps of texture feature extraction is reduction of gray levels number which is performed after a normalization of pixel intensities inside a region of interest. This reduces the noise effect on texture feature values. We demonstrated, based on analysis of natural and MR images, that such reduction improves classification accuracy while reducing the computational costs.
- Published
- 2018
26. Lytic region recognition in hip radiograms by means of statistical dominance transform
- Author
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Michal Strzelecki, Adam Piórkowski, Rafał Obuchowicz, Marcin Kociolek, and Paweł Kamiński
- Subjects
030222 orthopedics ,medicine.medical_specialty ,Computer science ,business.industry ,Radiography ,Total hip replacement ,Periprosthetic ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,0302 clinical medicine ,Low contrast ,Lytic cycle ,medicine ,Radiology ,Treatment procedure ,business - Abstract
Total hip replacement is the accepted treatment procedure of the end stage degeneration of the hip joint. Instability of the prosthesis might be recognized on the radiographic images as area of bone radio - lucency adjacent to the prosthesis pin. However, the very important issue of radiological recognition of periprosthetic lucent areas reflecting the lysis remains a challenge. Small dimensions and fuzzy borders of the lytic areas makes them difficult regions to recognize. Additional factors as high BMI of the patients and/or radiograms taken through a mattress can make the evaluation even more difficult, while small lucent areas might be additionally blurred and of very low contrast. The paper presents a new approach for quantitative recognition of preprothetic lytic areas. We have proposed a multistep algorithm utilizing Statistical Dominance Transform for detection of lytic areas on digital radiograms. Preliminary results are quite promising. It was demonstrated that location and shape of the detected lytic region is in good agreement with assessment by radiologists.
- Published
- 2018
27. Sex Differentiation of Trabecular Bone Structure Based on Textural Analysis of Pelvic Radiographs
- Author
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Paweł Kamiński, Karolina Nurzynska, Joanna Kwiecień, Rafał Obuchowicz, Adam Piórkowski, Elżbieta Pociask, Aleksandra Stępień, Marcin Kociołek, Michał Strzelecki, and Piotr Augustyniak
- Subjects
textural analysis ,radiographs ,pelvic regions ,sex estimation ,machine learning ,Medicine - Abstract
Objectives: The purpose of this paper is to assess the determination of male and female sex from trabecular bone structures in the pelvic region. The study involved analyzing digital radiographs for 343 patients and identifying fourteen areas of interest based on their medical significance, with seven regions on each side of the body for symmetry. Methods: Textural parameters for each region were obtained using various methods, and a thorough investigation of data normalization was conducted. Feature selection approaches were then evaluated to determine a small set of the most representative features, which were input into several classification machine learning models. Results: The findings revealed a sex-dependent correlation in the bone structure observed in X-ray images, with the degree of dependency varying based on the anatomical location. Notably, the femoral neck and ischium regions exhibited distinctive characteristics between sexes. Conclusions: This insight is crucial for medical professionals seeking to estimate sex dependencies from such image data. For these four specific areas, the balanced accuracy exceeded 70%. The results demonstrated symmetry, confirming the genuine dependencies in the trabecular bone structures.
- Published
- 2024
- Full Text
- View/download PDF
28. Barley defects identification
- Author
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Marcin Kociolek, Artur Klepaczko, and Piotr M. Szczypiński
- Subjects
021103 operations research ,Computer science ,business.industry ,media_common.quotation_subject ,Supervised learning ,Feature extraction ,0211 other engineering and technologies ,Process (computing) ,Pattern recognition ,04 agricultural and veterinary sciences ,02 engineering and technology ,Visual inspection ,Identification (information) ,Statistical classification ,040103 agronomy & agriculture ,0401 agriculture, forestry, and fisheries ,Preprocessor ,Quality (business) ,Artificial intelligence ,business ,media_common - Abstract
In brewing industry, quality of barley accepted for malt production is essential. The visual inspection of grain for malting is performed by a qualified expert. The process is time-consuming, expensive, and still may yield unreproducible results. Therefore, there is a need for automatic systems, based on computer vision, able to verify grain properties. We present a concept of such the system, which implements image preprocessing, texture, color and shape feature extraction, supervised learning and selected classification algorithms. The results of classification are presented and discussed.
- Published
- 2017
29. QMaZda — Software tools for image analysis and pattern recognition
- Author
-
Marcin Kociolek, Piotr M. Szczypiński, and Artur Klepaczko
- Subjects
Computer science ,business.industry ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,020207 software engineering ,Pattern recognition ,02 engineering and technology ,Image segmentation ,computer.file_format ,Linear discriminant analysis ,030218 nuclear medicine & medical imaging ,03 medical and health sciences ,High-definition video ,0302 clinical medicine ,Workflow ,Software ,Pattern recognition (psychology) ,0202 electrical engineering, electronic engineering, information engineering ,OS X ,Executable ,Artificial intelligence ,business ,computer - Abstract
Qmazda is a package of software tools for digital image analysis. They compute shape, color and texture attributes in arbitrary regions of interest, implement selected algorithms of discriminant analysis and machine learning, and enable texture based image segmentation. The algorithms generalize a concept of texture to three-dimensional data to enable analysis of volumetric images from magnetic resonance imaging or computed tomography scanners. The tools support a complete workflow — from image examples as an input to classification rules as an output. The extracted knowledge can be further used in custom made image analysis systems. Here we also present an application of QMaZda to identify defective barley kernels. The cereal seeds variability is high, therefore, characterization and discriminant analysis of such the biological objects is challenging and non-trivial. The software is available free of charge and open source, with executables for Windows, Linux and OS X platforms.
- Published
- 2017
30. Segmenting time-lapse phase contrast images of adjacent NIH 3T3 cells
- Author
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Marcin Kociolek, Joe Chalfoun, Mary Brady, Alden A. Dima, Peter Bajcsy, Antonio Cardone, Michael Halter, and Adele P. Peskin
- Subjects
Histology ,Pixel ,business.industry ,Rand index ,Scale-space segmentation ,Image segmentation ,Standard deviation ,Pathology and Forensic Medicine ,Minimum spanning tree-based segmentation ,Computer vision ,Segmentation ,Artificial intelligence ,Range segmentation ,business - Abstract
Summary We present a new method for segmenting phase contrast images of NIH 3T3 fibroblast cells that is accurate even when cells are physically in contact with each other. The problem of segmentation, when cells are in contact, poses a challenge to the accurate automation of cell counting, tracking and lineage modelling in cell biology. The segmentation method presented in this paper consists of (1) background reconstruction to obtain noise-free foreground pixels and (2) incorporation of biological insight about dividing and nondividing cells into the segmentation process to achieve reliable separation of foreground pixels defined as pixels associated with individual cells. The segmentation results for a time-lapse image stack were compared against 238 manually segmented images (8219 cells) provided by experts, which we consider as reference data. We chose two metrics to measure the accuracy of segmentation: the ‘Adjusted Rand Index’ which compares similarities at a pixel level between masks resulting from manual and automated segmentation, and the ‘Number of Cells per Field’ (NCF) which compares the number of cells identified in the field by manual versus automated analysis. Our results show that the automated segmentation compared to manual segmentation has an average adjusted rand index of 0.96 (1 being a perfect match), with a standard deviation of 0.03, and an average difference of the two numbers of cells per field equal to 5.39% with a standard deviation of 4.6%.
- Published
- 2012
31. Blue Whitish Veil, Atypical Vascular Pattern and Regression Structures Detection in Skin Lesions Images
- Author
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Dariusz Czubinski, Karol Kropidlowski, Michal Strzelecki, and Marcin Kociolek
- Subjects
medicine.medical_specialty ,Computer science ,business.industry ,020207 software engineering ,Image processing ,02 engineering and technology ,Diagnostic system ,Regression ,Melanoma detection ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Computer vision ,Radiology ,Artificial intelligence ,business ,Skin lesion - Abstract
There is no suitable standard for the detection of blue whitish veil atypical vascular pattern and regression structures applied to skin lesion images. This information however is important in assessment of melanoma in skin dermatoscopic images. Thus there is a need for development of image analysis techniques that satisfy at least subjective criteria required by dermatologists. In this paper the application of color based image features for detection of blue whitish veil and atypical vas-cular pattern is presented. Preliminary test results are promising; for analyzed melanoma images the accuracy of developed methods provides 78 % correctly detected blue whitish veils, 84 % correctly detected atypical vascular pattern, and 86,5 % correctly detected regression structures. This paper is a contribution to the computer aided diagnostic system implementing the ELM 7-point check-list aimed at melanoma detection.
- Published
- 2016
32. Nevus atypical pigment network distinction and irregular streaks detection in skin lesions images
- Author
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Marcin Kociolek, Dariusz Czubinski, Michal Strzelecki, and Karol Kropidlowski
- Subjects
Computer science ,business.industry ,Histogram ,medicine ,Nevus ,Computer vision ,Artificial intelligence ,Skin lesion ,medicine.disease ,business ,Melanoma diagnosis ,Image resolution - Abstract
There is no suitable golden standard for the detection of atypical pigment network and irregular streaks applied to skin lesion images. This information however is important in assessment of melanoma in skin dermatoscopic images. Thus there is a need for development of image analysis techniques that satisfy at least subjective criteria defined by dermatologists. In this paper we present the application of histogram based features for detection of atypical pigment network and shape based features supplemented by artificial neural network for detection of irregular streaks. Preliminary test results are promising, for analyzed melanoma images we get 97,7% correctly detected pigmentation networks and 94,8% correctly detected irregular streaks. This paper constitutes the part of our efforts to implement the ELM 7-point checklist in order to support melanoma diagnosis and to automate this process.
- Published
- 2015
33. Comparison of segmentation algorithms for fluorescence microscopy images of cells
- Author
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Alden A, Dima, John T, Elliott, James J, Filliben, Michael, Halter, Adele, Peskin, Javier, Bernal, Marcin, Kociolek, Mary C, Brady, Hai C, Tang, and Anne L, Plant
- Subjects
Mice ,Microscopy, Fluorescence ,Cells ,Image Interpretation, Computer-Assisted ,Animals ,Image Enhancement ,Algorithms ,Rats - Abstract
The analysis of fluorescence microscopy of cells often requires the determination of cell edges. This is typically done using segmentation techniques that separate the cell objects in an image from the surrounding background. This study compares segmentation results from nine different segmentation techniques applied to two different cell lines and five different sets of imaging conditions. Significant variability in the results of segmentation was observed that was due solely to differences in imaging conditions or applications of different algorithms. We quantified and compared the results with a novel bivariate similarity index metric that evaluates the degree of underestimating or overestimating a cell object. The results show that commonly used threshold-based segmentation techniques are less accurate than k-means clustering with multiple clusters. Segmentation accuracy varies with imaging conditions that determine the sharpness of cell edges and with geometric features of a cell. Based on this observation, we propose a method that quantifies cell edge character to provide an estimate of how accurately an algorithm will perform. The results of this study will assist the development of criteria for evaluating interlaboratory comparability.
- Published
- 2010
34. A Multi-Layer Perceptron Network for Perfusion Parameter Estimation in DCE-MRI Studies of the Healthy Kidney
- Author
-
Artur Klepaczko, Michał Strzelecki, Marcin Kociołek, Eli Eikefjord, and Arvid Lundervold
- Subjects
dynamic contrast-enhanced MRI ,kidney perfusion ,glomerular filtration rate ,pharmacokinetic modeling ,multi-layer perceptron ,parameter estimation ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
Background: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an imaging technique which helps in visualizing and quantifying perfusion—one of the most important indicators of an organ’s state. This paper focuses on perfusion and filtration in the kidney, whose performance directly influences versatile functions of the body. In clinical practice, kidney function is assessed by measuring glomerular filtration rate (GFR). Estimating GFR based on DCE-MRI data requires the application of an organ-specific pharmacokinetic (PK) model. However, determination of the model parameters, and thus the characterization of GFR, is sensitive to determination of the arterial input function (AIF) and the initial choice of parameter values. Methods: This paper proposes a multi-layer perceptron network for PK model parameter determination, in order to overcome the limitations of the traditional model’s optimization techniques based on non-linear least-squares curve-fitting. As a reference method, we applied the trust-region reflective algorithm to numerically optimize the model. The effectiveness of the proposed approach was tested for 20 data sets, collected for 10 healthy volunteers whose image-derived GFR scores were compared with ground-truth blood test values. Results: The achieved mean difference between the image-derived and ground-truth GFR values was 2.35 mL/min/1.73 m2, which is comparable to the result obtained for the reference estimation method (−5.80 mL/min/1.73 m2). Conclusions: Neural networks are a feasible alternative to the least-squares curve-fitting algorithm, ensuring agreement with ground-truth measurements at a comparable level. The advantages of using a neural network are twofold. Firstly, it can estimate a GFR value without the need to determine the AIF for each individual patient. Secondly, a reliable estimate can be obtained, without the need to manually set up either the initial parameter values or the constraints thereof.
- Published
- 2020
- Full Text
- View/download PDF
35. Survey statistics of automated segmentations applied to optical imaging of mammalian cells
- Author
-
Peter Bajcsy, Michael Halter, Carl G. Simon, Mary Brady, Antonio Cardone, Mylene Simon, Joe Chalfoun, Adele P. Peskin, Antoine Vandecreme, Marcin Kociolek, Michael Majurski, and Derek Juba
- Subjects
Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Scale-space segmentation ,Cell segmentation ,Biochemistry ,Segmentation evaluation ,Automation ,Structural Biology ,Histogram ,Animals ,Humans ,Computer vision ,Segmentation ,Molecular Biology ,Microscopy ,Segmentation-based object categorization ,business.industry ,Applied Mathematics ,Segmented objects ,Optical Imaging ,Accelerated execution of segmentation for high-throughput biological application ,Pattern recognition ,Image segmentation ,Computer Science Applications ,Cellular measurements ,Artificial intelligence ,business ,Algorithms ,Research Article - Abstract
The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements. We define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging modalities, (3) digital data axes, (4) segmentation algorithms, (5) segmentation evaluations, (6) computational hardware platforms used for segmentation acceleration, and (7) object (cellular) measurements. Finally, all classified papers are converted programmatically into a set of hyperlinked web pages with occurrence and co-occurrence statistics of assigned categories. The survey paper presents to a reader: (a) the state-of-the-art overview of published papers about automated segmentation applied to optical microscopy imaging of mammalian cells, (b) a classification of segmentation aspects in the context of cell optical imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmentations, segmented objects, segmentation evaluations, and the use of computational platforms for accelerating segmentation execution, and (d) open research problems to pursue. The novel contributions of this survey paper are: (1) a new type of classification of cellular measurements and automated segmentation, (2) statistics about the published literature, and (3) a web hyperlinked interface to classification statistics of the surveyed papers at https://isg.nist.gov/deepzoomweb/resources/survey/index.html .
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36. Model Based Approach for Melanoma Segmentation
- Author
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Marcin Kociolek, Karol Kropidlowski, Dariusz Czubinski, and Michal Strzelecki
- Subjects
Light intensity ,Jaccard index ,Computer-aided diagnosis ,Computer science ,business.industry ,Histogram ,Scale-space segmentation ,Segmentation ,Computer vision ,Image segmentation ,Sensitivity (control systems) ,Artificial intelligence ,business - Abstract
is no suitable golden standard for assessment and comparison of segmentation methods applied to skin lesions images. Thus there is a need for development of image analysis techniques that satisfy at least subjective criteria defined by dermatologists. We present a model based approach for melanocytic image segmentation as a tool to improve computer aided diagnosis. During the research it was necessary to correct non-uniform image illumination caused by dermatoscope lightning. The correction algorithm based on dermatoscope light intensity estimation was used. The proposed segmentation method is based on histogram skin modeling. Preliminary test results are promising, for the analyzed melanoma images mean Jaccard index of 89.48% and mean sensitivity of 92.45% were obtained (when compared to expert assessment).
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