scholarly journals Texture analysis of cervical cell nuclei by segmentation of chromatin patterns.

1979 ◽  
Vol 27 (1) ◽  
pp. 199-203 ◽  
Author(s):  
A W Smeulders ◽  
L Leyte-Veldstra ◽  
J S Ploem ◽  
C J Cornelisse
Keyword(s):  
Texture Analysis ◽  
Single Cell Analysis ◽  
Automated Classification ◽  
Cell Nuclei ◽  
Step Size ◽  
Data Set ◽  
Chromatin Pattern ◽  
Cervical Cell ◽  
Texture Parameters ◽  
Visual Properties

Texture parameters of the nuclear chromatin pattern can contribute to the automated classification of specimens on the basis of single cell analysis in cervical cytology. Current texture parameters are abstract and therefore hamper understanding. In this paper texture parameters are described that can be derived from the chromatin pattern after segmentation of the nuclear image. These texture parameters are more directly related to the visual properties of the chromatin pattern. The image segmentation procedure is based on a region grow algorithm which specifically isolates high chromatin density. The texture analysis method has been tested on a data set of images of 112 cervical nuclei on photographic negatives digitized with a step size of 0.125 micron. The preliminary results of a classification trial indicate that these visually interpretable parameters have promising discriminatory power for the distinction between negative and positive specimens.

scholarly journals Markovian analysis of cervical cell images.

1976 ◽  
Vol 24 (1) ◽  
pp. 138-144 ◽  
Author(s):  
N J Pressman
Keyword(s):  
Transition Probabilities ◽  
Classification Performance ◽  
Error Rates ◽  
Classification Error ◽  
Correct Classification ◽  
Cell Nuclei ◽  
Step Size ◽  
Cervical Cell ◽  
Texture Parameters ◽  
Markovian Analysis

Markovian analysis is a method to measure optical texture based on gray-level transition probabilities in digitized images. Experiments are described that investigate that classification performance of parameters generated by Markovian analysis. Results using Markov texture parameters show that the selection of a Markov step size strongly affects classification error rates and the number of parameters required to achieve the maximum correct classification rates. Markov texture parameters are shown to achieve high rates of correct classification in discriminating images of normal from abnormal cervical cell nuclei.

Deep Learning Based Classification of Time Series of Chaotic Systems over Graphic Images

2021 ◽  
Author(s):  
Süleyman UZUN ◽  
Sezgin KAÇAR ◽  
Burak ARICIOĞLU
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Transfer Learning ◽  
Chaotic Systems ◽  
Initial Conditions ◽  
Step Size ◽  
Learning Methods ◽  
Data Set ◽  
Graphic Images ◽  
Different Chaotic Systems

Abstract In this study, for the first time in the literature, identification of different chaotic systems by classifying graphic images of their time series with deep learning methods is aimed. For this purpose, a data set is generated that consists of the graphic images of time series of the most known three chaotic systems: Lorenz, Chen, and Rossler systems. The time series are obtained for different parameter values, initial conditions, step size and time lengths. After generating the data set, a high-accuracy classification is performed by using transfer learning method. In the study, the most accepted deep learning models of the transfer learning methods are employed. These models are SqueezeNet, VGG-19, AlexNet, ResNet50, ResNet101, DenseNet201, ShuffleNet and GoogLeNet. As a result of the study, classification accuracy is found between 96% and 97% depending on the problem. Thus, this study makes association of real time random signals with a mathematical system possible.

scholarly journals Inferring health conditions from fMRI-graph data

2018 ◽  
Author(s):  
PierGianLuca Porta Mana ◽  
Claudia Bachmann ◽  
Abigail Morrison
Keyword(s):  
Binary Classification ◽  
Disease Diagnosis ◽  
Health Condition ◽  
Automated Classification ◽  
Imaging Data ◽  
Data Set ◽  
Sufficient Statistics ◽  
Conjugate Priors ◽  
Healthy Control ◽  
Partial Exchangeability

Automated classification methods for disease diagnosis are currently in the limelight, especially for imaging data. Classification does not fully meet a clinician's needs, however: in order to combine the results of multiple tests and decide on a course of treatment, a clinician needs the likelihood of a given health condition rather than binary classification yielded by such methods. We illustrate how likelihoods can be derived step by step from first principles and approximations, and how they can be assessed and selected, using fMRI data from a publicly available data set containing schizophrenic and healthy control subjects, as a working example. We start from the basic assumption of partial exchangeability, and then the notion of sufficient statistics and the "method of translation" (Edgeworth, 1898) combined with conjugate priors. This method can be used to construct a likelihood that can be used to compare different data-reduction algorithms. Despite the simplifications and possibly unrealistic assumptions used to illustrate the method, we obtain classification results comparable to previous, more realistic studies about schizophrenia, whilst yielding likelihoods that can naturally be combined with the results of other diagnostic tests.

scholarly journals ON A KNOWLEDGE-BASED APPROACH TO THE CLASSIFICATION OF MOBILE LASER SCANNING POINT CLOUDS

2018 ◽  
Vol XLII-4 ◽  
pp. 343-349 ◽  
Author(s):  
M. Lemmens
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Decision Rules ◽  
Point Clouds ◽  
Bench Mark ◽  
Automated Classification ◽  
Initial Experiment ◽  
Data Set ◽  
Knowledge Based

<p><strong>Abstract.</strong> A knowledge-based system exploits the knowledge, which a human expert uses for completing a complex task, through a database containing decision rules, and an inference engine. Already in the early nineties knowledge-based systems have been proposed for automated image classification. Lack of success faded out initial interest and enthusiasm, the same fate neural networks struck at that time. Today the latter enjoy a steady revival. This paper aims at demonstrating that a knowledge-based approach to automated classification of mobile laser scanning point clouds has promising prospects. An initial experiment exploiting only two features, height and reflectance value, resulted in an overall accuracy of 79<span class="thinspace"></span>% for the Paris-rue-Madame point cloud bench mark data set.</p>

IMMU-51. SINGLE-CELL ANALYSIS OF TUMOR-ASSOCIATED MICROGLIA AND MACROPHAGES FROM HUMAN GLIOBLASTOMA

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi104-vi104
Author(s):  
Atul Anand ◽  
Rikke Sick Andersen ◽  
Mark Burton ◽  
Dylan Scott Lykke Harwood ◽  
Frantz Rom Poulsen ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Primary Brain Tumor ◽  
Tumor Type ◽  
Cell Analysis ◽  
Data Set ◽  
Human Glioblastoma ◽  
Glioblastoma Patient ◽  
Single Cell Rna Sequencing ◽  
Therapeutic Problem

Abstract Patients with glioblastoma, the most frequent and malignant primary brain tumor type, have a poor prognosis with a median survival of 14 months. A major therapeutic problem is chemoresistance. In surgically removed glioblastoma tissue, tumor-associated microglia and macrophages (TAMs) constitute up to 30 % of the total cells. TAMs are capable of secreting cytokines, chemokines and growth factors, thereby influencing the tumor microenvironment. However, the existence of different TAM subtypes and their role in glioblastoma is not fully comprehended and rarely considered therapeutically. This could explain why many glioblastoma clinical trials fail despite of promising preclinical results. This project aims to interrogate the existence and characteristics of different TAM subtypes in human glioblastoma biopsies in order to identify novel subpopulations and therapeutic targets. To study the heterogeneity in TAMs, CD11b+ cells were isolated from glioblastoma patient′s tissue, and single-cell RNA sequencing was performed using the 10X Genomics Chromium platform for single-cell generation and an Illumina NovaSeq6000 system for sequencing. We have sequenced TAMs from three glioblastomas and CD11b+ cells from brain tissue adjacent to two brain metastases samples. In the filtered data set of almost 71,000 CD11b+ cells, we were able to identify recently described TAM populations, such as an interferon-induced, a phagocytic, a hypoxic and a proliferating subset. Interestingly, we also discovered potential novel TAM subsets, such as a pro-angiogenic subset. We have detected a TAM population which is more complex than the established M1 and M2 phenotypes, constituting novel TAM subsets. We are currently investigating these findings to validate specific markers associated with these subpopulations, and for the identification of novel clinically relevant targets.

scholarly journals octoFLU: Automated Classification for the Evolutionary Origin of Influenza A Virus Gene Sequences Detected in U.S. Swine

2019 ◽  
Vol 8 (32) ◽  
Author(s):  
Jennifer Chang ◽  
Tavis K. Anderson ◽  
Michael A. Zeller ◽  
Phillip C. Gauger ◽  
Amy L. Vincent
Keyword(s):  
Influenza A Virus ◽  
Influenza A ◽  
Antigenic Drift ◽  
Evolutionary Origin ◽  
Automated Classification ◽  
Gene Sequences ◽  
Influenza A Viruses ◽  
Query Gene ◽  
Data Set ◽  
Virus Gene

The diversity of the 8 genes of influenza A viruses (IAV) in swine reflects introductions from nonswine hosts and subsequent antigenic drift and shift. Here, we curated a data set and present a pipeline that assigns evolutionary lineage and genetic clade to query gene segments.

scholarly journals ALTEA: A Software Tool for the Evaluation of New Biomarkers for Alzheimer’s Disease by Means of Textures Analysis on Magnetic Resonance Images

Diagnostics ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 47 ◽  
Author(s):  
Carlos López-Gómez ◽  
Rafael Ortiz-Ramón ◽  
Enrique Mollá-Olmos ◽  
David Moratal ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Texture Analysis ◽  
Software Tool ◽  
Magnetic Resonance Images ◽  
Gray Level ◽  
Predictive Capacity ◽  
Texture Parameters ◽  
New Biomarkers

The current criteria for diagnosing Alzheimer’s disease (AD) require the presence of relevant cognitive deficits, so the underlying neuropathological damage is important by the time the diagnosis is made. Therefore, the evaluation of new biomarkers to detect AD in its early stages has become one of the main research focuses. The purpose of the present study was to evaluate a set of texture parameters as potential biomarkers of the disease. To this end, the ALTEA (ALzheimer TExture Analyzer) software tool was created to perform 2D and 3D texture analysis on magnetic resonance images. This intuitive tool was used to analyze textures of circular and spherical regions situated in the right and left hippocampi of a cohort of 105 patients: 35 AD patients, 35 patients with early mild cognitive impairment (EMCI) and 35 cognitively normal (CN) subjects. A total of 25 statistical texture parameters derived from the histogram, the Gray-Level Co-occurrence Matrix and the Gray-Level Run-Length Matrix, were extracted from each region and analyzed statistically to study their predictive capacity. Several textural parameters were statistically significant (p < 0.05) when differentiating AD subjects from CN and EMCI patients, which indicates that texture analysis could help to identify the presence of AD.

scholarly journals Revisiting the Fanaroff–Riley dichotomy and radio-galaxy morphology with the LOFAR Two-Metre Sky Survey (LoTSS)

2019 ◽  
Vol 488 (2) ◽  
pp. 2701-2721 ◽  
Author(s):  
B Mingo ◽  
J H Croston ◽  
M J Hardcastle ◽  
P N Best ◽  
K J Duncan ◽  
...  
Keyword(s):  
Radio Galaxy ◽  
Large Population ◽  
Automated Classification ◽  
Host Galaxies ◽  
Data Set ◽  
Extended Radio ◽  
Sky Survey ◽  
Low Surface Brightness ◽  
Extended Sources ◽  
Galaxy Population

Abstract The relative positions of the high and low surface brightness regions of radio-loud active galaxies in the 3CR sample were found by Fanaroff and Riley to be correlated with their luminosity. We revisit this canonical relationship with a sample of 5805 extended radio-loud active galactic nuclei (AGN) from the LOFAR Two-Metre Sky Survey (LoTSS), compiling the most complete data set of radio-galaxy morphological information obtained to date. We demonstrate that, for this sample, radio luminosity does not reliably predict whether a source is edge-brightened (FRII) or centre-brightened (FRI). We highlight a large population of low-luminosity FRIIs, extending three orders of magnitude below the traditional FR break, and demonstrate that their host galaxies are on average systematically fainter than those of high-luminosity FRIIs and of FRIs matched in luminosity. This result supports the jet power/environment paradigm for the FR break: low-power jets may remain undisrupted and form hotspots in lower mass hosts. We also find substantial populations that appear physically distinct from the traditional FR classes, including candidate restarting sources and ‘hybrids’. We identify 459 bent-tailed sources, which we find to have a significantly higher SDSS cluster association fraction (at z &lt; 0.4) than the general radio-galaxy population, similar to the results of previous work. The complexity of the LoTSS faint, extended radio sources not only demonstrates the need for caution in the automated classification and interpretation of extended sources in modern radio surveys, but also reveals the wealth of morphological information such surveys will provide and its value for advancing our physical understanding of radio-loud AGN.

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