59,577 research outputs found
A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells
Leukemia (blood cancer) is an unusual spread of White Blood Cells or
Leukocytes (WBCs) in the bone marrow and blood. Pathologists can diagnose
leukemia by looking at a person's blood sample under a microscope. They
identify and categorize leukemia by counting various blood cells and
morphological features. This technique is time-consuming for the prediction of
leukemia. The pathologist's professional skills and experiences may be
affecting this procedure, too. In computer vision, traditional machine learning
and deep learning techniques are practical roadmaps that increase the accuracy
and speed in diagnosing and classifying medical images such as microscopic
blood cells. This paper provides a comprehensive analysis of the detection and
classification of acute leukemia and WBCs in the microscopic blood cells.
First, we have divided the previous works into six categories based on the
output of the models. Then, we describe various steps of detection and
classification of acute leukemia and WBCs, including Data Augmentation,
Preprocessing, Segmentation, Feature Extraction, Feature Selection (Reduction),
Classification, and focus on classification step in the methods. Finally, we
divide automated detection and classification of acute leukemia and WBCs into
three categories, including traditional, Deep Neural Network (DNN), and mixture
(traditional and DNN) methods based on the type of classifier in the
classification step and analyze them. The results of this study show that in
the diagnosis and classification of acute leukemia and WBCs, the Support Vector
Machine (SVM) classifier in traditional machine learning models and
Convolutional Neural Network (CNN) classifier in deep learning models have
widely employed. The performance metrics of the models that use these
classifiers compared to the others model are higher
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The detection and classification of blast cell in Leukaemia Acute Promyelocytic Leukaemia (AML M3) blood using simulated annealing and neural networks
This paper was delivered at AIME 2011: 13th Conference on Artifical Intelligence in Medicine.This paper presents a method for the detection and classification of blast cells in M3 with others sub-types using simulated annealing and neural networks. In this paper, we increased our test result from 10 images to 20 images. We performed Hill Climbing, Simulated Annealing and Genetic Algorithms for detecting the blast cells. As a result, simulated annealing is the “best” heuristic search for detecting the leukaemia cells. From the detection, we performed features extraction on the blast cells and we classifying based on M3 and other sub-types using neural networks. We received convincing result which has targeting around 97% in classifying of M3 with other sub-types. Our results are based on real world image data from a Haematology Department.Universiti Sains Islam Malaysia and the Ministry of Higher Education, Malaysi
Dual-wavelength thulium fluoride fiber laser based on SMF-TMSIF-SMF interferometer as potential source for microwave generationin 100-GHz region
A dual-wavelength thulium-doped fluoride
fiber (TDFF) laser is presented. The generation of the TDFF
laser is achieved with the incorporation of a single modemultimode-
single mode (SMS) interferometer in the laser
cavity. The simple SMS interferometer is fabricated using the
combination of two-mode step index fiber and single-mode fiber.
With this proposed design, as many as eight stable laser lines
are experimentally demonstrated. Moreover, when a tunable
bandpass filter is inserted in the laser cavity, a dual-wavelength
TDFF laser can be achieved in a 1.5-μm region. By heterodyning
the dual-wavelength laser, simulation results suggest that the
generated microwave signals can be tuned from 105.678 to
106.524 GHz with a constant step of �0.14 GHz. The presented
photonics-based microwave generation method could provide
alternative solution for 5G signal sources in 100-GHz region
Classification of large circulating tumor cells isolated with ultra-high throughput microfluidic Vortex technology.
Circulating tumor cells (CTCs) are emerging as rare but clinically significant non-invasive cellular biomarkers for cancer patient prognosis, treatment selection, and treatment monitoring. Current CTC isolation approaches, such as immunoaffinity, filtration, or size-based techniques, are often limited by throughput, purity, large output volumes, or inability to obtain viable cells for downstream analysis. For all technologies, traditional immunofluorescent staining alone has been employed to distinguish and confirm the presence of isolated CTCs among contaminating blood cells, although cells isolated by size may express vastly different phenotypes. Consequently, CTC definitions have been non-trivial, researcher-dependent, and evolving. Here we describe a complete set of objective criteria, leveraging well-established cytomorphological features of malignancy, by which we identify large CTCs. We apply the criteria to CTCs enriched from stage IV lung and breast cancer patient blood samples using the High Throughput Vortex Chip (Vortex HT), an improved microfluidic technology for the label-free, size-based enrichment and concentration of rare cells. We achieve improved capture efficiency (up to 83%), high speed of processing (8 mL/min of 10x diluted blood, or 800 μL/min of whole blood), and high purity (avg. background of 28.8±23.6 white blood cells per mL of whole blood). We show markedly improved performance of CTC capture (84% positive test rate) in comparison to previous Vortex designs and the current FDA-approved gold standard CellSearch assay. The results demonstrate the ability to quickly collect viable and pure populations of abnormal large circulating cells unbiased by molecular characteristics, which helps uncover further heterogeneity in these cells
Methods for Analysing Endothelial Cell Shape and Behaviour in Relation to the Focal Nature of Atherosclerosis
The aim of this thesis is to develop automated methods for the analysis of the
spatial patterns, and the functional behaviour of endothelial cells, viewed under
microscopy, with applications to the understanding of atherosclerosis.
Initially, a radial search approach to segmentation was attempted in order to
trace the cell and nuclei boundaries using a maximum likelihood algorithm; it
was found inadequate to detect the weak cell boundaries present in the available
data. A parametric cell shape model was then introduced to fit an equivalent
ellipse to the cell boundary by matching phase-invariant orientation fields of the
image and a candidate cell shape. This approach succeeded on good quality
images, but failed on images with weak cell boundaries. Finally, a support
vector machines based method, relying on a rich set of visual features, and a
small but high quality training dataset, was found to work well on large numbers
of cells even in the presence of strong intensity variations and imaging noise.
Using the segmentation results, several standard shear-stress dependent parameters
of cell morphology were studied, and evidence for similar behaviour
in some cell shape parameters was obtained in in-vivo cells and their nuclei.
Nuclear and cell orientations around immature and mature aortas were broadly
similar, suggesting that the pattern of flow direction near the wall stayed approximately
constant with age. The relation was less strong for the cell and
nuclear length-to-width ratios.
Two novel shape analysis approaches were attempted to find other properties
of cell shape which could be used to annotate or characterise patterns, since a
wide variability in cell and nuclear shapes was observed which did not appear
to fit the standard parameterisations. Although no firm conclusions can yet be
drawn, the work lays the foundation for future studies of cell morphology.
To draw inferences about patterns in the functional response of cells to flow,
which may play a role in the progression of disease, single-cell analysis was performed
using calcium sensitive florescence probes. Calcium transient rates were
found to change with flow, but more importantly, local patterns of synchronisation
in multi-cellular groups were discernable and appear to change with flow.
The patterns suggest a new functional mechanism in flow-mediation of cell-cell
calcium signalling
Entropy of leukemia on multidimensional morphological and molecular landscapes
Leukemia epitomizes the class of highly complex diseases that new
technologies aim to tackle by using large sets of single-cell level
information. Achieving such goal depends critically not only on experimental
techniques but also on approaches to interpret the data. A most pressing issue
is to identify the salient quantitative features of the disease from the
resulting massive amounts of information. Here, I show that the entropies of
cell-population distributions on specific multidimensional molecular and
morphological landscapes provide a set of measures for the precise
characterization of normal and pathological states, such as those corresponding
to healthy individuals and acute myeloid leukemia (AML) patients. I provide a
systematic procedure to identify the specific landscapes and illustrate how,
applied to cell samples from peripheral blood and bone marrow aspirates, this
characterization accurately diagnoses AML from just flow cytometry data. The
methodology can generally be applied to other types of cell-populations and
establishes a straightforward link between the traditional statistical
thermodynamics methodology and biomedical applications.Comment: 15 pages, 4 figures, and supplementary informatio
The malaria system microApp: A new, mobile device-based tool for malaria diagnosis
Background: Malaria is a public health problem that affects remote areas worldwide. Climate change has contributed to the problem by allowing for the survival of Anopheles in previously uninhabited areas. As such, several groups have made developing news systems for the automated diagnosis of malaria a priority.
Objective: The objective of this study was to develop a new, automated, mobile device-based diagnostic system for malaria. The system uses Giemsa-stained peripheral blood samples combined with light microscopy to identify the Plasmodium falciparum species in the ring stage of development.
Methods: The system uses image processing and artificial intelligence techniques as well as a known face detection algorithm to identify Plasmodium parasites. The algorithm is based on integral image and haar-like features concepts, and makes use of weak classifiers with adaptive boosting learning. The search scope of the learning algorithm is reduced in the preprocessing step by removing the background around blood cells.
Results: As a proof of concept experiment, the tool was used on 555 malaria-positive and 777 malaria-negative previously-made slides. The accuracy of the system was, on average, 91%, meaning that for every 100 parasite-infected samples, 91 were identified correctly.
Conclusions: Accessibility barriers of low-resource countries can be addressed with low-cost diagnostic tools. Our system, developed for mobile devices (mobile phones and tablets), addresses this by enabling access to health centers in remote communities, and importantly, not depending on extensive malaria expertise or expensive diagnostic detection equipment.Peer ReviewedPostprint (published version
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