scholarly journals AUTOMATED CLASSIFICATION OF NORMAL AND ABNORMAL LEUKOCYTES

1974 ◽  
Vol 22 (7) ◽  
pp. 697-706 ◽  
Author(s):  
J. F. BRENNER ◽  
P. W. NEURATH ◽  
W. D. SELLES ◽  
T. F. NECHELES ◽  
E. S. GELSEMA ◽  
...  
Keyword(s):  
Automated System ◽  
Classification Error ◽  
Correct Classification ◽  
Automated Classification ◽  
Texture Parameters ◽  
Blood Smears ◽  
Definition Of ◽  
Peripheral Blood Smears ◽  
Optimal Set

The development of an automated system for counting and classifying normal and abnormal leukocytes in peripheral blood smears is described. General requirements are discussed and the results of a simulation experiment are presented. A sample of 1572 leukocytes, divided equally among 17 types, was photographed and analyzed using computerized pattern recognition techniques. Various geometrical, color and texture parameters were extracted from the cell images and an optimal set of 20 were used in several computerized classification runs. Training on one-half of the sample and classifying the other half resulted in an over-all correct classification of between 67 and 77% depending on the definition of classification error. When only normal cells are considered, correct classification is obtained for 9l.5% of the cells.

Performance of the CellaVision DC-1 digital cell imaging analyser for differential counting and morphological classification of blood cell

2021 ◽  
pp. jclinpath-2021-207863
Author(s):  
Lisa N van der Vorm ◽  
Henriët A Hendriks ◽  
Simone M Smits
Keyword(s):  
Blood Cell ◽  
Peripheral Blood ◽  
Cell Imaging ◽  
Small Satellite ◽  
Morphological Classification ◽  
Clinical Sensitivity ◽  
Blood Smears ◽  
Low Volume ◽  
Peripheral Blood Smears

AimsRecently, a new automated digital cell imaging analyser (Sysmex CellaVision DC-1), intended for use in low-volume and small satellite laboratories, has become available. The purpose of this study was to compare the performance of the DC-1 with the Sysmex DI-60 system and the gold standard, manual microscopy.MethodsWhite blood cell (WBC) differential counts in 100 normal and 100 abnormal peripheral blood smears were compared between the DC-1, the DI-60 and manual microscopy to establish accuracy, within-run imprecision, clinical sensitivity and specificity. Moreover, the agreement between precharacterisation and postcharacterisation of red blood cell (RBC) morphological abnormalities was determined for the DC-1.ResultsWBC preclassification and postclassification results of the DC-1 showed good correlation compared with DI-60 results and manual microscopy. In addition, the within-run SD of the DC-1 was below 1 for all five major WBC classes, indicating good reproducibility. Clinical sensitivity and specificity were, respectively, 96.7%/95.9% compared with the DI-60% and 96.6%/95.3% compared with manual microscopy. The overall agreement on RBC morphology between the precharacterisation and postcharacterisation results ranged from 49% (poikilocytosis) to 100% (hypochromasia, microcytosis and macrocytosis).ConclusionsThe DC-1 has proven to be an accurate digital cell imaging system for differential counting and morphological classification of WBCs and RBCs in peripheral blood smears. It is a compact and easily operated instrument that can offer low-volume and small satellite laboratories the possibilities of readily available blood cell analysis that can be stored and retrieved for consultation with remote locations.

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.

Immunohistochemical classification of acute leukemias using peripheral blood smears

1987 ◽  
Vol 11 (5) ◽  
pp. 475-480 ◽  
Author(s):  
John F. Vago ◽  
Paul E. Hurtubise ◽  
Orlando J. Martelo ◽  
Steven H. Swerdlow
Keyword(s):  
Peripheral Blood ◽  
Acute Leukemias ◽  
Blood Smears ◽  
Peripheral Blood Smears

scholarly journals Isolation and two-step classification of normal white blood cells in peripheral blood smears

2012 ◽  
Vol 3 (1) ◽  
pp. 13 ◽  
Author(s):  
Nisha Ramesh ◽  
MohammedE Salama ◽  
Bryan Dangott ◽  
Tolga Tasdizen
Keyword(s):  
Peripheral Blood ◽  
Blood Cells ◽  
White Blood Cells ◽  
Blood Smears ◽  
Peripheral Blood Smears

Taxonomic congruence in three populations of Quebec brook trout, Salvelinus fontinalis (Mitchill)

1979 ◽  
Vol 57 (10) ◽  
pp. 1998-2009 ◽  
Author(s):  
Jacquie McGlade ◽  
Hugh MacCrimmon
Keyword(s):  
Brook Trout ◽  
Salvelinus Fontinalis ◽  
Function Analysis ◽  
Taxonomic Status ◽  
Electrophoretic Analysis ◽  
Morphological Characterization ◽  
Correct Classification ◽  
Definition Of ◽  
Insight Into

The degree of taxonomic congruence between three methods of classification is examined for three Quebec populations of wild brook trout, Salvelinus fontinalis, from the Matamek–Moisie watersheds. The three methods utilize morphometric, meristic, and electrophoretic characters. Electrophoretic comparison is also made between the Quebec samples and brook trout from the Dorion Hatchery, Ontario. Using discriminant function analysis on data for the three Quebec populations, a subset of nine morphometric measurements was identified with between 84.0 and 92.6% correct classification, and a meristic subset of four characters, including mandibular pores, with between 72.5 and 95.0% correct classification. Electrophoretic analysis has identified a number of newly defined alleles at four loci, from five tissues. The meristic and electrophoretic analyses show congruence in the order of similarity and, therefore, are considered the most suitable methods for classification of the brook trout. Morphometric analysis provides an insight into localized adaptation particularly in response to habitat and resource utilization. An understanding of the taxonomic status of populations of brook trout requires not only a definition of individuals in terms of meristic and electrophoretic profiles, but also morphological characterization, to increase our understanding of their ecology and response to other faunal components.

Development and Evaluation of Fuzzy Criteria for the Diagnosis of Rheumatoid Arthritis

1996 ◽  
Vol 35 (04/05) ◽  
pp. 334-342 ◽  
Author(s):  
K.-P. Adlassnig ◽  
G. Kolarz ◽  
H. Leitich
Keyword(s):  
Rheumatoid Arthritis ◽  
Reference Model ◽  
American Rheumatism Association ◽  
Uniform Definition ◽  
Sensitivity Rate ◽  
Fuzzy Techniques ◽  
Definition Of ◽  
Different Levels ◽  
Specificity Rate

Abstract:In 1987, the American Rheumatism Association issued a set of criteria for the classification of rheumatoid arthritis (RA) to provide a uniform definition of RA patients. Fuzzy set theory and fuzzy logic were used to transform this set of criteria into a diagnostic tool that offers diagnoses at different levels of confidence: a definite level, which was consistent with the original criteria definition, as well as several possible and superdefinite levels. Two fuzzy models and a reference model which provided results at a definite level only were applied to 292 clinical cases from a hospital for rheumatic diseases. At the definite level, all models yielded a sensitivity rate of 72.6% and a specificity rate of 87.0%. Sensitivity and specificity rates at the possible levels ranged from 73.3% to 85.6% and from 83.6% to 87.0%. At the superdefinite levels, sensitivity rates ranged from 39.0% to 63.7% and specificity rates from 90.4% to 95.2%. Fuzzy techniques were helpful to add flexibility to preexisting diagnostic criteria in order to obtain diagnoses at the desired level of confidence.

Sign in / Sign up

Export Citation Format

Share Document