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E Granum

Publications and source records attributed to E Granum.

14 recordsLinked to original sources
PubMed2000

Classifying the illumination condition from two light sources by color histogram assessment.

We investigate and propose a method for assessment of the illumination condition covering two light sources. The method may be of some support for color vision and multispectral analysis methods that rely on a specific illumination condition. It is constrained to classifying the illumination condition for dielectric objects illuminated by two light sources. The reflected light is modeled by the dichromatic reflection model, which describes the light as the sum of its body reflections and surface reflections. Further, reflected light from an object illuminated by two light sources may give from one to four primary reflections depending on the condition, and it may be expressed as an additive mixture of these reflections. An additive mixture of two reflections expressed in chromaticities is limited to falling within the area enclosed by the chromaticities of the primary reflections of the light sources. So after finding the set of primary chromaticities enclosing the pixel points' chromaticities, it is possible for one to assess the current illumination condition. Since the method operates on pixel points globally, it is independent of illumination geometry and hence may be used on irregular objects. Two experiments are performed. One uses regular objects in a well-controlled laboratory environment and demonstrates that the pixel-point distribution is as expected. The second experiment demonstrates the method's potential use in support of spectroscopic analysis of vegetation through assessing the illumination condition of barley plants in an outdoor illumination condition.

Color↗
PubMed1991

Finding chromosome centromeres using band pattern information.

A method for finding centromeres of chromosomes using band pattern information only is described. Rather than using shape-related characteristics to identify the position of the centromere constriction, its position in relation to the band pattern is encoded into a structural band pattern model for each chromosome type individually. These models are automatically inferred from strings of symbols representing band pattern profiles. When used for analysis, a sample string is compared with a model from which the position in the string corresponding to the centromere can be derived. The approach is experimentally investigated with centromere finding both for class of the chromosomes known and unknown. A scheme for simultaneous centromere finding and classification is also proposed and tested on different band pattern representations with encouraging results.

Centromere↗
PubMed1990

Automatically inferred Markov network models for classification of chromosomal band pattern structures.

A structural pattern recognition approach to the analysis and classification of metaphase chromosome band patterns is presented. An operational method of representing band pattern profiles as sharp edged idealized profiles is outlined. These profiles are nonlinearly scaled to a few, but fixed number of "density" levels. Previous experience has shown that profiles of six levels are appropriate and that the differences between successive bands in these profiles are suitable for classification. String representations, which focuses on the sequences of transitions between local band pattern levels, are derived from such "difference profiles." A method of syntactic analysis of the band transition sequences by dynamic programming for optimal (maximal probability) string-to-network alignments is described. It develops automatic data-driven inference of band pattern models (Markov networks) per class, and uses these models for classification. The method does not use centromere information, but assumes the p-q-orientation of the band pattern profiles to be known a priori. It is experimentally established that the method can build Markov network models, which, when used for classification, show a recognition rate of about 92% on test data. The experiments used 200 samples (chromosome profiles) for each of the 22 autosome chromosome types and are designed to also investigate various classifier design problems. It is found that the use of a priori knowledge of Denver Group assignment only improved classification by 1 or 2%. A scheme for typewise normalization of the class relationship measures prove useful, partly through improvements on average results and partly through a more evenly distributed error pattern. The choice of reference of the p-q-orientation of the band patterns is found to be unimportant, and results of timing of the execution time of the analysis show that recent and efficient implementations can process one cell in less than 1 min on current standard hardware. A measure of divergence between data sets and Markov network models is shown to provide usable estimates of experimental classification performance.

Chromosome Banding↗
PubMed1989

On fully automatic feature measurement for banded chromosome classification.

Procedures for fully automatic location of chromosome axis and centromere in metaphase chromosomes are described for a practical interactive chromosome analysis system that omits the usual stages of interactive axis and centromere correction. Accuracy of centromere finding and consequential determination of a chromosome's polarity, i.e., which end is which, is measured experimentally. The saving in interaction by not correcting centromeres is compared to the increase in errors at the classification stage and the consequent increase in interaction needed to correct these errors. Some previously unreported features for banded chromosome classification are described, and in particular a set of global shape features is introduced. The discrimination capability of the feature measurements is evaluated by use of simple statistics and by reference to the performance of classifiers trained with various feature subsets. Class discrimination capability of the global shape feature set is shown to be comparable to that of centromere position, a widely used local shape feature. The variability of feature measurements that might occur in data from different laboratories on account of differing tissue, preparation methods, and digitiser hardware is assessed using three data bases of G-banded human metaphase cells. It is shown that the differences can be considerable and that appropriate feature selection and classifier training substantially improve classification performance.

Chromosomes↗
PubMed1983

An efficient multiple-cell approach to automatic aneuploidy screening.

We describe a statistical method of discriminating efficiently, on the basis of multiple-cell measurements without operator interaction, between chromosomally normal human cell lines and those either containing a single additional chromosome or missing one chromosome. We begin by defining hypothetical but realistic "confusion matrices," which give the probabilities of (1) assigning each chromosome to each of various possible groups and (2) rejecting it as unclassifiable. From these, false-positive and false-negative rates of 0.01 and 0.001, respectively, are found to be attainable by processing 16 to 32 cells if the average probability of misclassifying or rejecting individual chromosomes is 5% to 9% for "Denver" groups or 10% to 17% for homologous pairs. Since these values are probably within the reach of current technology, the method is a basis for a realistic, fully automatic screening system. We also show how the method can be extended to the detection of quite general types of chromosomally abnormal cell lines.

Aneuploidy↗
PubMed1981

Automatic chromosome analysis. II. Karyotyping of banded human chromosomes using band transition sequences.

Human chromosomes, represented by band transition sequences, chromosome area, centromeric index by area and centromeric index by density, were karyotyped by computer. A reference set of chromosomes provided frequencies of occurrence of each density class and difference class of the band transition sequence as well as of each of the three global features. The karyotyping program was designed to handle all metaphases, even those from which severely bent and overlapped chromosomes were excluded. In one experiment, 21 metaphases were karyotyped on the basis of a reference set and the results were compared with earlier results of visual analysis of band transition profiles developed from band transition sequences: 0.8% errors were made in the visual experiment and 1.4% errors were made in the computer based experiment. In a second experiment, 179 metaphases were divided into reference and test sets and karyotyped by computer with an error rate of 3.4%. By further analysis it was found that metaphases with many misclassified chromosomes could often be automatically distinguished from metaphases with few errors. Thus by automatic rejection of 7% of the metaphases the error rate could be reduced to 2.6%. The computer program for chromosome karyotyping will now be implemented in a semi-automatic system for practical clinical chromosome analysis.

Chromosome Banding↗
PubMed1980

Quantitative analysis of 6985 digitized trypsin G-banded human metaphase chromosomes.

The frequency and staining intensity of the dark bands were measured in 6985 trypsin G-banded human chromosomes, described by so-called band transition sequences which represent the chromosome banding patterns in a condensed quantitative way. In the haploid chromosome complement a maximum of 351 bands were registered: 181 white and 170 dark bands. The frequency with which bands occurred and the staining intensity of the bands differed considerably between the chromosome types. Among the dark bands the darker stained bands occurred more frequently than the lighter stained bands. A study of the relationship between the degree of chromosome contraction and the frequency of band occurrence revealed that for all chromosome types the average band frequency increased with increasing chromosome elongation. Twenty of the 23 dark bands chosen as landmarks by the Paris Conference (1971) occurred with high frequency and staining intensity. The remaining three landmarks occurred less frequently, due to fusion of dark and white bands, respectively. A study of global features showed, as expected, good agreement between chromosome length, area and density. there was good agreement between the centromeric indices determined by length and area, respectively, and no difference was found between contracted and elongated chromosomes, with the exception of the acrocentrics where elongated chromosomes showed higher centromeric indices than contracted chromosomes. Most often, the centromeric index by density differed considerably from the centromeric indices by length and area, respectively. The data presented here may be used in clinical cytogenetics as a supplement to the ISCN idiograms (ISCN 1978), as the band frequencies and staining intensities may help in identifying and characterizing specific bands.

Chromosome Banding↗
PubMed1980

Automatic chromosome analysis. I. A simple method for classification of B- and D-group chromosomes represented by band transition sequences.

This paper describes an approach to the automatic analysis of banded B- and D-group chromosomes, represented by band transition sequences (BT-sequences), using Bayes formula in a simple way. The analysis considers the 14 BT-codes constituting the BT-sequence as being independent variables. Error rates of 6-8% in classification experiments and 3-4% in karyotyping experiments are clearly smaller than those reported by other authors using other methods. If the error rates of karyotyping are adjusted for errors in the basic material, they are reduced to 2-3%. The reason for the small error rate is presumably that the BT-sequences are superior to other methods for condensed band-pattern description. The method will be incorporated into a programme system for automatic karyotyping.

Chromosome Banding↗
PubMed1979

Description of chromosome banding patterns by band transition sequences: a new basis for automated chromosome analysis.

For visual and automated analysis of banded human chromosomes, the band pattern features of chromosome profiles considered essential for the cytogeneticist were evaluated. These features were found to be related to each peak (dark band) and its adjacent valley (light band) in the direction p--q. A method for extracting and describing these features was developed and implemented on a computer. The method determines three normalized parameters for each peak and adjacent valley: (1) density of peak; (2) density difference (transition) between peak and valley; and (3) position of peak. Each profile is described by a simple sequence of band transitions (BT-sequence). The BT-sequence was visualized as a profile (BT-profile) using only the information retained in the BT-sequence. Visual classification of BT-profiles shows error rates comparable to visual classification of ordinary density profiles (Lundsteen & Granum 1979). It is therefore concluded that the BT-profiles do retain the important band pattern features of the profiles, and it is supposed that the simple and condensed BT-sequences constitute an appropriate basis for automated karyotyping.

Chromosome Banding↗
PubMed1979

Visual classification of banded human chromosomes. III. Classification and karyotyping of density profiles described by band transition sequences.

Band transition profiles (BT-profiles) representing extracted band pattern features of 898 density profiles of banded chromosomes were classified and karyotyped by a cytogeneticist in order to investigate how much information was lost by substituting for the original density profiles their extracted features. The results were evaluated and compared with visual classification and karyotyping of the same 898 density profiles from which the BT-profiles were derived. Six per cent errors were made in classification of isolated BT-profiles and 0.7% errors were made in karyotyping BT-profiles. These error rates were comparable to the corresponding error rates in classifying and karyotyping density profiles, which were 5% and 0.5%, respectively. It is concluded that most of the important band pattern information of the density profiles is retained in the BT-profiles, and it is supposed that the condensed BT-sequences (from which the BT-profiles are derived) constitute a sufficient and appropriate basis for automated karyotyping.

Chromosome Banding↗
PubMed1977

Visual classification of banded human chromosomes ii. classification and karyotyping of integrated density profiles.

Visual classification and karyotyping of 897 integrated density profiles generated from straight and non-overlapping chromosomes from 22 trypsin-banded metaphases of average quality was carried out and evaluated. The results were compared with visual classification of photographic prints of the same 897 chromosomes. The experiments were carried out by one observer. About 5% errors were made in classification of isolated profiles; 0-5% errors were made in karyotyping profiles and about 3% errors were made in classification of isolated chromosome prints. The reason for the small error rate obtained by karyotyping profiles as compared to the error rate when classifying isolated profiles was assumed to be the use of a priori knowledge of the composition of (normal) metaphases and the possibility of making appropriate comparisons between the individual profiles within the metaphase. Comparison between classification of isolated prints and of profiles showed different error patterns on the basis of which it was assumed that prints constitute a better basis for visual classification than profiles. The results seemed to indicate two ways of improving computer classification of banded chromosomes: (1) information of value in the chromosomes (band pattern, shape etc.) should be extracted from the digitized chromosome image in a manner superior to the simple integration by which profiles are produced; (2) computer karyotyping should simulate the human method, thus taking advantage of a priori knowledge of the composition of the metaphases and being able to make appropriate comparisons between individual chromosomes.

Chromosomes↗
PubMed1976

Visual classification of banded human chromosomes. I. Karyotyping compared with classification of isolated chromosomes.

Visual karyotyping and visual classification of isolated chromosomes was carried out by seven investigators on 22 trypsin banded metaphases of average quality. The karyotyping experiment resulted in an average error rate of 0-1% (zero-0-4%) and the classification of isolated chromosomes resulted in an error rate of 3% (2-5%). The B and F group chromosomes were found to be most difficult to classify when isolated, while no errors were made of the no. 1 and the X chromosome. Large differences were seen in the resulting error pattern for the individual investigators both with regard to their total error rate and also the chromosome types which they most frequently misclassified. Based upon these error patterns it is suggested that more than 95% of the chromosomes in an average quality material contain features upon which a reliable visual classification can be made. Thus there may be a potential possibility that these chromosomes may be classified by computer on the basis of these features. The fact that visual karyotyping is much more reliable than visual classification of isolated chromosomes indicated that computer classification of chromosomes should include programming capable of making appropriate comparison between the chromosomes in the metaphase and at the same time take into account the expected presence of 23 chromosome pairs for normal cells. This would simulate the human performance of visual karyotyping and make a classification possible of at least some of the remaining 5% difficult chromosomes.

Chromosomes↗