The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia

Quantitative examination of prostate histology offers clues in the diagnostic classification of lesions and in the prediction of response to treatment and prognosis. To facilitate the collection of quantitative data, the development of machine vision systems is necessary. This study explored the use of imaging for identifying tissue abnormalities in prostate histology. Medium-power histological scenes were recorded from whole-mount radical prostatectomy sections at × 40 objective magnification and assessed by a pathologist as exhibiting stroma, normal tissue (nonneoplastic epithelial component), or prostatic carcinoma (PCa). A machine vision system was developed that divided the scenes into subregions of 100 × 100 pixels and subjected each to image-processing techniques. Analysis of morphological characteristics allowed the identification of normal tissue. Analysis of image texture demonstrated that Haralick feature 4 was the most suitable for discriminating stroma from PCa. Using these morphological and texture measurements, it was possible to define a classification scheme for each subregion. The machine vision system is designed to integrate these classification rules and generate digital maps of tissue composition from the classification of subregions; 79.3% of subregions were correctly classified. Established classification rates have demonstrated the validity of the methodology on small scenes; a logical extension was to apply the methodology to whole slide images via scanning technology. The machine vision system is capable of classifying these images. The machine vision system developed in this project facilitates the exploration of morphological and texture characteristics in quantifying tissue composition. It also illustrates the potential of quantitative methods to provide highly discriminatory information in the automated identification of prostatic lesions using computer vision.

The present-day chemical composition of the LMC

High-resolution observations of five OB-type main-sequence stars in the Large Magellanic Cloud (LMC) have been obtained with the UCL echelle spectrograph on the 3.9-m Anglo-Australian Telescope. These spectra have been analysed using LTE model- atmosphere techniques, to derive stellar atmospheric parameters and chemical compositions. As these stars are located within the hydrogen burning main-sequence band, their surface abundances should reflect those of the present-day interstellar medium. Detailed line-by-line differential analyses have been undertaken relative to Galactic comparison stars. We conclude that there exists a general metal deficiency of - 0.31 +/- 0.04 dex within the LMC, and find no significant abundance variations between cluster and field stars. There is also tentative evidence to suggest a lower oxygen to iron abundance ratio, and an over-deficiency of magnesium relative to the other alpha-elements. These are discussed in terms of previous abundance analyses and models of discontinuous (or bursting) star formation within the LMC. Finally, there is some evidence to suggest a greater chemical enrichment of material within the H. region LH104.

The present-day chemical composition of the SMC from UVES spectra of the sharp-lined, B-type dwarf AV 304

High-resolution spectroscopic VLT/UVES observations are presented for the B-type main-sequence star, AV 304, in the Small Magellanic Cloud (SMC). These spectra have been analysed using LTE model-atmosphere techniques, to derive stellar atmospheric parameters and chemical compositions. As AV 304 is located within the hydrogen burning main-sequence band, its chemical composition should reflect that of the SMC interstellar medium (ISM). A detailed line-by-line differential analysis has been undertaken relative to a Galactic comparison star. A general metal deficiency for the a-process elements O, Si & S of -0.43 +/- 0.05 dex is found for AV 304, with iron having a similar underabundance. Oxygen may be relatively over- abundant by similar to0.1 dex and carbon and aluminium underabundant by similar to0.2 dex. A large nitrogen underabundance (of -1.2 dex relative to hydrogen and -0.7 dex relative to iron) is found. This is interpreted in terms of the CNO bi-cycle having been suppressed in the SMC. Furthermore, the large nitrogen deficiency is in excellent agreement with that found for SMC H II regions. Indeed, this represents a first for stellar astrophysics - confirming the low base-line nitrogen composition of the SMC ISM (viz. 12+log(N/H) similar to 6.66 +/- 0.10 dex).

Chemical composition of B-type supergiants in the OB 8, OB 10, OB 48, OB 78 associations of M 31

Absolute and differential chemical abundances are presented for the largest group of massive stars in M31 studied to date. These results were derived from intermediate resolution spectra of seven B-type supergiants, lying within four OB associations covering a galactocentric distance of 5-12 kpc. The results are mainly based on an LTE analysis, and we additionally present a full non-LTE, unified model atmosphere analysis of one star (OB 78-277) to demonstrate the reliability of the differential LTE technique. A comparison of the stellar oxygen abundance with that of previous nebular results shows that there is an off set of between similar to0.15-0.4 dex between the two methods which is critically dependent on the empirical calibration adopted for the R 23 parameter with [O/H]. However within the typical errors of the stellar and nebular analyses (and given the strength of dependence of the nebular results on the calibration used) the oxygen abundances determined in each method are fairly consistent. We determine the radial oxygen abundance gradient from these stars, and do not detect any systematic gradient across this galactocentric range. We find that the inner regions of M31 are not, as previously thought, very "metal rich". Our abundances of C, N, O, Mg, Si, Al, S and Fe in the M31 supergiants are very similar to those of massive stars in the solar neighbourhood.

Prediction of adipose tissue composition using Raman spectroscopy: average properties and individual fatty acids.

Abstract: Raman spectroscopy has been used for the first time to predict the FA composition of unextracted adipose tissue of pork, beef, lamb, and chicken. It was found that the bulk unsaturation parameters could be predicted successfully [R-2 = 0.97, root mean square error of prediction (RMSEP) = 4.6% of 4 sigma], with cis unsaturation, which accounted for the majority of the unsaturation, giving similar correlations. The combined abundance of all measured PUFA (>= 2 double bonds per chain) was also well predicted with R-2 = 0.97 and RMSEP = 4.0% of 4 sigma. Trans unsaturation was not as well modeled (R-2 = 0.52, RMSEP = 18% of 4 sigma); this reduced prediction ability can be attributed to the low levels of trans FA found in adipose tissue (0.035 times the cis unsaturation level). For the individual FA, the average partial least squares (PLS) regression coefficient of the 18 most abundant FA (relative abundances ranging from 0.1 to 38.6% of the total FA content) was R-2 = 0.73; the average RMSEP = 11.9% of 4 sigma. Regression coefficients and prediction errors for the five most abundant FA were all better than the average value (in some cases as low as RMSEP = 4.7% of 4 sigma). Cross-correlation between the abundances of the minor FA and more abundant acids could be determined by principal component analysis methods, and the resulting groups of correlated compounds were also well-predicted using PLS. The accuracy of the prediction of individual FA was at least as good as other spectroscopic methods, and the extremely straightforward sampling method meant that very rapid analysis of samples at ambient temperature was easily achieved. This work shows that Raman profiling of hundreds of samples per day is easily achievable with an automated sampling system.

Multivariate prediction of clarified butter composition using Raman spectroscopy

Raman spectroscopy has been used to predict the abundance of the FA in clarified butterfat that was obtained from dairy cows fed a range of levels of rapeseed oil in their diet. Partial least squares regression of the Raman spectra against FA compositions obtained by GC showed good prediction for the five major (abundance >5%) FA with R-2=0.74-0.92 and a root mean SE of prediction (RMSEP) that was 5-7% of the mean. In general, the prediction accuracy fell with decreasing abundance in the sample, but the RMSEP was 1.25%. The Raman method has the best prediction ability for unsaturated FA (R-2=0.85-0.92), and in particular trans unsaturated FA (best-predicted FA was 18:1 tDelta9). This enhancement was attributed to the isolation of the unsaturated modes from the saturated modes and the significantly higher spectral response of unsaturated bonds compared with saturated bonds. Raman spectra of the melted butter samples could also be used to predict bulk parameters calculated from standard analyzes, such as iodine value (R-2=0.80) and solid fat content at low temperature (R-2=0.87). For solid fat contents determined at higher temperatures, the prediction ability was significantly reduced (R-2=0.42), and this decrease in performance was attributed to the smaller range of values in solid fat content at the higher temperatures. Finally, although the prediction errors for the abundances of each of the FA in a given sample are much larger with Raman than with full GC analysis, the accuracy is acceptably high for quality control applications. This, combined with the fact that Raman spectra can be obtained with no sample preparation and with 60-s data collection times, means that high-throughput, on-line Raman analysis of butter samples should be possible.