Quantitative Texture Analysis Research Articles

Accuracy and precision of silicon based impression media for quantitative areal texture analysis.

Areal surface texture analysis is becoming widespread across a diverse range of applications, from engineering to ecology. In many studies silicon based impression media are used to replicate surfaces, and the fidelity of replication defines the quality of data collected. However, while different investigators have used different impression media, the fidelity of surface replication has not been subjected to quantitative analysis based on areal texture data. Here we present the results of an analysis of the accuracy and precision with which different silicon based impression media of varying composition and viscosity replicate rough and smooth surfaces. Both accuracy and precision vary greatly between different media. High viscosity media tested show very low accuracy and precision, and most other compounds showed either the same pattern, or low accuracy and high precision, or low precision and high accuracy. Of the media tested, mid viscosity President Jet Regular Body and low viscosity President Jet Light Body (Coltène Whaledent) are the only compounds to show high levels of accuracy and precision on both surface types. Our results show that data acquired from different impression media are not comparable, supporting calls for greater standardisation of methods in areal texture analysis.

Using multiscale texture and density features for near-term breast cancer risk analysis.

To help improve efficacy of screening mammography by eventually establishing a new optimal personalized screening paradigm, the authors investigated the potential of using the quantitative multiscale texture and density feature analysis of digital mammograms to predict near-term breast cancer risk. The authors' dataset includes digital mammograms acquired from 340 women. Among them, 141 were positive and 199 were negative/benign cases. The negative digital mammograms acquired from the "prior" screening examinations were used in the study. Based on the intensity value distributions, five subregions at different scales were extracted from each mammogram. Five groups of features, including density and texture features, were developed and calculated on every one of the subregions. Sequential forward floating selection was used to search for the effective combinations. Using the selected features, a support vector machine (SVM) was optimized using a tenfold validation method to predict the risk of each woman having image-detectable cancer in the next sequential mammography screening. The area under the receiver operating characteristic curve (AUC) was used as the performance assessment index. From a total number of 765 features computed from multiscale subregions, an optimal feature set of 12 features was selected. Applying this feature set, a SVM classifier yielded performance of AUC = 0.729 ± 0.021. The positive predictive value was 0.657 (92 of 140) and the negative predictive value was 0.755 (151 of 200). The study results demonstrated a moderately high positive association between risk prediction scores generated by the quantitative multiscale mammographic image feature analysis and the actual risk of a woman having an image-detectable breast cancer in the next subsequent examinations.

On the development of the quantitative texture analysis and its application in solving problems of the Earth sciences

A history of texture analysis (TA) evolution is shown, beginning from the first experimental and theoretical attempts to find and characterize preferred orientations of crystal lattices of grains in real polycrystalline samples. Stages of formation of TA theoretical apparatus, its basic elements, and also application of its capabilities for quantitatively describing anisotropic properties of textured samples are discussed. Attention is also paid to the limitations and difficulties associated with the analysis. The application of the quantitative TA apparatus is demonstrated by example describing elastic properties of textured materials up to multiphase samples containing pores and cracks. A wide scope of TA includes the analysis based on neutron scattering which has been effectively developed at the Frank Laboratory of Neutron Physics. A practical opportunity to determine the bulk crystallographic textures of single-phase and multiphase materials is offered by the use of modern neutron diffractometers, including the SKAT diffractometer at the IBR-2 pulsed reactor. This is especially important for studying samples of natural rocks. The examples given show how the neutron scattering data for the quantitative TA are used in combination with other physical and petrological methods for solving fundamental problems of geology and geophysics based on the analysis of a structure and properties of the Earth’s lithosphere matter. The review includes a detailed list of references of original works concerning the TA elaboration, overview publications and monographs, and also information on the most popular TA-related software.

17th International Conference on Textures of Materials (ICOTOM 17)

17th International Conference on Textures of Materials (ICOTOM 17)

Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?

To determine the accuracy of texture analysis to differentiate fat-poor angiomyolipoma (fp-AML) from renal cell carcinoma (RCC) on unenhanced computed tomography (CT) images. In this institutional review board-approved retrospective case-control study, patients with AML and RCC were identified from the pathology database: there were 16 patients with fp-AML (no visible fat at unenhanced CT) and 84 patients with RCC. Axial unenhanced CT images were contoured manually by two independent analysts. Texture analysis was performed for each lesion, and reproducibility was assessed. Texture features related to the gray-level histogram, gray-level co-occurrence, and run-length matrix statistics were evaluated. The most discriminative features were used to generate support vector machine (SVM) classifiers. Diagnostic accuracy of textural features was assessed and 10-fold cross validation was performed. Unenhanced CT images for each patient were independently reviewed by two blinded radiologists who subjectively graded lesion heterogeneity on a five-point scale. Differences in area under the receiver operating characteristic curve (AUC) between subjective heterogeneity ratings and textural features were evaluated by using the DeLong method. There was lower lesion homogeneity and higher lesion entropy in RCCs (P ≤ .01). A model incorporating several texture features resulted in an AUC of 0.89 ± 0.04. The average SVM accuracy of textural features ranged from 83% to 91% (after 10-fold cross validation). An optimal subjective heterogeneity rating of 2 or higher was identified as a predictor of RCC for both readers, with no significant difference in AUC between readers (P = .06). Each of the three textural-based classifiers was more accurate than either radiologists' subjective heterogeneity ratings for the models incorporating a subset of the top three textural features (difference in AUC between textural features and subjective visual heterogeneity, 0.25; 95% confidence interval: 0.02, 0.47; P = .03). CT texture analysis can be used to accurately differentiate fp-AML from RCC on unenhanced CT images.

Texture gradient in a copper tube at maximum and minimum wall thickness

Tube drawing is an inhomogeneous process with anisotropic flow. Quantitative texture analysis was used to get inside in eccentricity and ovality of a Cu-tube drawn without a plug. To obtain the texture gradient over the wall thickness high resolution synchrotron radiation was used. One obtains on one hand different textures at the surfaces than in the central region and the other hand a variation of the texture gradient at the maximum wall thickness of the tube compared to the minimum wall thickness.

Quantitative ultrasound texture analysis of fetal lungs to predict neonatal respiratory morbidity.

To develop and evaluate the performance of a novel method for predicting neonatal respiratory morbidity based on quantitative analysis of the fetal lung by ultrasound. More than 13,000 non-clinical images and 900 fetal lung images were used to develop a computerized method based on texture analysis and machine learning algorithms, trained to predict neonatal respiratory morbidity risk on fetal lung ultrasound images. The method, termed 'quantitative ultrasound fetal lung maturity analysis' (quantusFLM™), was then validated blindly in 144 neonates, delivered at 28 + 0 to 39 + 0 weeks' gestation. Lung ultrasound images in DICOM format were obtained within 48 h of delivery and the ability of the software to predict neonatal respiratory morbidity, defined as either respiratory distress syndrome or transient tachypnea of the newborn, was determined. Mean (SD) gestational age at delivery was 36 + 1 (3 + 3) weeks. Among the 144 neonates, there were 29 (20.1%) cases of neonatal respiratory morbidity. Quantitative texture analysis predicted neonatal respiratory morbidity with a sensitivity, specificity, positive predictive value and negative predictive value of 86.2%, 87.0%, 62.5% and 96.2%, respectively. Quantitative ultrasound fetal lung maturity analysis predicted neonatal respiratory morbidity with an accuracy comparable to that of current tests using amniotic fluid.

CO2 bubble generation and migration during magma–carbonate interaction

We conducted quantitative textural analysis of vesicles in high temperature and pressure carbonate assimilation experiments (1200 °C, 0.5 GPa) to investigate CO2 generation and subsequent bubble migration from carbonate into magma. We employed Mt. Merapi (Indonesia) and Mt. Vesuvius (Italy) compositions as magmatic starting materials and present three experimental series using (1) a dry basaltic-andesite, (2) a hydrous basaltic-andesite (2 wt% H2O), and (3) a hydrous shoshonite (2 wt% H2O). The duration of the experiments was varied from 0 to 300 s, and carbonate assimilation produced a CO2-rich fluid and CaO-enriched melts in all cases. The rate of carbonate assimilation, however, changed as a function of melt viscosity, which affected the 2D vesicle number, vesicle volume, and vesicle size distribution within each experiment. Relatively low-viscosity melts (i.e. Vesuvius experiments) facilitated efficient removal of bubbles from the reaction site. This allowed carbonate assimilation to continue unhindered and large volumes of CO2 to be liberated, a scenario thought to fuel sustained CO2-driven eruptions at the surface. Conversely, at higher viscosity (i.e. Merapi experiments), bubble migration became progressively inhibited and bubble concentration at the reaction site caused localised volatile over-pressure that can eventually trigger short-lived explosive outbursts. Melt viscosity therefore exerts a fundamental control on carbonate assimilation rates and, by consequence, the style of CO2-fuelled eruptions.

Internal optimization of the texture component approximation method

The component approximation method for the reconstruction of orientation distribution function (ODF) is based on the assumption that the texture could be presented as a weighted linear combination of distributions depending on the parameters, which are related to the position of bell shaped function in orientation space and to the dispersion. The method uses a minimization procedure to obtain the values of ODF parameters. Traditionally, the mean- square deviation of the measured and recalculated pole figures is minimized. However, the quantitative measure of the fit is RP value which differs from the mean-square deviation. In the present work it is suggested to minimize the RP value to obtain ODF parameters. We are using Trust Region method for solving a non-linear optimization problem. The convergences of the proposed method for different minimized functional are compared. We also illustrate a usage of the different objective function on modeling data for the cubic crystalline symmetry. This study is fulfilled using new RIGAKU software for quantitative texture analysis.

The impulse response method for pear quality evaluation using a laser Doppler vibrometer

The impulse response method using a laser Doppler vibrometer (LDV) was performed to nondestructively measure pear quality. To get a wide range of texture and different freshness in pears, the experiment was conducted every other day during 7days storage. Each pear was excited by a half-sine impulse signal, and an LDV was used to measure the response signal from the top of the pear. A fast Fourier transform algorithm was used to transform time domain signals to frequency domain signals. A total of 15 and 8 features were extracted from the time and frequency domain signals, respectively. Pear texture was measured by the puncture test. Maximum force (MF), flesh firmness (FF) and stiffness (Stif) were extracted from the force–deformation curve as texture indices. Different modeling methods, including the stepwise multiple linear regression (SMLR), back propagation neural network (BPNN), and principal component analysis-back propagation neural network (PCA-BPNN) methods, were used for quantitative analysis of pear texture. Best prediction results were obtained by the PCA-BPNN method, especially for predicting FF with correlation coefficient (rp) of 0.840 and root mean square error of prediction (RMSEP) of 0.959N. The Fisher’s discriminant analysis (FDA), BPNN, and PCA-BPNN methods were applied to qualitative analysis of pear freshness. Pears were categorized into 4 groups with different freshness according to the 4 test days. The best results were also obtained by the PCA-BPNN method, resulting in accuracy of 89.0% and 83.3% for calibration and validation, respectively. Experimental results showed that the impulse response method using an LDV is capable for evaluating pear texture and freshness. The proposed approach provides a way for rapid detection of pear quality to meet the requirement of on-line detection.

Myocardial integrated ultrasonic backscatter for early detection of cardiac involvement in patients with Wilson disease.

Videodensitometry is a feasible noninvasive ultrasound tissue characterization method allowing early detection of myocardial changes. This study aimed to investigate ultrasonic backscatter properties of the myocardium in Wilson disease patients. We compared cardiologically asymptomatic Wilson disease patients (W group) (n=18) with age-matched (26.7±9.6 years) healthy controls (C group) (n=15). Diagnosis of Wilson disease was made on the basis of clinical manifestations, family history, and laboratory findings and confirmed by liver biopsy. Transthoracic echocardiographic quantitative texture analysis was performed on data from the septum and left ventricular posterior wall, and mean gray level (MGL) histograms at end-diastole (d) and end-systole (s) were obtained after background correction (c). Cyclic variation index (CVI) was calculated using the formula [(cMGLd - cMGLs) / cMGLd] ×100. There were no significant differences in sex, age, body mass index, heart rate or blood pressure, and conventional echocardiographic parameters between the 2 groups. The cMGLs value of the posterior wall was higher in the W group than in the C group (30.9±2.6 vs. 22.2±2.7, p=0.033). The W group had a significantly lower CVI of the septum than did the C group (-22±4.4% vs. 43.4 ±12.9%, p<0.001), and there was no significant difference in the CVI of the posterior wall (-67.0±15.9% vs. 41.7±18.6%, p=0.32). Abnormalities in two-dimensional echocardiographic grey-level distributions were present in Wilson disease patients. These videodensitometric myocardial alterations were significantly lower in Wilson disease patients than in the controls, and this probably represents an early stage of cardiac involvement.

MR image texture in Parkinson’s disease: a longitudinal study

Few of the structural changes caused by Parkinson's disease (PD) are visible in magnetic resonance imaging (MRI) with visual inspection but there is a need for a method capable of observing the changes beyond the human eye. Texture analysis offers a technique that enables the quantification of the image gray-level patterns. To investigate the value of quantitative image texture analysis method in diagnosis and follow-up of PD patients. Twenty-six PD patients underwent MRI at baseline and after 2 years of follow-up. Four co-occurrence matrix-based texture parameters, describing the image homogeneity and complexity, were calculated within clinically interesting areas of the brain. In addition, correlations with clinical characteristics (Unified Parkinson's Disease Ranking Scales I-III and Mini-Mental State Examination score) along with a comparison to healthy controls were evaluated. Patients at baseline and healthy volunteers differed in their brain MR image textures mostly in the areas of substantia nigra pars compacta, dentate nucleus, and basilar pons. During the 2-year follow-up of the patients, textural differences appeared mainly in thalamus and corona radiata. Texture parameters in all the above mentioned areas were also found to be significantly related to clinical scores describing the severity of PD. Texture analysis offers a quantitative method for detecting structural changes in brain MR images. However, the protocol and repeatability of the method must be enhanced before possible clinical use.

An Evolutionary Algorithm for the Texture Analysis of Cubic System Materials Derived by the Maximum Entropy Principle

Based on the principle of maximum entropy method (MEM) for quantitative texture analysis, the differential evolution (DE) algorithm was effectively introduced. Using a DE-optimized algorithm with a faster but more stable convergence rate of iteration reliable complete orientation distributions (C-ODF) have been obtained for deep-drawn IF steel sheets and the recrystallized aluminum foils after cold-rolling, which are designated as showing a macroscopic cubic-orthogonal symmetry. With special reference to the data processing, no more other assumptions are required for DE-optimized MEM except that the system entropy approach the maximum.

Quantitative textural analysis of ilmenite in Apollo 17 high-titanium mare basalts

Quantitative textural analysis is a powerful tool in the investigation of basalt crystallization. We present the first comprehensive crystal size distribution analysis of Apollo 17 high-titanium lunar basalts, with a focus on ilmenite. Crystal size distributions of ilmenite, pyroxene, plagioclase, olivine and armalcolite were determined for 18 high-Ti mare basalt samples from the Apollo 17 mission. A subset of the ilmenite size distribution (size bins of <0.6mm in length) reflects growth at post-eruption or post-emplacement cooling rates. Growth of these small ilmenite crystals is controlled by cooling rate and not bulk composition or ilmenite abundance. CSD characteristics tied to cooling rate determined by experiments yield estimates of cooling rate in natural samples. Matrix ilmenite grew at rates up to 250°C/h, while most samples contained phenocrysts that originated in environments cooling at <3°C/h. Textural characteristics of ilmenite phenocrysts are used to develop a relative stratigraphy for the samples within a lava flow based upon comparisons with terrestrial analogues.

186 The use of Quantitative Textural Analysis imaging biomarkers to predict response to temsirolimus treatment in advanced HCC subjects

186 The use of Quantitative Textural Analysis imaging biomarkers to predict response to temsirolimus treatment in advanced HCC subjects

Abstract 1885: Quantitative Textural Analysis (QTA) in CT imaging: Identifying markers for genetic instability and overall survival in cohort of previously treated metastatic pancreatic cancer (mPC)

Abstract PURPOSE. To explore the use of helical CT-derived Quantitative Textural Analysis (QTA) parameters in identifying elevated rates of intrachromosomal copy number aberrations in mPC and secondarily in revealing post-treatment QTA prognostic markers. MATERIALS AND METHODS. Metastatic tumor sites were biopsied and molecularly profiled in a cohort patients with mPC (n=35). 85% of the cohort had 1-3 previous treatment regimens, and the remaining 15% had 4-6. Intrachromosal copy number were assessed by CGH in tumor specimens, and patients were treated with commercially available cytotoxic regimens based on these individual molecular profiling results1. Patients with available pre-biopsy transverse abdominal portal-venous phase CT scans were obtained for retrospective analysis (n=17). Analysis of pre-biopsy pancreas imaging was performed by drawing regions of interest around the primary pancreas adenocarcinoma (n=15) and the normal pancreas tissue when available (n=12). Two patients did not have any quantifiable imaging due to surgery or local structural obfuscation and were not included in our cohort. There were 9 males and 6 females all with stage IV cancer from 34 - 71 years old (median 59). QTA parameters were derived using the TexRAD platform at texture filtering densities of medium (3 pixels) and coarse (5 pixels). There values were compared to intrachromosomal copy number aberrations per tumor and overall survival post-treatment using a Spearman's rank correlation coefficient. RESULTS. Intrachromosomal copy number aberrations (median: 14; range: 5 - 52) were negatively correlated with the kurtosis value of the primary tumor mass (median: -0.146; range: -0.810 - 0.633) using medium texture filtering (p = 0.034, n = 15). Secondary analysis of the normal pancreas with coarse texture filtering yielded a correlation between reduced overall survival time post-treatment and increasing entropy (p = 0.0014, n = 12). Using entropy as a cutoff value (median: 4.165; range 2.686 - 4.891), median overall survival was greater in the entropy &amp;lt; 4.165 group versus the entropy &amp;gt; 4.165 group (6.1 v 2.0 months, respectively; hazards ratio [HR] = 11.5; 95% CI 1.36 - 97.4; p = 0.025, n = 12). Median survival for the entire cohort was 120 days and ranged from 1 day to 372 days post-treatment. CONCLUSION. This exploratory study with admittedly limited sample size raises interesting questions about the use of QTA parameters as diagnostic tools and/or biopsy adjuncts in assessing pancreatic adenocarcinoma susceptibility to commercially available cytotoxics. Secondarily, entropy, a potential marker of heterogeneity and inflammation in the normal pancreas, represents an intriguing possibility for gauging prognosis in mPC. (Clinical trial supported by a grant from the SU2C/AACR-pancreatic dream team) REFERENCES. 1. Ramanathan RK et al AACR Annual Meeting. LB-221. 2012. Citation Format: David H. Campbell, Michael Barrett, Ramesh K. Ramanathan, Daniel D. Von Hoff, Ronald Korn. Quantitative Textural Analysis (QTA) in CT imaging: Identifying markers for genetic instability and overall survival in cohort of previously treated metastatic pancreatic cancer (mPC). [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1885. doi:10.1158/1538-7445.AM2014-1885

Quantitative CT texture and shape analysis: can it differentiate benign and malignant mediastinal lymph nodes in patients with primary lung cancer?

To assess the accuracy of CT texture and shape analysis in the differentiation of benign and malignant mediastinal nodes in lung cancer. Forty-three patients with biopsy-proven primary lung malignancy with pathological mediastinal nodal staging and unenhanced CT of the thorax were studied retrospectively. Grey-level co-occurrence and run-length matrix textural features, as well as morphological features, were extracted from 72 nodes. Differences between benign and malignant features were assessed using Mann-Whitney U tests. Receiver operating characteristic (ROC) curves for each were constructed and the area under the curve (AUC) calculated with histopathology diagnosis as outcome. Combinations of features were also entered as predictors in logistic regression models and optimal threshold criteria were used to estimate sensitivity and specificity. Using optimum-threshold criteria, the combined textural and shape features identified malignant mediastinal nodes with 81% sensitivity and 80% specificity (AUC = 0.87, P < 0.0001). Using this combination, 84% malignant and 71% benign nodes were correctly classified. Quantitative CT texture and shape analysis has the potential to accurately differentiate malignant and benign mediastinal nodes in lung cancer. • Mediastinal nodal staging is crucial in the management of lung cancer • Mediastinal nodal metastasis affects prognosis and suitability for surgical treatment • Computed tomography (CT) is limited for mediastinal nodal staging • Texture analysis measures tissue heterogeneity not perceptible to human vision • CT texture analysis may accurately differentiate malignant and benign mediastinal nodes.

Characterization of squamous cell carcinomas of the head and neck using methods of spatial statistics.

In the present study, 53 cases of squamous cell carcinomas of the head and neck were characterized by a quantitative histological texture analysis based on principles of spatial statistics. A planar tessellation of the epithelial tumour component was generated by a skeletonization algorithm. The size distribution of the virtual cells of this planar tessellation, and the size distribution of the profiles of the tumour cell nuclei were estimated in terms of area and boundary length. The intensity, the reduced second moment function (K-function) and the pair correlation function of the point process of the centroids of the profiles of the tumour cell nuclei were also estimated. For both purposes, it is necessary to correct for edge effects, which we consider in this paper in some detail. Specifically, the point patterns of the tumour cell nuclei were considered as realizations of a point process, where the points exist only in the epithelial tumour component (the permitted phase) and not in the stroma (the forbidden phase). The methods allow to characterize each individual tumour by a series of summary statistics. The total set of cases was then partitioned into two groups: 19 cases without lymph node metastases (pN0), and 34 nodal positive cases (pN1 or pN2). Statistical analysis showed no significant differences between the intensities, the mean K-functions and the mean pair correlation functions of the tumour cell nucleus profiles of the two groups. However, there were some significant differences between the sizes of the virtual cells and of the nucleus profiles of the nodal negative cases as compared to the nodal positive cases. In a logistic regression analysis, one of the quantitative nuclear size variables (mean nuclear area) was found to be a significant predictor of lymph node metastasis, in addition to tumour stage. The study shows the potential of methods of spatial statistics for objective quantitative grading of squamous cell carcinomas of the head and neck, and provides an example for modelling histological point patterns as realizations of planar point processes occupying a reference phase which is only a partial component of the total tissue.

The role of crystallinity and viscosity in the formation of submarine lava flow morphology

Submarine lava flow morphology is commonly used to estimate relative flow velocity, but the effects of crystallinity and viscosity are rarely considered. We use digital petrography and quantitative textural analysis techniques to determine the crystallinity of submarine basaltic lava flows, using a set of samples from previously mapped lava flow fields at the hotspot-affected Galapagos Spreading Center. Crystallinity measurements were incorporated into predictive models of suspension rheology to characterize lava flow consistency and rheology. Petrologic data were integrated to estimate bulk lava viscosity. We compared the crystallinity and viscosity of each sample with its flow morphology to determine their respective roles in submarine lava emplacement dynamics. We find no correlation between crystallinity, bulk viscosity, and lava morphology, implying that flow advance rate is the primary control on submarine lava morphology. However, we show systematic variations in crystal size and shape distribution among pillows, lobates, and sheets, suggesting that these parameters are important indicators of eruption processes. Finally, we compared the characteristics of lavas from two different sampling sites with contrasting long-term magma supply rates. Differences between lavas from each study site illustrate the significant effect of magma supply on the physical properties of the oceanic upper crust.

Anisotropic resistivity tensor of melt-cast Bi2212 superconductors from QTA

Bi2Sr2CaCu2O8+δ HTSC superconductor is characterized by a very strong normal-state resistivity anisotropy, with ρc/ρab typically above 10E4. The aim of this study is to use Quantitative Texture Analysis from x-ray diffraction measurements to estimate the orientation effect on the anisotropic macroscopic resistivity in melt-cast bulk Bi2Sr2CaCu2O8+δ superconductors. Our approach uses the geometric mean [1] of the single crystal resistivity tensor weighted by the Orientation Distribution Function (ODF) to quantitatively estimate the macroscopic resistivity tensor of the samples. The ODF is obtained from x-ray Combined Analysis [2], using the E-WIMV algorithm of the MAUD software. The GMA applies to the rank-two resistivity tensor of the orthorhombic space group considered tetragonal due to the small difference of a- and b-axes of the phase, with only two independent tensor components. We relate a relatively good agreement between measured and calculated macroscopic anisotropic resistivity ratios. Even with ρc/ρab between 10E4 and 10E5 for Bi2212 at room temperature in single crystals [3], we experiment macroscopic ratio in our bulk samples of around only 2. This small ratio is explained by the weak planar- or fiber-like (Figure) texture achieved in the melt-cast samples, characterized by maxima of orientation distributions not larger than 10 mrd. Calculated resistivities, based on homogeneous crystallites, perfect grain boundaries and no secondary phases, are 10 times larger than the observed ones. This suggests that the observed minor phases positively affect conductive pathways between grains. Calculated and measured anisotropic resistive ratios are coherent with one another, and Combined Analysis gives good predictions of these former.