Toward automated classification of monolayer versus few-layer nanomaterials using texture analysis and neural networks.

The need for a fast and robust method to characterize nanostructure thickness is growing due to the tremendous number of experiments and their associated applications. By automatically analyzing the microscopic image texture of MoS2 and WS2, it was possible to distinguish monolayer from few-layer nanostructures with high accuracy for both materials. Three methods of texture analysis (TA) were used: grey level histogram (GLH), grey levels co-occurrence matrix (GLCOM), and run-length matrix (RLM), which correspond to first, second, and higher-order statistical methods, respectively. The best discriminating features were automatically selected using the Fisher coefficient, for each method, and used as a base for classification. Two classifiers were used: artificial neural networks (ANN), and linear discriminant analysis (LDA). RLM with ANN was found to give high classification accuracy, which was 89% and 95% for MoS2 and WS2, respectively. The result of this work suggests that RLM, as a higher-order TA method, associated with an ANN classifier has a better ability to quantify and characterize the microscopic structure of nanolayers, and, therefore, categorize thickness to the proper class.

Neural network assessment of herbal protection against chemotherapeutic-induced reproductive toxicity.

The aim of this study is to assess the protective effects of Ginkgo biloba's (GB) extract against chemotherapeutic-induced reproductive toxicity using a data mining tool, namely Neural Network Clustering (NNC) on two types of data: biochemical & fertility indicators and Texture Analysis (TA) parameters. GB extract (1 g/kg/day) was given orally to male albino rats for 26 days. This period began 21 days before a single cisplatin (CIS) intraperitoneal injection (10 mg/kg body weight). GB given orally significantly restored reproductive function. Tested extract also notably reduced the CIS-induced reproductive toxicity, as evidenced by restoring normal morphology of testes. In GB, the attenuation of CIS-induced damage was associated with less apoptotic cell death both in the testicular tissue and in the sperms. CIS-induced alterations of testicular lipid peroxidation were markedly improved by the examined plant extract. NNC has been used for classifying animal groups based on the quantified biochemical & fertility indicators and microscopic image texture parameters extracted by TA. NNC showed the separation of two clusters and the distribution of groups among them in a way that signifies the dose-dependent protective effect of GB. The present study introduces the neural network as a powerful tool to assess both biochemical and histopathological data. We also show here that herbal protection against CIS-induced reproductive toxicity utilizing classic methodologies is validated using neural network analysis.

Optimizing automated characterization of liver fibrosis histological images by investigating color spaces at different resolutions.

Texture analysis (TA) of histological images has recently received attention as an automated method of characterizing liver fibrosis. The colored staining methods used to identify different tissue components reveal various patterns that contribute in different ways to the digital texture of the image. A histological digital image can be represented with various color spaces. The approximation processes of pixel values that are carried out while converting between different color spaces can affect image texture and subsequently could influence the performance of TA. Conventional TA is carried out on grey scale images, which are a luminance approximation to the original RGB (Red, Green, and Blue) space. Currently, grey scale is considered sufficient for characterization of fibrosis but this may not be the case for sophisticated assessment of fibrosis or when resolution conditions vary. This paper investigates the accuracy of TA results on three color spaces, conventional grey scale, RGB, and Hue-Saturation-Intensity (HSI), at different resolutions. The results demonstrate that RGB is the most accurate in texture classification of liver images, producing better results, most notably at low resolution. Furthermore, the green channel, which is dominated by collagen fiber deposition, appears to provide most of the features for characterizing fibrosis images. The HSI space demonstrated a high percentage error for the majority of texture methods at all resolutions, suggesting that this space is insufficient for fibrosis characterization. The grey scale space produced good results at high resolution; however, errors increased as resolution decreased.

Texture analysis of liver fibrosis microscopic images: a study on the effect of biomarkers.

Chronic hepatic injury results in liver fibrosis with eventual progression to irreversible cirrhosis. Liver fibrogenesis involves the activation of the quiescent hepatic stellate cell into an activated myofibroblast that is characterized by α-smooth muscle actin (α-SMA) expression and the production of collagens (types I and III). In the present study, rats were randomly divided into three groups: (i) control group, where rats were only treated with a vehicle; (ii) fibrosis group, where rats were treated with carbon tetrachloride (CCl(4)) to induce liver fibrosis; and (iii) silymarin group, where rats were protected with silymarin during CCl(4) treatment. Rats were sacrificed and sections of liver tissue were counterstained with hematoxylin and eosin and Masson's trichrome. Other sections were immunostained using collagens and α-SMA primary antibodies. Fibrosis was confirmed using serum marker measurements. Microscopic images of the stained sections were acquired and digitized. The Biomarker Index of Fibrosis (BIF) was calculated from the images by quantifying the percentage of stained fibers. Statistical methods of texture analysis (TA), namely co-occurrence and run-length matrices, were applied on the digital images followed by classification using agglomerative hierarchical clustering and linear discriminant analysis with cross validation. TA applied on different biomarkers was successful in discriminating between the groups, showing 100% sensitivity and specificity for classification between the control and fibrosis groups using any biomarker. Some classification attempts showed dependence on the biomarker used, especially for classification between the silymarin and fibrosis groups, which showed optimal results using Masson's trichrome. TA results were consistent with both BIF and serum marker measurements.

Zizyphus spina-christi protects against carbon tetrachloride-induced liver fibrosis in rats.

The study of chronic hepatic fibrosis has been receiving an escalating attention in the past two decades. The aim of the study was to examine the effects of the water extract of Zizyphus spina-christi (L.) (ZSC) on carbon tetrachloride (CCl(4))-induced hepatic fibrosis. ZSC extract was daily administered [alone (ZSC-control group) or along with CCl(4) (protected groups)] at 0.125 (low dose), 0.250 (medium dose) and 0.350 (high dose) g/kg b.wt. for 8 weeks. Histo-pathological, biochemical and histology texture analyses revealed that ZSC significantly impede the progression of hepatic fibrosis. ZSC resulted in a significant amelioration of liver injury judged by the reduced activities of serum ALT and AST. Oral administration of ZSC has also restored normal levels of malondialdehyde and retained control activities of endogenous antioxidants such as SOD, CAT and GSH. Furthermore, ZSC reduced the expression of alpha-smooth muscle actin, the deposition of types I and III collagen in CCl(4)-injured rats. Texture analysis of microscopic images along with fibrosis index calculation showed improvement in the quality of type I collagen distribution and its quantity after administration of ZSC extract. These results demonstrate that administration of ZSC may be useful in the treatment and prevention of hepatic fibrosis.

The impact of image dynamic range on texture classification of brain white matter.

BACKGROUND: The Greylevel Cooccurrence Matrix method (COM) is one of the most promising methods used in Texture Analysis of Magnetic Resonance Images. This method provides statistical information about the spatial distribution of greylevels in the image which can be used for classification of different tissue regions. Optimizing the size and complexity of the COM has the potential to enhance the reliability of Texture Analysis results. In this paper we investigate the effect of matrix size and calculation approach on the ability of COM to discriminate between peritumoral white matter and other white matter regions. METHOD: MR images were obtained from patients with histologically confirmed brain glioblastoma using MRI at 3-T giving isotropic resolution of 1 mm3. Three Regions of Interest (ROI) were outlined in visually normal white matter on three image slices based on relative distance from the tumor: one peritumoral white matter region and two distant white matter regions on both hemispheres. Volumes of Interest (VOI) were composed from the three slices. Two different calculation approaches for COM were used: i) Classical approach (CCOM) on each individual ROI, and ii) Three Dimensional approach (3DCOM) calculated on VOIs. For, each calculation approach five dynamic ranges (number of greylevels N) were investigated (N = 16, 32, 64, 128, and 256). RESULTS: Classification showed that peritumoral white matter always represents a homogenous class, separate from other white matter, regardless of the value of N or the calculation approach used. The best test measures (sensitivity and specificity) for average CCOM were obtained for N = 128. These measures were also optimal for 3DCOM with N = 128, which additionally showed a balanced tradeoff between the measures. CONCLUSION: We conclude that the dynamic range used for COM calculation significantly influences the classification results for identical samples. In order to obtain more reliable classification results with COM, the dynamic range must be optimized to avoid too small or sparse matrices. Larger dynamic ranges for COM calculations do not necessarily give better texture results; they might increase the computation costs and limit the method performance.

Texture analysis of magnetic resonance images of rat muscles during atrophy and regeneration.

OBJECTIVES: The goals of the current study were (i) to introduce texture analysis on magnetic resonance imaging (MRI-TA) as a noninvasive method of muscle investigation that can discriminate three muscle conditions in rats; these are normal, atrophy and regeneration; and (ii) to show consistency between MRI-TA results and histological results of muscle type 2 fibers' cross-sectional area. METHOD: Twenty-three adult female Wistar rats were randomized into (i) control (C), (ii) immobilized (I) and (iii) recovering (R) groups. For the last two groups, the right hind limb calf muscles were immobilized against the abdomen for 14 days; then, the hind limb was remobilized only for the R group for 40 days. At the end of each experimental period, MRI was performed using 7-T magnet Bruker Avance DRX 300 (Bruker, Wissembourg); T1-weighted MRI acquisition parameters were applied to show predominantly muscle fibers. Rats were sacrificed, and the gastrocnemius muscle (GM) was excised immediately after imaging. (A) Histology: GM type 2 fibers (fast twitch) were selectively stained using the adenosine triphosphatase (ATPase) technique. The mean cross-sectional areas were compared between the three groups. (B) Image analysis: regions of interest (ROIs) were selected on GM MR images where statistical methods of texture analysis were applied followed by linear discriminant analysis (LDA) and classification. RESULTS: Histological analysis showed that the fibers' mean cross-sectional areas on GM transversal sections represented a significant statistical difference between I and C rats (ANOVA, P<.001) as well as between R and I rats (ANOVA, P<.01), but not between C and R rats. Similarly, MRI-TA on GM transversal images detected different texture for each group with the highest discrimination values (Fisher F coefficient) between the C and I groups, as well as between I and R groups. The lowest discrimination values were found between C and R groups. LDA showed three texture classes schematically separated. CONCLUSION: Quantitative results of MRI-TA were statistically consistent with histology. MRI-TA can be considered as a potentially interesting, reproducible and nondestructive method for muscle examination during atrophy and regeneration.

Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas.

The discrimination of tumor boundaries from normal tissue, as well as the evaluation of tissue heterogeneity and tumor grading often continue to pose a challenge in MRI. Although yielding promising results in various fields of medical imaging, two- dimensional (2D) texture analysis in MRI has, until now, demonstrated a lack of specificity in brain tumor classification. A new three-dimensional (3D) approach using Cooccurrence Matrix analysis is proposed to increase the sensitivity and specificity of brain tumor characterization. A preliminary comparative evaluation of 2D and 3D texture analysis was performed on T(1)-weighted MRI of seven gliomas for characterization of solid tumor, necrosis, edema and surrounding white matter. With 3D compared to 2D method, a better discrimination is obtained between necrosis and solid tumor as well as between edema and solid tumor. Using both methods, peritumoral white matter overlaps with edema, but is completely separated from far homo-lateral matter. This latter shows a complete overlapping with contra-lateral matter. The 3D texture analysis approach could provide a new tool for tumor grading and treatment follow-up, as well as for surgery or radiation therapy planning.