Comparison of digital image analysis methods for morphometric characterization of soil aggregates in thin sections

The purpose of this study was to investigate the applicability of semiautomatic segmentation methods for obtaining and evaluating morphometric parameters of soil aggregates in artificially prepared loose samples in soil thin sections. The object of the research is typical arable Chernozem. The aggregates were separated by wet sieving method from loose sample of upper 10 cm of the plowing horizon after erosion by a model shallow water flow on a large erosion tray. The aggregates, loosely scattered on the glass and fixed with polyester resin, were used to produce the thin sections. Images of the thin sections were taken under a polarizing microscope and then were processed using two methods compared: Adobe Photoshop + CTan and Thixomet Pro. Data on morphometric parameters of aggregates were obtained: the shape factor, the degree of roundness and the coefficient of aggregate surface roughness. The convergence of the results obtained using Photoshop + CTan by three researchers was evaluated by comparing samples using the Student's test and the Mann-Whitney test. The convergence of the averaged results obtained using Photoshop + CTan and the results obtained using Thixomet Pro was evaluated using the Mann - Whitney test. No significant differences were found between the parameters of the same aggregates obtained using a combination of Adobe Photoshop and CTan programs by different researchers. No significant differences were found between the parameters of the same aggregates obtained by the compared methods. So, one can conclude that the reliability of determining the morphometric parameters of soil aggregates using Thixomet Pro is comparable to the reliability of results when working with images of sectionsin CTan after binarization in Adobe Photoshop. The method of obtaining data on morphometric parameters of soil aggregates using Thixomet Pro completely eliminates the possibility of subjective error, shows a high degree of automation, reproducibility and reliability of the results obtained, and is faster

Image texture analysis of transvaginal ultrasound in monitoring ovarian cancer

Ovarian cancer has the highest mortality rate of all gynaecologic cancers and is the fifth most common cancer in UK women. It has been dubbed “the silent killer” because of its non-specific symptoms. Amongst various imaging modalities, ultrasound is considered the main modality for ovarian cancer triage. Like other imaging modalities, the main issue is that the interpretation of the images is subjective and observer dependent. In order to overcome this problem, texture analysis was considered for this study. Advances in medical imaging, computer technology and image processing have collectively ramped up the interest of many researchers in texture analysis. While there have been a number of successful uses of texture analysis technique reported, to my knowledge, until recently it has yet to be applied to characterise an ovarian lesion from a B-mode image. The concept of applying texture analysis in the medical field would not replace the conventional method of interpreting images but is simply intended to aid clinicians in making their diagnoses. Five categories of textural features were considered in this study: grey-level co-occurrence matrix (GLCM), Run Length Matrix (RLM), gradient, auto-regressive (AR) and wavelet. Prior to the image classification, the robustness or how well a specific textural feature can tolerate variation arises from the image acquisition and texture extraction process was first evaluated. This includes random variation caused by the ultrasound system and the operator during image acquisition. Other factors include the influence of region of interest (ROI) size, ROI depth, scanner gain setting, and „calliper line‟. Evaluation of scanning reliability was carried out using a tissue-equivalent phantom as well as evaluations of a clinical environment. iii Additionally, the reliability of the ROI delineation procedure for clinical images was also evaluated. An image enhancement technique and semi-automatic segmentation tool were employed in order to improve the ROI delineation procedure. The results of the study indicated that two out of five textural features, GLCM and wavelet, were robust. Hence, these two features were then used for image classification purposes. To extract textural features from the clinical images, two ROI delineation approaches were introduced: (i) the textural features were extracted from the whole area of the tissue of interest, and (ii) the anechoic area within the normal and malignant tissues was excluded from features extraction. The results revealed that the second approach outperformed the first approach: there is a significant difference in the GLCM and wavelet features between the three groups: normal tissue, cysts, and malignant. Receiver operating characteristic (ROC) curve analysis was carried out to determine the discriminatory ability of textural features, which was found to be satisfactory. The principal conclusion was that GLCM and wavelet features can potentially be used as computer aided diagnosis (CAD) tools to help clinicians in the diagnosis of ovarian cancer

Nucleus segmentation : towards automated solutions

Single nucleus segmentation is a frequent challenge of microscopy image processing, since it is the first step of many quantitative data analysis pipelines. The quality of tracking single cells, extracting features or classifying cellular phenotypes strongly depends on segmentation accuracy. Worldwide competitions have been held, aiming to improve segmentation, and recent years have definitely brought significant improvements: large annotated datasets are now freely available, several 2D segmentation strategies have been extended to 3D, and deep learning approaches have increased accuracy. However, even today, no generally accepted solution and benchmarking platform exist. We review the most recent single-cell segmentation tools, and provide an interactive method browser to select the most appropriate solution.Peer reviewe