Textural Analysis of Benthic Sediments

Textural analysis involved the handling of approximately 3,700 benthic samples by the Sedimentation Laboratory of the Bureau of Economic Geology. In addition to determination of gravel-sand-mud ratios, particle-size distribution within the sand fraction (-1.0 phi to 4 phi) was determined using a Rapid Sediment Analyzer and included sand-sized shell material. Most of the shell material was broken and apparently transported. Size distribution within the mud fraction (4.0 phi to 10.62 phi) was determined with a Coulter TA II electronic suspended particle counter. Because the gravel fraction (larger than -1.0 phi) consisted largely of unbroken shell material (much of which was probably not transported), no size distribution within this fraction was determined. Most aspects of the basic sample preparation, and particle-size analysis used by this laboratory, have been treated by Krumbein and Pettijohn (1938), Ingram (1971), and Folk (1974), among others. This paper outlines a procedure for sample preparation and determination of particle-size distribution, with special attention given to problems associated with the handling of marine benthic sediments containing an abundance of soluble salts, organic matter, and shell material. The objectives of this procedure are as follows: 1) the removal of soluble salts which affect clay flocculation; 2) removal of disseminated organic matter which bonds otherwise discrete mud particles and generates misleading mud analysis data; 3) preservation of shell material in its original form so that sand analysis reflects original sediment texture more accurately.Bureau of Economic Geolog

Within-guild dietary discrimination from 3-D textural analysis of tooth microwear in insectivorous mammals

Resource exploitation and competition for food are important selective pressures in animal evolution. A number of recent investigations have focused on linkages between diversification, trophic morphology and diet in bats, partly because their roosting habits mean that for many bat species diet can be quantified relatively easily through faecal analysis. Dietary analysis in mammals is otherwise invasive, complicated, time consuming and expensive. Here we present evidence from insectivorous bats that analysis of three-dimensional (3-D) textures of tooth microwear using International Organization for Standardization (ISO) roughness parameters derived from sub-micron surface data provides an additional, powerful tool for investigation of trophic resource exploitation in mammals. Our approach, like scale-sensitive fractal analysis, offers considerable advantages over twodimensional (2-D) methods of microwear analysis, including improvements in robustness, repeatability and comparability of studies. Our results constitute the first analysis of microwear textures in carnivorous mammals based on ISO roughness parameters. They demonstrate that the method is capable of dietary discrimination, even between cryptic species with subtly different diets within trophic guilds, and even when sample sizes are small. We find significant differences in microwear textures between insectivore species whose diet contains different proportions of ‘hard’ prey (such as beetles) and ‘soft’ prey (such as moths), and multivariate analyses are able to distinguish between species with different diets based solely on their tooth microwear textures. Our results show that, compared with previous 2-D analyses of microwear in bats, ISO roughness parameters provide a much more sophisticated characterization of the nature of microwear surfaces and can yield more robust and subtle dietary discrimination. ISO-based textural analysis of tooth microwear thus has a useful role to play, complementing existing approaches, in trophic analysis of mammals, both extant and extinct

Classification of Rock Images using Textural Analysis

The classification of natural images is an useful task in current computer vision, pattern recognition applications etc. Rock images are a typical example of natural images, therefore their analysis is of major importance in the rock industry and in bedrock investigations. Rock image classification is based on specific textural descriptors which are extracted from the images. Using these descriptors, images are divided into various types. In the case of natural images, the feature distributions are often non-homogeneous and the image classes are also overlapping in the feature space. This can be problematic, if all the descriptors are combined into a single feature vector in the classification of an image. A method is presented for combining different visual descriptors in rock image classification. In this paper, k-nearest neighbor classification will be carried out for pair of descriptor separately. After that, the final decision is made by combining the results of each classification. The total numbers of the neighbors representing each class are used as votes in the final classification. The classification method will be tested using three types of rock. DOI: 10.17762/ijritcc2321-8169.15039

Classification of Brain Hemorrhage using Textural Analysis

In order to assist in fast diagnosis of brain hemorrhage, computer-aided diagnosis have been developed in recent years. Image processing and analysis is considered to be an important area as technological tool for medical evaluation and diagnosis. With this, we decided to venture in the image processing and analysis of brain hemorrhage. Image processing comprises of different techniques and phases, wherein each techniques intend to contribute to the accuracy of medical diagnosis. With only few studies on image processing for the diagnosis of brain hemorrage, there is a need to improve the algorithm of image processing for accuracy and robustness

On Importance of Acoustic Backscatter Corrections for Texture-based Seafloor Characterization

Seafloor segmentation and characterization based on local textural properties of acoustic backscatter has been a subject of research since 1980s due to the highly textured appearance of sonar images. The approach consists of subdivision of sonar image in a set of patches of certain size and calculation of a vector of features reflecting the patch texture. Advance of multibeam echosounders (MBES) allowed application of texture-based techniques to real geographical space, and predicted boundaries between acoustic facies became experimentally verifiable. However, acoustic return from uncalibrated MBES produces artifacts in backscatter mosaics, which in turn affects accuracy of delineation. Development of Geocoder allowed creation of more visually consistent images, and reduced the number of factors influencing mosaic creation. It is intuitively clear that more accurate backscatter mosaics lead to more reliable classification results. However, this statement has never been thoroughly verified. It has not been investigated which corrections are important for texture-based characterization and which are not essential. In this paper the authors are investigating the Stanton Banks common dataset. Raw data files from the dataset have been processed by the Geocoder at different levels of corrections. Each processing resulted in a backscatter mosaic demonstrating artifacts of different levels of severity. Mosaics then underwent textural analysis and unsupervised classification using Matlab package SonarClass. Results of seafloor characterization corresponding to varying levels of corrections were finally compared to the one generated by the best possible mosaic (the one embodying all the available corrections), providing an indicator of classification accuracy and giving guidance about which mosaic corrections are crucial for acoustic classification and which could be safely ignored

Using texture analysis in the development of a potential radiomic signature for early identification of hepatic metastasis in colorectal cancer

Background: Radiomics allows information not readily available to the naked eye to be extracted from high resolution imaging modalities such as CT. Identifying that a cancer has already metastasised at the time of presentation through a radiomic signature will affect the treatment pathway. The ability to recognise the existence of metastases earlier will have a significant impact on the survival outcomes. / Aim: To create a novel radiomic signature using textural analysis in the evaluation of synchronous liver metastases in colorectal cancer. / Methods: CT images at baseline and subsequent surveillance over a 5-year period of patients with colorectal cancer were processed using textural analysis software. Comparison was made between those patients who developed liver metastases and those that remained disease free to detect differences in the ‘texture’ of the liver. / Results: A total of 24 patients were divided into two matched groups for comparison. Significant differences between the two groups scores when using the textural analysis programme were found on coarse filtration (p = 0.044). Patients that went on to develop metastases an average of 18 months after presentation had higher levels of hepatic heterogeneity on CT. / Conclusion: This initial study demonstrates the potential of using a textural analysis programme to build a radiomic signature to predict the development of hepatic metastases in rectal cancer patients otherwise thought to have clear staging CT scans at time of presentation

Benthic habitat mapping in the Olympic Coast National Marine Sanctuary: Classification of side scan sonar data from survey HMPR-108-2002-01: Version I

In September 2002, side scan sonar was used to image a portion of the sea floor in the northern OCNMS and was mosaiced at 1-meter pixel resolution using 100 kHz data collected at 300-meter range scale. Video from a remotely-operated vehicle (ROV), bathymetry data, sedimentary samples, and sonar mapping have been integrated to describe geological and biological aspects of habitat and polygon features have been created and attributed with a hierarchical deep-water marine benthic classification scheme (Greene et al. 1999). The data can be used with geographic information system (GIS) software for display, query, and analysis. Textural analysis of the sonar images provided a relatively automated method for delineating substrate into three broad classes representing soft, mixed sediment, and hard bottom. Microhabitat and presence of certain biologic attributes were also populated into the polygon features, but strictly limited to areas where video groundtruthing occurred. Further groundtruthing work in specific areas would improve confidence in the classified habitat map. (PDF contains 22 pages.

A computer-aided diagnosis system for glioma grading using three dimensional texture analysis and machine learning in MRI brain tumour

Glioma grading is vital for therapeutic planning where the higher level of glioma is associated with high mortality. It is a challenging task as different glioma grades have mixed morphological characteristics of brain tumour. A computer-aided diagnosis (CAD) system based on three-dimensional textural grey level co-occurrence matrix (GLCM) and machine learning is proposed for glioma grading. The purpose of this paper is to assess the usefulness of the 3D textural analysis in establishing a malignancy prediction model for glioma grades. Furthermore, this paper aims to find the best classification model based on textural analysis for glioma grading. The classification system was evaluated using leave-one-out cross-validation technique. The experimental design includes feature extraction, feature selection, and finally the classification that includes single and ensemble classification models in a comparative study. Experimental results illustrate that single and ensemble classification models, can achieve efficient prediction performance based on 3D textural analysis and the classification accuracy result has significantly improved after using feature selection methods. In this paper, we compare the proficiency of applying different angles of 3D textural analysis and different classification models to determine the malignant level of glioma. The obtained sensitivity, accuracy and specificity are 100%, 96.6%, 90% respectively. The prediction system presents an effective approach to assess the malignancy level of glioma with a non-invasive, reproducible and accurate CAD system for glioma grading

Discriminating small wooded elements in rural landscape from aerial photography: a hybrid pixel/object-based analysis approach

While small, fragmented wooded elements do not represent a large surface area in agricultural landscape, their role in the sustainability of ecological processes is recognized widely. Unfortunately, landscape ecology studies suffer from the lack of methods for automatic detection of these elements. We propose a hybrid approach using both aerial photographs and ancillary data of coarser resolution to automatically discriminate small wooded elements. First, a spectral and textural analysis is performed to identify all the planted-tree areas in the digital photograph. Secondly, an object-orientated spatial analysis using the two data sources and including a multi-resolution segmentation is applied to distinguish between large and small woods, copses, hedgerows and scattered trees. The results show the usefulness of the hybrid approach and the prospects for future ecological applications