A Novel Machine-Assisted Technique for Extracting Multiscale Vugs and Fractures in Heterogeneous Carbonates Sequence

Predicting petrophysical properties in carbonate reservoirs is challenging due to the deposition and diagenetic history, which creates pore-scale features and heterogeneity at multiple-length scale. Non-fractured carbonate rocks with monomodal pore distribution often provide weak transportation properties compared to carbonates with multimodal pore system. The behaviour of such formations is subject to percolation effect where the connectivity of vug clusters control the poro-perm relationship which can be explained with high-resolution microresistivity images and nuclear magnetic resonance (NMR) data. A machine-assisted processing technique, defined as "thresholding," was applied to high-resolution microresistivity images, resolving vugs and fractures with similar resistivity. Other objects of interest are removed using object-oriented filters and thresholding, resulting in a "sculptured image" containing only vugs and fractures. The image is analysed to quantify formation porosity. A Laplacian of Gaussian filter is used to avoid highlighting features of no interest. Step two analyses T1 and T2 relaxations allowing portions of signal from a pore-size group to spill across the discrete boundaries. The pore-size takes on a fuzziness near the discrete relaxation time cut-offs corresponding to pore radii breakover points. High poro-perm layers of grainstone in overall thinly bedded sequences of packstone and wackestone were successfully identified and subsequently shed light upon the ambiguities observed in mobility values obtained from formation tester across the same lithocolumn. This novel technology helps in deciphering high-resolution integrated lithofacies. The histogram from the image porosity binning demonstrates a different response within vugular zones compared to fractured zones. Where the vugs sizes are variable, they exhibit a multi-pore system nature in NMR. For the fractured interval, the images and NMR exhibit weak distribution. The resistivity independent image pixel-based filtration technique helps to define interesting features on images which can be enhanced and measurable at various scales. Machine assisted technique in NMR complement the results in aiding to characterize the heterogeneous carbonate rocks.

Texture Feature Extraction of Lumbar Spine Trabecular Bone Radiograph Image using Laplacian of Gaussian Filter with KNN Classification to Diagnose Osteoporosis

The human bones are categorized based on elemental micro architecture and porosity. The porosity of the inner trabecular bone is high that is 40-95% and the nature of the bone is soft and spongy whereas the cortical bone is harder and is less porous that is 5 to 15%. Osteoporosis is a disease that normally affects women usually after their menopause. It largely causes mild bone fractures and further stages lead to the demise of an individual. The detection of Osteoporosis in Lumbar Spine has been widely recognized as a promising way to frequent fractures. Therefore, premature analysis of osteoporosis will estimate the risk of the bone fracture which prevents life threats. The paper is systematized in two different sections to classify normal (non-osteoporosis) and abnormal(osteoporosis)Lumbar spine trabecular bone. In this method, the first section is based on discriminating the lumbar spine trabecular bone micro-architecture predisposing by means of first and second order directional derivative of Laplacian of Gaussian filter with different standard deviation to acquire the minimum and maximum responses. The dimension reduction of texture features, quantization and adjacent scale coding with weighted multipliers are used to lessen the intensity variations of texture features. The second section is based on the reduction of histogram features as a training data set for classification of normal and osteoporotic images of lumbar spine (L1-L4) using K-Nearest Neighborhood (KNN) classifier. The tested dataset result gives effective classification accuracy of 97.22% with lesser texture feature dimension. The usage of weight multiplier as well as quantization technique plays a major role for the improvement of accuracy to diagnose osteoporosis for an input noisy and noiseless image.

Benchmarking Image Processing Algorithms for Unmanned Aerial System-Assisted Crack Detection in Concrete Structures

This paper summarizes the results of traditional image processing algorithms for detection of defects in concrete using images taken by Unmanned Aerial Systems (UASs). Such algorithms are useful for improving the accuracy of crack detection during autonomous inspection of bridges and other structures, and they have yet to be compared and evaluated on a dataset of concrete images taken by UAS. The authors created a generic image processing algorithm for crack detection, which included the major steps of filter design, edge detection, image enhancement, and segmentation, designed to uniformly compare different edge detectors. Edge detection was carried out by six filters in the spatial (Roberts, Prewitt, Sobel, and Laplacian of Gaussian) and frequency (Butterworth and Gaussian) domains. These algorithms were applied to fifty images each of defected and sound concrete. Performances of the six filters were compared in terms of accuracy, precision, minimum detectable crack width, computational time, and noise-to-signal ratio. In general, frequency domain techniques were slower than spatial domain methods because of the computational intensity of the Fourier and inverse Fourier transformations used to move between spatial and frequency domains. Frequency domain methods also produced noisier images than spatial domain methods. Crack detection in the spatial domain using the Laplacian of Gaussian filter proved to be the fastest, most accurate, and most precise method, and it resulted in the finest detectable crack width. The Laplacian of Gaussian filter in spatial domain is recommended for future applications of real-time crack detection using UAS.