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The nail matrix is the area where your fingernails and toenails start to grow. The matrix creates new skin cells, which pushes out the old, dead skin cells to make your nails. As a result, injuries to the nail bed or disorders that affect the matrix can affect your nail growth.
Subungual melanoma (or nail matrix melanoma) is a condition where cancerous cells grow in the nail matrix. The cancerous cells can cause changes in pigments in the nail known as melanin. As a result, a distinct striped discoloration can grow from the nail matrix.
Pterygium unguis is a condition that causes scarring that extends to the nail matrix. It causes the nail fold where the fingernail usually goes over the fingertip to fuse to the nail matrix. The nails take on a ridged appearance on the nail plate.
The nail matrix is responsible for nail growth. It can be vulnerable to damage and disease. Seeing a doctor as soon as discoloration, pain, swelling, or other symptoms occur can ideally ensure you are treated as quickly as possible.
Abstract:Annually, millions of dollars are spent to carry out defect detection in key infrastructure including roads, bridges, and buildings. The aftermath of natural disasters like floods and earthquakes leads to severe damage to the urban infrastructure. Maintenance operations that follow for the damaged infrastructure often involve a visual inspection and assessment of their state to ensure their functional and physical integrity. Such damage may appear in the form of minor or major cracks, which gradually spread, leading to ultimate collapse or destruction of the structure. Crack detection is a very laborious task if performed via manual visual inspection. Many infrastructure elements need to be checked regularly and it is therefore not feasible as it will require significant human resources. This may also result in cases where cracks go undetected. A need, therefore, exists for performing automatic defect detection in infrastructure to ensure its effectiveness and reliability. Using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection. This paper provides a review of image-based crack detection techniques which implement image processing and/or machine learning. A total of 30 research articles have been collected for the review which is published in top tier journals and conferences in the past decade. A comprehensive analysis and comparison of these methods are performed to highlight the most promising automated approaches for crack detection.Keywords: crack detection; machine learning; artificial intelligence; image processing
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The present work reports an efficient way of capturing real-time crack propagation in concrete structures. The modified spectral analysis based algorithm and finite element modeling (FEM) were utilised for crack detection and quantitative analysis of crack propagation. Crack propagation was captured in cement-based composite (CBC) containing saw dust and M20 grade concrete under compressive loading using a simple and inexpensive 8-megapixel mobile phone camera. The randomly selected images showing crack initiation and propagation in CBCs demonstrated the crack capturing capability of developed algorithm. A measure of oriented energy was provided at crack edges to develop a similarity spatial relationship among the pairwise pixels. FE modelling was used for distress anticipation, by analysing stresses during the compressive test in constituents of CBCs. FE modeling jointly with the developed algorithm, can provide real-time inputs from the crack-prone areas and useful in early crack detection of concrete structures for preventive support and management.
Cracks and distress in concrete structures are generally due to restrained shrinkage, improper load balancing or material degradation. Their early detection and repair is a priority for proper maintenance as their development might lead to fatal damage and structural collapse. Before image processing techniques were developed, man-made inspection was the only crack inspection technique, but it is dreggy and requires considerable time dedication and skilled staff1,2. Image processing is cheap, accurate, and can be automated, becoming the alternative of this manual approach for structural inspection.
Image processing methods can provide a highly precise crack detection results based on local features of the crack image3. There are many powerful techniques in image processing for crack detection and takes a different form of images captured through the digital camera, Infra-red (IR) camera, ultrasonic imaging, laser imaging, time of flight diffraction (TOFD) and various other imaging technologies. The generalised process of crack detection using image processing involves image pre-processing and then utilise the preprocessed picture for the feature extraction and parameter estimation required for the determination of crack length and direction of propagation4.
The images captured using a digital camera for concrete crack detection is frequently reported5,6,7. These studies have attempted to explain the role of camera imaging for crack detection through threshold segmentation, neural network, image stitching, and acoustic emission. The crack detection using the structural feature (morphological and multidirectional shape of the crack) based algorithm on camera images has been developed and tested in a few studies8,9. Different filters based on region, edge and contrast features of the crack images have been used to find cracks in the multi-step model10,11,12,13. Some authors developed an artificial intelligence-based depth analysis model for the crack depth calculation6,14,15,16.
Compared to other methods available for image segmentation, thresholding is the simplest and fastest amongst all. Pal et al.25 studied the impact of thresholding and its associated methods such as hidden Markov random field (HMRF), Markov Random Field, and K-means Clustering for accurate crack detection. Clustering is a powerful technique26, and Sathya et al.27 discussed some important clustering methods such as k-means, improved k means, fuzzy c-mean (FCM), and improved fuzzy c-mean algorithm (IFCM) to determine the even small crack in the concrete structure. Jorden et al.28 presented two algorithms, one for spectral clustering and another for similarity matrix, to derive a new cost function for spectral clustering based on error measurement.
Noise, illumination conditions and macrotexture are some factors that can weaken crack information in captured images. Jin et al.33 developed a crack detection method based on spectral clustering. The proposed method worked not only on the local features (gray features) but also considered the step edge, roof, and line profile to improve the accuracy of the crack detection.
Although various methods have been proposed on crack detection using image processing, their accuracy still requires improvement, especially if the noise is present in the image acquisition environment. Thus, the first objective of the study was to develop an image processing algorithm for crack detection based on the modifications in the spectral clustering method to overcome stated challenges. However, this algorithm needs to take into account concrete composition, as it has been reported that the fracture initiation and crack propagation of concrete depend on the type of Supplementary cementitious material (SCM) incorporated4,7,8,9,10,11,12,13.
Waste wood dust is readily available as SCM at no cost in India53,54. Thus, the second objective of this study was to define the crack propagation pattern of CBC under compressive strength analysed combining FE quantitative analyses and SEM qualitative assessment. Finally, the effect of wood dust inclusion as SCM on the cracking pattern of a green CBC was also studied. Collectively, investigating these multidisciplinary aspects propose a novel way for unmanned inspection of the damage in the structure for appropriate maintenance.
A survey of crack detection algorithms including image processing, neural network, machine and deep learning based methods is available in the literature55. An 8-Mega Pixel mobile phone camera has captured crack initiation and propagation in green CBC samples during the compression test. The ordinary camera contributes to the lower cost requirement for the developed system. The images of the crack developed during the compression test were captured. Further, the spectral clustering method was used to detect the crack edges. For accurate crack detection, features for differentiating the cracks from the background were selected.
In general, the gray features for crack are derived from its roof, step change, and line profile33. The shape, contour, and texture information along with the gray features were used for modeling a global descriptor required for high accuracy in crack detection. This allows the detection of gray features on points where sharp intensity change occurs. The pattern of orders in-phase component present at edges was studied using Fourier transform. The phase component obtained thus used for oriented energy calculation for crack edge pixels in the image. 350c69d7ab
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