A kidney stone is a hard object that is made from chemicals in the urine. There are four types of kidney stones: calcium oxalate, uric acid, struvite, and cystine. A kidney stone may be treated with shockwave lithotripsy, uteroscopy, percutaneous nephrolithomy or nephrolithotripsy. Common symptoms include severe pain in lower back, blood in your urine, nausea, vomiting, fever and chills, or urine that smells bad or looks cloudy.

Heterogeneous surfaces are a combination of two or more distinct homogeneous surfaces. The pixels belonging to heterogeneous surfaces are known as mixed pixels. The spectral signature of a mixed pixel is a combination of two or more endmember signatures. This spatial heterogeneity is mainly due to the low spatial resolution of the hyperspectral sensor.

See your doctor and/or a registered dietitian about making diet changes if you have had a stone or think you could be at increased risk for getting a kidney stone. To guide you, they need to know your medical history and the food you eat. Here are some questions you might ask:

Kidney stones are found in children as young as 5 years. In fact, this problem is so common in children that some hospitals conduct 'stone' clinics for pediatric patients. The increase in the United States has been attributed to several factors, mostly related to food choices. The two most important reasons are not drinking enough fluids and eating foods that are high in salt. Kids should eat less salty potato chips and French fries. There are other salty foods: sandwich meats, canned soups, packaged meals, and even some sports drinks. Sodas and other sweetened beverages can also increase the risk of stones if they contain high fructose corn syrup.

Use the ppi function to estimate the endmembers by using the PPI approach. The PPI approach projects the pixel spectra to an orthogonal space and identifies extrema pixels in the projected space as endmembers. This is a non-iterative approach, and the results depend on the random unit vectors generated for orthogonal projection. To improve results, you must increase the random unit vectors for projection, which can be computationally expensive.

The band selection approach uses orthogonal space projections to find the spectrally distinct and most informative bands in the data cube. Use the selectBands and removeBands functions for the finding most informative bands and removing one or more bands, respectively.

Spectral unmixing is the process of decomposing the spectral signatures of mixed pixels into their constituent endmembers. The spectral unmixing process involves two steps:

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To compensate for the atmospheric effects, you must first calibrate the pixel values, which are digital numbers (DNs). You must preprocess the data by calibrating DNs using radiometric and atmospheric correction methods. This process improves interpretation of the pixel spectra and provides better results when you analyse multiple data sets. In addition, spectral distortions which occur due to hyperspectral sensor characteristics during acquisition, can lead to inaccuracies in the spectral signatures. To enhance the reliability of spectral data for further analysis, you must apply preprocessing techniques that significantly reduce spectral distortions in hyperspectral images. For information about hyperspectral data correction methods, see Hyperspectral Data Correction.

Spectralimage ghost

Less common types of stones are: Infection-related stones, containing magnesium and ammonia called struvite stones and stones formed from monosodium urate crystals, called uric acid stones, which might be related to obesity and dietary factors. The rarest type of stone is a cvstine stone that tends to run in families.

Non-destructive testing or visual inspection applications such as maturity monitoring of fruits.

You can reduce excess salt in your diet. What foods are high in salt? Everyone thinks of salty potato chips and French fries. Those should be rarely eaten. There are other products that are salty: sandwich meats, canned soups, packaged meals, and even sports drinks.

The false-color scheme uses a combination of any number of bands other than the visible red, green, and blue spectral bands. Use false-color representation to visualize the spectral responses of bands outside the visible spectrum. The false-color scheme efficiently captures distinct information across all spectral bands of hyperspectral data.

Drinking enough fluid will help keep your urine less concentrated with waste products. Darker urine is more concentrated, so your urine should appear very light yellow to clear if you are well hydrated. Most of the fluid you drink should be water. Most people should drink more than 12 glasses of water a day. Speak with a healthcare professional about the right amount of water that's best for you. Water is better than soda, sports drinks or coffee/tea. lf you exercise or if it is hot outside, you should drink more. Sugar and high-fructose corn syrup should be limited to small quantities.

The other preprocessing step that is important in all hyperspectral imaging applications is dimensionality reduction. The large number of bands in the hyperspectral data increases the computational complexity of processing the data cube. The contiguous nature of the band images results in redundant information across bands. Neighboring bands in a hyperspectral image have high correlation, which results in spectral redundancy. You can remove the redundant bands by decorrelating the band images. Popular approaches for reducing the spectral dimensionality of a data cube include band selection and orthogonal transforms.

Diagnosis of a kidney stone starts with a medical history, physical examination, and imaging tests. Your doctors will want to know the exact size and shape of the kidney stones. This can be done with a high resolution CT scan from the kidneys down to the bladder or an x-ray called a "KUB x-ray'' (kidney-ureter-bladder x-ray) which will show the size of the stone and its position. The KUB x-ray is often obtained by the surgeons to determine if the stone is suitable for shock wave treatment. The KUB test may be used to monitor your stone before and after treatment, but the CT scan is usually preferred for diagnosis. In some people, doctors will also order an intravenous pyelogram or lVP, a special type of X- ray of the urinary system that is taken after injecting a dye.

You can use the denoiseNGMeet function to remove noise from a hyperspectral data by using the non-local meets global approach.

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To enhance the spatial resolution of a hyperspectral data, you can use image fusion methods. The fusion approach combines information from the low resolution hyperspectral data with a high resolution multispectral data or panchromatic image of the same scene. This approach is also known as sharpening or pansharpening in hyperspectral image analysis. Pansharpening specifically refers to fusion between hyperspectral and panchromatic data. You can use the sharpencnmf function for sharpening hyperspectral data using coupled non-matrix factorization method.

Spectralimaging camera

KDOQI (Kidney Disease Outcomes Quality Initiative) is world renowned for improving the diagnosis and treatment of kidney disease, these guidelines have changed the practices of healthcare professionals and improved thousands of lives.

Perform target detection by matching the known spectral signature of a target material to the pixel spectra in hyperspectral data. For examples of target detection, see Target Detection Using Spectral Signature Matching and Ship Detection from Sentinel-1 C Band SAR Data Using YOLOX Object Detection.

In a hyperspectral image, the intensity values recorded at each pixel specify the spectral characteristics of the region that the pixel belongs to. The region can be a homogeneous surface or heterogeneous surface. The pixels that belong to a homogeneous surface are known as pure pixels. These pure pixels constitute the endmembers of the hyperspectral data.

Possible causes include drinking too little water, exercise (too much or too little), obesity, weight loss surgery, or eating food with too much salt or sugar. Infections and family history might be important in some people. Eating too much fructose correlates with increasing risk of developing a kidney stone. Fructose can be found in table sugar and high fructose corn syrup.

After it is formed, the stone may stay in the kidney or travel down the urinary tract into the ureter. Sometimes, tiny stones move out of the body in the urine without causing too much pain. But stones that don't move may cause a back-up of urine in the kidney, ureter, the bladder, or the urethra. This is what causes the pain.

Eat more fruits and vegetables, which make the urine less acid. When the urine is less acid, then stones may be less able to form. Animal protein produces urine that has more acid, which can then increase your risk for kidney stones.

The hyperpca and hypermnf functions reduce the spectral dimensionality of the data cube by using the PCA and MNF transforms respectively. You can use the pixel spectra derived from the reduced data cube for hyperspectral data analysis.

The CIR color scheme uses spectral bands in the NIR range. The CIR representation of a hyperspectral data cube is particularly useful in displaying and analyzing vegetation areas of the data cube.

Abundance map estimation — Given the endmember signatures, it is useful to estimate the fractional amount of each endmember present in each pixel. You can generate the abundance maps for each endmember, which represent the distribution of endmember spectra in the image. You can label a pixel as belonging to an endmember spectra by comparing all of the abundance map values obtained for that pixel.

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Spectralimaging CT

To visualize and understand the object being imaged, it is useful to represent the data cube as a 2-D image by using color schemes. The color representation of the data cube enables you to visually inspect the data and supports decision making. You can use the colorize function to compute the Red-Green-Blue (RGB), false-color, and color-infrared (CIR) representations of the data cube.

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Shock-wave lithotripsy is a noninvasive procedure that uses high-energy sound waves to blast the stones into fragments that are then more easily passed out in the urine. In ureteroscopy, an endoscope is inserted through the ureter to retrieve or obliterate the stone. Rarely, for very large or complicated stones, doctors will use percutaneous nephrolithotomy/nephrolithotripsy.

Orthogonal transforms such as principal component analysis (PCA) and maximum noise fraction (MNF), decorrelate the band information and find the principal component bands.

Hyperspectral image processing involves representing, analyzing, and interpreting information contained in hyperspectral images.

There are four types of stones. Studying the stone can help understand why you have it and how to reduce the risk of further stones. The most common type of stone contains calcium. Calcium is a normal part of a healthy diet. The kidney usually removes extra calcium that the body doesn't need. Often people with stones keep too much calcium. This calcium combines with waste products like oxalate to form a stone. The most common combination is called calcium oxalate.

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Spectral imagesmeaning

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Spectralinformation in remote sensing

Use the nfindr function to estimate the endmembers by using the N-FINDR method. N-FINDR is an iterative approach that constructs a simplex by using the pixel spectra. The approach assumes that the volume of a simplex formed by the endmembers is larger than the volume defined by any other combination of pixels. The set of pixel signatures for which the volume of the simplex is high are the endmembers.

The kidney stone starts to hurt when it causes irritation or blockage. This builds rapidly to extreme pain. In most cases, kidney stones pass without causing damage-but usually not without causing a lot of pain. Pain relievers may be the only treatment needed for small stones. Other treatment may be needed, especially for those stones that cause lasting symptoms or other complications. In severe cases, however, surgery may be required.

You can also use spectral matching to identify materials or perform target detection, detecting specific targets in a hyperspectral image when the spectral signature of the target is distinct from other regions in the hyperspectral image. However, when the spectral contrast between the target and other regions is low, spectral matching becomes more challenging. In such cases, you must use more sophisticated target detection algorithms, such ad those provided by the detectTarget function, that consider the entire hyperspectral data cube and use statistical or machine learning methods. For more information on spectral matching and target detection techniques, see Spectral Matching and Target Detection Techniques.

For hyperspectral image processing, the values read from the data file are arranged into a three-dimensional (3-D) array of the form M-by-N-by-C, where M and N are the spatial dimensions of the acquired data, C is the spectral dimension specifying the number of spectral wavelengths (bands) used during acquisition. Thus, you can consider the 3-D array as a set of two-dimensional (2-D) monochromatic images captured at varying wavelengths. This set is known as the hyperspectral data cube or data cube.

Remote sensing applications such as identification of vegetation, water bodies, and roads, as different landscapes have distinct spectral signatures.

You want to try to get to a normal weight if you are overweight. But, high-protein weight loss diets that include high amounts of animal-based protein, as well as crash diets can add to the risk of stone formation. You need adequate protein, but it needs to be part of a balanced diet. Seek guidance from a registered dietitian when embarking on a weight loss diet or any dietary interventions to reduce the risk of kidney stones.

The prevalence of kidney stones in the United States increased from 3.8% in the late 1970s to 8.8% in the late 2000s. The prevalence of kidney stones was 10% during 2013–2014. The risk of kidney stones is about 11% in men and 9% in women. Other diseases such as high blood pressure, diabetes, and obesity may increase the risk for kidney stones.

The hyperspectral imaging sensors typically have high spectral resolution and low spatial resolution. The spatial and the spectral characteristics of the acquired hyperspectral data are characterized by its pixels. Each pixel is a vector of values that specify the intensities at a location (x,y) in z different bands. The vector is known as the pixel spectrum, and it defines the spectral signature of the pixel located at (x,y). The pixel spectra are important features in hyperspectral data analysis. But these pixel spectra gets distorted due to factors such as sensor noise, low resolution, atmospheric effects, and spectral distortions from sensors.

The hypercube function constructs the data cube by reading the data file and the metadata information in the associated header file. The hypercube function creates a hypercube object and stores the data cube, spectral wavelengths, and the metadata to its properties. You can use the hypercube object as input to all other functions in the Hyperspectral Imaging Library for Image Processing Toolbox™. You can use the Hyperspectral Viewer app to interactively visualize and process hyperspectral images.

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NKF PEERS is a program where kidney patients, living donors, or care partners can connect over the phone with a trained mentor.

Hyperspectralimagesdataset

Hyperspectral imaging measures the spatial and spectral characteristics of an object by imaging it at numerous different wavelengths. The wavelength range extends beyond the visible spectrum and covers the spectrum from ultraviolet (UV) to long wave infrared (LWIR) wavelengths. The most popular are the visible, near-infrared, and mid-infrared wavelength bands. A hyperspectral imaging sensor acquires several images with narrow and contiguous wavelengths within a specified spectral range. Each of these images contains more subtle and detailed information. The different information in the various wavelengths is useful in diverse applications such as these.

Hyperspectral image processing applications include land cover classification, material analysis, target detection, change detection, visual inspection, and medical image analysis.

The RGB color scheme uses the red, green, and blue spectral band responses to generate the 2-D image of the hyperspectral data cube. The RGB color scheme brings a natural appearance, but results in a significant loss of subtle information.

Some kidney stones are as small as a grain of sand. Others are as large as a pebble. A few are as large as a golf ball! As a general rule, the larger the stone, the more noticeable are the symptoms.

Kidney stones increase the risk of developing chronic kidney disease. lf you have had one stone, you are at increased risk of having another stone. Those who have developed one stone are at approximately 50% risk for developing another within 5 to 7 years.

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The treatment for kidney stones is similar in children and adults. You may be asked to drink a lot of water. Doctors try to let the stone pass without surgery. You may also get medication to help make your urine less acid. But if it is too large, or if it blocks the flow of urine, or if there is a sign of infection, it is removed with surgery.

The values measured by a hyperspectral imaging sensor are stored to a binary data file by using band sequential (BSQ), band-interleaved-by-pixel (BIP), or band-interleaved-by-line (BIL) encoding formats. The data file is associated to a header file that contains ancillary information (metadata) like sensor parameters, acquisition settings, spatial dimensions, spectral wavelengths, and encoding formats that are required for proper representation of the values in the data file. Alternatively, the ancillary information can also be directly added to the data file as in TIFF and NITF file formats.

Perform visual inspection and non-destructive testing operations, such as maturity monitoring of fruit. The comprehensive spectral data available in hyperspectral images enables precise and non-destructive analysis. For an example, see Predict Sugar Content in Grape Berries Using PLS Regression on Hyperspectral Data.

Use the fippi function to estimate the endmembers by using the FIPPI approach. The FIPPI approach is an iterative approach, which uses an automatic target generation process to estimate the initial set of unit vectors for orthogonal projection. The algorithm converges faster than the PPI approach and identifies endmembers that are distinct from one another.

MNF computes the principal components that maximize the signal-noise-ratio, rather than the variance. MNF transform is particularly efficient at deriving principal components from noisy band images. The principal component bands are spectrally distinct bands with low interband correlation.

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You can also use the spectral indices for change detection and threshold-based segmentation of hyperspectral images. To segment regions that cannot be distinguished using spectral indices, you can use spectral clustering approaches such as Simple Linear Iterative Clustering (SLIC) with the hyperslic function or anchor graphs with the hyperseganchor function.

Some herbal substances are promoted as helping prevent stones. You should know that there is insufficient published medical evidence to support the use of any herb or supplement in preventing stones.

Later, your doctor will want to find the cause of the stone. The stone will be analyzed after it comes out of your body, and your doctor will test your blood for calcium, phosphorus and uric acid. The doctor may also ask that you collect your urine for 24 hours to test for calcium and uric acid.

Processing hyperspectral images of very large spatial resolution requires a large amount of system memory, and might cause MATLAB to run out of memory. You can crop a large hyperspectral image to a small region of interest, and then read only that small region into memory using the hypercube object and its functions. For an example of how to process small regions of large hyperspectral images, see Process Large Hyperspectral Images.

Classify land cover by classifying each pixel in a hyperspectral image. For examples of classification, see these examples.

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Spectralimage Processing

Interpret the pixel spectra by performing spectral matching using the spectralMatch function or target detection using the detectTarget function. Spectral matching identifies the class of an endmember material by comparing its spectra with one or more reference spectra. The reference data consists of pure spectral signatures of materials, which are available as spectral libraries. Use the readEcostressSig function to read the reference spectra files from the ECOSTRESS spectral library. Then, you can compute the similarity between the spectra in the ECOSTRESS library files and the spectra of an endmember material by using the spectralMatch function.

Endmember extraction — The spectra of the endmembers are prominent features in the hyperspectral data and can be used for efficient spectral unmixing of hyperspectral images. Convex geometry based approaches, such as pixel purity index (PPI), fast iterative pixel purity index (FIPPI), and N-finder (N-FINDR) are some of the efficient approaches for endmember extraction.

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Answer a few simple questions to find out if you are one of the 33% of adults in the U.S. who is at risk for kidney disease.

Hyperspectralimages

Urine has various wastes dissolved in it. When there is too much waste in too little liquid, crystals begin to form. The crystals attract other elements and join together to form a solid that will get larger unless it is passed out of the body with the urine. Usually, these chemicals are eliminated in the urine by the body's master chemist: the kidney. In most people, having enough liquid washes them out or other chemicals in urine stop a stone from forming. The stone-forming chemicals are calcium, oxalate, urate, cystine, xanthine, and phosphate.

A spectral index is a function such as a ratio or difference of two or more spectral bands. Spectral indices delineate and identify different regions in an image based on their spectral properties. By calculating a spectral index for each pixel, you can transform the hyperspectral data into a single-band image where the index values indicate the presence and concentration of a feature of interest. Use the spectralIndices and customSpectralIndex functions to identify different regions in the hyperspectral image. For more information on spectral indices, see Spectral Indices.

Don't be confused about having a "calcium" stone. Dairy products have calcium, but they actually help prevent stones, because calcium binds with oxalate before it gets into the kidneys. People with the lowest dietary calcium intake have an increased risk of kidney stones. A stone can form from salt, the waste products of protein, and potassium. The most common type of kidney stone is a calcium oxalate stone. Most kidney stones are formed when oxalate, a by product of certain foods, binds to calcium as urine is being made by the kidneys. Both oxalate and calcium are increased when the body doesn't have enough fluids and also has too much salt. Based on blood and urine tests, your doctor will determine which types of dietary changes are needed in your particular case.

Use the estimateAbundanceLS function to estimate the abundance maps for each endmember spectra.

Second, your doctors will decide how to treat your stone. The health of your kidneys will be evaluated by blood tests and urine tests. Your overall health, and the size and location of your stone will be considered.

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See a doctor as soon as possible. You may be asked to drink extra fluid in an attempt to flush out the stone out in the urine. If you strain your urine and can save a piece of the stone that has passed, bring it to your doctor. Or, the stone may need to be removed with surgery.

Each year, more than half a million people go to emergency rooms for kidney stone problems. It is estimated that one in ten people will have a kidney stone at some time in their lives.

Detect changes in hyperspectral images over time. For examples of change detection, see Change Detection in Hyperspectral Images and Map Flood Areas Using Sentinel-1 SAR Imagery.

Identify materials in a hyperspectral image using a spectral library. For an example, see Endmember Material Identification Using Spectral Library.

PCA transforms the data to a lower dimensional space and finds principal component vectors with their directions along the maximum variances of the input bands. The principal components are in descending order of the amount of total variance explained.