Grayscale Variation Research Articles

Local pixel patterns

In this paper, a new class of image texture operators is proposed. We firstly determine that the number of gray levels in each B × B subblock is a fundamental property of the local image texture. Thus, an occurrence histogram for each B × B sub-block can be utilized to describe the texture of the image. Moreover, using a new multi-bit plane strategy, i.e., representing the image texture with the occurrence histogram of the first one or more significant bit-planes of the input image, more powerful operators for describing the image texture can be obtained. The proposed approach is invariant to gray scale variations since the operators are, by definition, invariant under any monotonic transformation of the gray scale, and robust to rotation. They can also be used as supplementary operators to local binary patterns (LBP) to improve their capability to resist illuminance variation, surface transformations, etc.

Intensity-Invariant Texture Analysis for Classification of BI-RADS Category 3 Breast Masses

Radiologists likely incorrectly classify benign masses as Breast Imaging Reporting and Data System (BI-RADS) category 3. A computer-aided diagnosis (CAD) system was developed in this study as a second viewer to avoid misclassification of carcinomas. Sixty-nine biopsy-proven BI-RADS category 3 masses, including 21 malignant and 48 benign masses, were used to evaluate the CAD system. To improve the texture features, gray-scale variations between images were reduced by transforming pixels into intensity-invariant ranklet coefficients. The textures of the tumor and speckle pixels were extracted from the transformed ranklet images to provide more robust features than in conventional CAD systems. As a result, tumor texture and speckle texture with ranklet transformation achieved significantly better areas under the receiver operating characteristic curve (Az) compared with those without ranklet transformation (Az = 0.83 vs. 0.58 and Az = 0.80 vs. 0.56, p value < 0.05). The improved CAD system can be a second reader to confirm the classification of BI-RADS category 3 masses.

Robust phase-based texture descriptor for classification of breast ultrasound images.

BackgroundClassification of breast ultrasound (BUS) images is an important step in the computer-aided diagnosis (CAD) system for breast cancer. In this paper, a novel phase-based texture descriptor is proposed for efficient and robust classifiers to discriminate benign and malignant tumors in BUS images.MethodThe proposed descriptor, namely the phased congruency-based binary pattern (PCBP) is an oriented local texture descriptor that combines the phase congruency (PC) approach with the local binary pattern (LBP). The support vector machine (SVM) is further applied for the tumor classification. To verify the efficiency of the proposed PCBP texture descriptor, we compare the PCBP with other three state-of-art texture descriptors, and experiments are carried out on a BUS image database including 138 cases. The receiver operating characteristic (ROC) analysis is firstly performed and seven criteria are utilized to evaluate the classification performance using different texture descriptors. Then, in order to verify the robustness of the PCBP against illumination variations, we train the SVM classifier on texture features obtained from the original BUS images, and use this classifier to deal with the texture features extracted from BUS images with different illumination conditions (i.e., contrast-improved, gamma-corrected and histogram-equalized). The area under ROC curve (AUC) index is used as the figure of merit to evaluate the classification performances.Results and conclusionsThe proposed PCBP texture descriptor achieves the highest values (i.e. 0.894) and the least variations in respect of the AUC index, regardless of the gray-scale variations. It’s revealed in the experimental results that classifications of BUS images with the proposed PCBP texture descriptor are efficient and robust, which may be potentially useful for breast ultrasound CADs.

K-space design of terahertz plasmonic filters

Wireless communication systems in the terahertz (THz) frequency range promise to dramatically increase available bandwidth in the electromagnetic spectrum. These wireless systems will require filtering techniques capable of operating in this relatively unused part of the spectrum. Here, we report a versatile technique for designing different classes of THz plasmonic filters based on a k-space methodology, in which the desired frequency response is mapped into two-dimensional (2D) k-space and then inverse Fourier transformed into the spatial domain. We use a recently developed inkjet printing technique to fabricate the spatial patterns allowing for grayscale conductivity variation. In general, any technique that allows for high-fidelity reproduction of the real-space grayscale variation in the fabricated plasmonic structure can be used. We demonstrate the flexibility of this approach by creating several classes of filters that allow for changes in the relative magnitudes in multiresonant filters; the polarization dependence, where the anisotropy can be carefully controlled; and the resonance bandwidth. We further demonstrate that, by cascading or adding filter functions together, even more complex filter designs can be achieved. We expect this approach to dramatically expand the design capabilities for filter technology for THz systems applications, such as THz wireless communications as well as applications in other spectral regions.

用于弱纹理场景三维重建的机器人视觉系统

To realize the obstacle avoidance and automatic navigation of a robot in a low texture environment,an active stereo visual system consisting a binocular camera and a compact laser projector was established.The dense stereo matching algorithm was investigated.Firstly,the compact laser projector generated the spot patterns with excellent uniqueness and anti-noise performance for increasing the texture information.Then,an adaptive-window matching algorithm was proposed based on Integral Grayscale Variance(IGSV)and Integral Gradient Variance(IGV).The algorithm was used calculate the integral variance in a matching window using the integral image obtained by the left image.If it was greater than the variance threshold,the correlation between the left and right image pixelswas calculated to get the dense disparity maps.Experimental results show that the vision system accurately gets the 3Ddense scene around the robot and the 3D reconstruction accuracy is 0.16 mm,which is suitable for the obstacle avoidance and automatic navigation.As compared with the traditional methods,the computation cost of dense matching has at least decreased by 93%since the computation used for image variance could not increase with the size of the matching window.

Mosaic algorithm for remote sensing images based on minimum gradient point in local area

Seeking for the optimal seam- line is an important part in remote sensing image mosaic. Most algorithms for finding the optimal seam- line only emphasize the minimum difference between pixels rather than the integrity of objects. Therefore,the authors put forward a new algorithm for finding the optimal seam- line based on minimum gradient point in local area in this paper. This method adopts the gradient to indicate the change of gray scale and then finds the pixel of the minimum gradient in local area progressively according to the principle that the change of gray scale is milder when the gradient is smaller. Meanwhile,an improved difference method to solve the band effect caused by the hard correction method is proposed in this paper. The experimental results show that the optimal seam- line determined in this paper can avoid the great gray scale variation area and the objects can be reserved unbroken. It avoids the band effect well and the seam- line is removed smoothly,the mosaic image also has a good sense of sight. In addition,the proposed method is very simple,effective,and easy to realize.

Use of basis images for detection and classification of celiac disease.

Celiac disease commonly occurs in approximately 1% of populations, but it can be difficult to diagnose. The standard method to diagnose celiac disease includes analysis of endoscopy images of the small intestinal mucosa to detect presence of villous atrophy, which can be subtle. We have devised a means to improve the image-based detection of villous atrophy and other abnormality in videocapsule endoscopy by means of incorporating basis images. Basis images were extracted from a series of 200 consecutive image frames acquired over 100 seconds at the level of the duodenal bulb in 13 celiac patients and in 13 controls. They were converted from color to 256 grayscale levels (gsl; 0 = black, 255 = white). Eight basis images were used for analysis. A histogram was constructed for each basis image, and the mean and standard deviation of the histogram values were tabulated. The significance of the difference in histogram mean level for celiacs versus controls was determined. Then the histogram mean was plotted versus the standard deviation, separately for all eight basis images, and also averaged for all bases combined. The mean histogram level for celiacs was 127.59+6.05 gsl versus 129.25+5.53 gsl for controls (p< 0.05). Thus celiac basis images tended to be darker and also more variable as compared with controls. For nonlinear classification, using the average of combined basis images, the sensitivity was 84.6% while the specificity was 92.3%. Using the single most important basis image for nonlinear classification, the sensitivity was 84.6% while the specificity was 76.9%. Construction of basis images can be useful to condense videocapsule image series into salient information, for detection of differences in grayscale level mean and variation in celiac versus control image series, and for classification of celiac versus control videoclips with nonlinear discriminant functions.

Measurement of neutron energy spectrum from 241 Am-B source using CR-39 detectors and an in-house image analysis program (autoTRAK_n)

An attempt has been made in the present work to estimate the neutron spectrum of a standard 241 Am-B source using CR-39 solid polymeric track detector and an in-house image analysis program autoTRAK_n. The program works on the principle of greyscale variations in and around the recoil tracks and calculates the track parameters such as diameter, major, minor axes and estimates three dimensional parameters like recoil track depth and angular incidence. It is also capable of counting overlapping tracks without any segregation procedure. In the present study, the standard 241 Am-B neutron spectrum generated from the recoil track parameters with the autoTRAK_n program is compared with the reported values measured using the time of flight method and found to be matching well, mainly at the characteristic peak. The total neutron fluence and dose equivalents are also determined with the program and are compared with the standard source measurements. The results obtained from autoTRAK_n are found to be in close agreement with the standard measurements with less than 5% error for both the parameters. Hence, this methodology can be used as a backup technique for neutron spectrum measurements along with other techniques considering its low cost, small size, easy irradiation, and processing.

A Novel Texture Spectrum Descriptor

A concept of equivalence classes of texture pattern is put forward according to the visual consistency between the rotation texture and the flip texture. An improved texture spectrum descriptor (iTS) is proposed based on the equivalence classes. The iTS depicts the grayscale variation pattern of the pixels in the image neighbour domain and denotes the texture content of an image with a histogram of texture spectrum. Compared with the basic texture spectrum descriptor (TS), local binary pattern (LBP) and Shis local binary pattern (sLBP), iTS has best precision in the image retrieval experiments. The iTS has stronger ability to describle the texture and more adapt to the image rotation transformation.

Robust automatic facial expression detection method based on sparse representation plus LBP map

Recently the sparse representation based classification (SRC) is successfully used to automatically recognize facial expression, well-known for its ability to solve occlusion and corruption problems. The results of those methods which using different features conjunction with SRC framework show state of the art performance on clean or noised facial expression images. Therefore, the role of feature extraction for SRC framework will greatly affect the success of facial expression recognition (FER). In this paper, we select a new feature which called LBP map. This feature is generated using local binary pattern (LBP) operator. It is not only robust to gray-scale variation, but also extracts sufficient texture information for SRC to deal with FER problem. Then we proposed a new method using the LBP map conjunction with the SRC framework. Firstly, we compared our method with state of the art published work. Then experiments on the Cohn–Kanade database show that the LBP map+SRC can reach the highest accuracy with the lowest time-consuming on clean face images than those methods which use different features such as raw image, Downsample image, Eigenfaces, Laplacianfaces and Gabor conjunction with SRC. We also experiment the LBP map+SRC to recognize face image with partial occluded and corrupted, the result shows that this method is more robust to occlusion and corruption than existing methods based on SRC framework.

Corner Detection and Classification Using Anisotropic Directional Derivative Representations

This paper proposes a corner detector and classifier using anisotropic directional derivative (ANDD) representations. The ANDD representation at a pixel is a function of the oriented angle and characterizes the local directional grayscale variation around the pixel. The proposed corner detector fuses the ideas of the contour- and intensity-based detection. It consists of three cascaded blocks. First, the edge map of an image is obtained by the Canny detector and from which contours are extracted and patched. Next, the ANDD representation at each pixel on contours is calculated and normalized by its maximal magnitude. The area surrounded by the normalized ANDD representation forms a new corner measure. Finally, the nonmaximum suppression and thresholding are operated on each contour to find corners in terms of the corner measure. Moreover, a corner classifier based on the peak number of the ANDD representation is given. Experiments are made to evaluate the proposed detector and classifier. The proposed detector is competitive with the two recent state-of-the-art corner detectors, the He & Yung detector and CPDA detector, in detection capability and attains higher repeatability under affine transforms. The proposed classifier can discriminate effectively simple corners, Y-type corners, and higher order corners.

New Local Thresholding Method for Soil Images by Minimizing Grayscale Intra‐Class Variance

Recent advances in imaging techniques offer the possibility of visualizing the three‐dimensional structure of soils at very fine scales. To make use of such information, a thresholding process is commonly implemented to separate the image into solid particles and pores. Despite the multitude of thresholding algorithms available, their performance is being challenged by the complexity of the soil structure. Experience shows that, to improve thresholding performance, existing methods require significant input from a skilled operator, making the thresholding subjective. In this context, this article proposes a new, operator‐independent thresholding technique based on the analysis of the intraclass grayscale variance. The method extends the well‐established Otsu technique, by applying first a preclassification of the voxels corresponding to the solid phase. Then, a threshold value is determined through minimization of the intraclass variance of the unclassified voxels. The method was implemented globally, then locally for a range of window sizes, with the optimal window size selected as that for which the standardized grayscale variances of the two voxel populations are equal. Results on the three‐dimensional soil images investigated suggest that the proposed method performs noticeably better than Otsu's method, and in particular is robust enough to variations in image contrast and soil structure. Tested on a synthetic image, the new method produces a misclassification of only 2% of voxels, compared to 4.9% with Otsu's method. These results suggest that the proposed method can be very useful in the analysis of images of a variety of heterogeneous media, including soils.

Analysis of the Interaction between Tropomyosin Allergens and Antibodies Using a Biosensor Based on Imaging Ellipsometry

A biosensor based on high spatial resolution imaging ellipsometry has been studied to examine its role in the rapid detection and analysis of the tropomyosin allergen existing in crustaceans. This methodology has been established for detection of the tropomyosin allergen and includes ligand screening, the determination of sensitivity, and a comparison with traditional detection methods. Three kinds of monoclonal antibodies (2F9, 4C7, and 2H6) known to have a high bioactivity against the tropomyosin allergen were screened and separately immobilized as ligands on a silicon wafer surface, thus allowing them to capture the tropomyosin allergen. Resulting changes on the wafer surface were visualized in gray scale variation on an ellipsometry image. Images showed that these antibodies are able to recognize the presence of the tropomyosin allergen in shrimp and crab with sensitivity of 1 mg L(-1), and at a detecting time of approximately 30 min for an extracted sample. This preliminary study has shown that the detection of the tropomyosin allergen is rapid and specific when using this type of assay on products containing shrimp and crab. When compared with the enzyme-linked immunosorbent assay, the biosensor based on imaging ellipsometry is able to perform a fast label-free analysis.

Duration of the Early Bajocian and the associated δ 13 C positive excursion based on cyclostratigraphy

The Early Bajocian, about 172 Ma ago, was a period of tectonic changes, a global carbon-isotope positive excursion, and biological diversification of marine invertebrates (e.g. ammonites, radiolarians and coccolithophores). Unfortunately, both the duration of the Early Bajocian and the associated palaeoenvironmental changes are still poorly constrained. We propose here an estimate of the duration of this sub-stage based on a cyclostratigraphic analysis of the carbonate content from the Chaudon–Norante section, French Subalpine Basin, France. The Chaudon–Norante succession has been correlated with Les Dourbes section using greyscale variations to detect possible local hiatuses. The duration estimated here for the entire Early Bajocian is 4.082 Ma. Two intervals were identified in the positive δ 13 C excursion: an increase of carbon isotope values lasting 1.36 Ma and climax isotope values lasting for 2.72 Ma. A cooling at high latitudes and warming at low latitudes may have enhanced the Earth’s temperature gradient leading to an increase in humidity, which in turn triggered ocean eutrophication at the origin of the δ 13 C positive excursion. Supplementary material: The complete dataset presented in the paper, a colour version of the figure 8 and correlation of δ 13 C excursions between our studied site and contemporaneous sites are available at www.geolsoc.org.uk/SUP18555 .

Seam Tracking Monitoring during High-Power Fiber Laser Welding

Accurate detection of weld seam deviations from the desired path during laser welding process facilitates seam tracking monitoring and is thus of primary concern in performing successful weld. This paper proposes a novel method for real-time seam tracking monitoring during high-power fiber laser welding, as it investigates into the techniques of infrared image sensing and visual recognition. Molten pool images were taken by a high-speed camera arranged in the off-axis direction of laser beam. Accumulative grayscale variation based on a molten pool thermal distribution was extracted and used to determine the deviations between the laser beam focus and the weld seam center. The proposed algorithm was experimented on a butt joint laser welding of Type 304 stainless steel plates at a continuous wave fiber laser power of 10 kW.

Research on Flame Combustion Stability Based on Neural Network

For the use of boiler flame image analysis to detect the boiler flame combustion stability, when the combustion affected by coal, peaking , improper operation or other effects, the flame appeared short pulsation. In general, the traditional detection methods based on gray scale variance can not avoid the impact of flame pulsation on account of the inaccuracy of the boiler combustion stability detection. This paper presents a flame combustion instability detection method based on neural network and selects multiple features which are directly related to the flame stability as neural network input vector. Experiments show that this method can fight off the tiny ripple influence caused by the impurities combustion or peak and simultaneously, greatly improve the detection accuracy and stability.

Multiphase petrography of volcanic rocks using element maps: a method applied to Mount St. Helens, 1980–2005

Quantitative textural analyses including crystal size distributions (CSDs) provide insights into crystallisation kinetics of magmatic systems. Investigations of volcanic crystal textures often rely on greyscale variations on backscattered electron images to identify crystal phases, which must then be thresholded and/or traced manually, a laborious task, and investigations are typically restricted to a single crystal phase. A method is presented that uses energy-dispersive X-ray element maps to generate textural data. Each pixel is identified as a crystal phase, glass or vesicle according to relative chemical composition enabling concurrent acquisition of multiphase CSD, crystallinity and mineral mode data. Data processing is less time intensive for the operator but considerable instrument time is required to generate element maps. The method is applied to 17 dacite samples from the 1980–1986 and 3 from the 2004–2005 eruptive periods of Mount St. Helens volcano (USA) to provide quantitative insights into multiphase textural evolution. All of the CSDs are curved and concave-up in the standard CSD plot with curvature increasing with plagioclase content. To facilitate comparisons with previous studies, CSDs for microlites (<50 μm length crystals) are approximated as straight lines. The line intercepts and slopes provide information on n 0 (nucleation density) and characteristic length or Gτ (the product of growth rate (G) and residence time (τ)), respectively. These parameters, as well as the total groundmass crystallinity, show distinct differences between explosive deposits from summer 1980 and post-summer 1980 domes. Post-summer 1980 microlite n 0 values are mostly at the lower end of the range of those measured for summer 1980 samples. Total groundmass crystallinities during summer 1980 are between 10 and 30 vol.%, whereas post-summer 1980 crystallinity increases to between 39 and 51 vol.%. The range of n 0 values is similar to those previously published for Mount St. Helens but Gτ is consistently higher. Gτ of a May 1985 sample analysed in this study is approximately 2 μm higher compared with previously published data for the same sample when processed using similar methodologies. Groundmass crystallinity data show similar trends to those previously published for the 1980 to 1986 eruption, increasing sharply after summer 1980 then increasing more gradually during the dome-building phase of the eruption. The effects of varying L, the apparent crystal size, and crystal aspect ratio on resultant CSDs are also investigated. Whilst relative temporal variations in CSDs for a given dataset can be investigated, absolute values from different studies cannot be compared unless methods of data acquisition and processing are exactly the same.

Astronomical duration of polarity Chron C31r (Lower Maastrichtian): cyclostratigraphy of ODP Site 762 (Indian Ocean) and the Contessa Highway section (Gubbio, Italy)

AbstractThe duration of polarity Chron C31r is estimated with a cyclostratigraphic approach. Two sites are investigated: ODP Site 762 (Indian Ocean) and the Contessa Highway section (Gubbio, Italy). Cyclostratigraphic analysis is performed on greyscale variations (Site 762) and magnetic susceptibility variations (Contessa section). Both sites reveal an astronomical control of the sedimentation, highlighted by the identification of all the orbital periodicities. Cyclostratigraphic signals are tuned on 405 ka eccentricity cycles extracted from the La04 astronomical solution. In both sites, cycle counting gives an estimate of the duration of polarity Chron C31r of about 2.09 ± 0.03 Ma.

Automatic subarachnoid space segmentation and hemorrhage detection in clinical head CT scans

The subarachnoid space (SAS) lies between the arachnoid membrane and the pia mater of the human brain, normally filled with cerebrospinal fluid (CSF). Subarachnoid hemorrhage (SAH) is a serious complication of neurological disease that can have high mortality and high risk of disability. Computed tomography (CT) head scans are often used for diagnosing SAH which may be difficult when the hemorrhage is small or subtle. A computer-aided diagnosis system from CT images is thus developed to augment image interpretation. Supervised learning using the probability of distance features of several landmarks was employed to recognize SAS. For each CT image, the SAS was approximated in four steps: (1) Landmarks including brain boundary, midsagittal plane (MSP), anterior and posterior intersection points of brain boundary with the MSP, and superior point of the brain were extracted. (2) Distances to all the landmarks were calculated for every pixel in the CT image, and combined to construct a high-dimensional feature vector. (3) Using head CT images with manually delineated SAS as training dataset, the prior probabilities of distances for pixels within SAS and non-SAS were computed. (4) Any pixel of a head CT scan in the testing dataset was classified as an SAS or non-SAS pixel in a Bayesian decision framework based on its distance features. The proposed method was validated on clinical head CT images by comparison with manual segmentation. The results showed that the automated method is consistent with the gold standard. Compared with elastic registration based on grayscale information, the proposed method was less affected by grayscale variation between normal controls and patients. Compared with manual delineation, the average spatial overlap, relative overlap, and similarity index were, respectively, 89, 63, and 76% for the automatic SAS approximation of the 69 head CT scans tested. The proposed method was tested for SAH detection and yielded a sensitivity of 100% and a specificity of 92%. Automated SAH detection with high sensitivity was shown feasible in a prototype computer-aided diagnosis system. The proposed method may be extended for computer-aided diagnosis of several CSF-related diseases relevant to SAS abnormalities.

Local multiple patterns based multiresolution gray-scale and rotation invariant texture classification

The local binary pattern (LBP) is a robust but computationally simple approach in texture analysis. However, LBP does not have enough information to discriminate among multiple patterns due to its binary patterns only comprising of 0s and 1s. Thus, a multi-resolution gray-scale and rotation invariant texture classification based on local multiple patterns (LMP) is proposed in the paper. The LMP extends binary patterns to multiple patterns, which can preserve more structural information, and be more suitable for image analysis, including the analysis of flat image areas, in which case LBP code is often a random value and thus unsuitable. In addition, several extensions to the LMP, including multi-resolution analysis with the “uniform” LMP with rotation invariance, are presented. The “uniform” LMP can be regarded as a gray-scale and rotation invariant description in various applications and its discrete occurrence histogram over a region of image is proved to be a powerful invariant texture feature. Experimental results of multi-resolution texture classification with support vector machine show that the proposed method obtains overall better performance than other common methods in several aspects of space-resolution and gray-scale variation, rotation, and noise.