Histogram Correlation Research Articles

A multi-class bi-level thresholding method for accurate anthropometric measurements of scanned plantar images

Traditionally, the anthropometric measurements of foot or insole were taken manually by an anthropometrist for clinical and sporty purposes. Digital foot scanners have been developed in recent years to yield anthropometrists digital image of plantar with pressure distribution and anthropometric information. In this paper, an automated thresholding method based on Shanbag entropy and the weighted gray level spatial correlation histogram is presented for analysis of scanned foot images. The presented method compared with traditional two-dimensional histogram and other single-class-based methods automatically accurately detects the foot edges and areas. Also, by background removal, it attains a perfect foot image while in which white pixels of the resulting thresholded image correspond to the points of the scanner which detects pressure and on the contrary the black pixels correspond to background. We are taking into accounts the image’s local properties along with its global properties in a fuzzy domain by employing weighted gray level spatial correlation histogram (W-GLSC). First, a three-dimensional histogram based on the statistics of the gray levels’ probability and similarity with neighboring pixels (GLSC histogram) is weighted by the membership values assigned by Jawahar clustering method for foreground–background discrimination. Then, the Shanbag entropy is used to maximize the information transfer from the original image to the resulting thresholded image. Resulting binary images are undergone anthropometric measurements by considering the scale factor of pixel size to metric scale. The proposed method is finally applied to plantar images obtained through scanning feet of randomly selected subjects by a foot scanner system as our experimental setup described in the paper.

A Hybrid Soft-computing Method for Image Analysis of Digital Plantar Scanners

Digital foot scanners have been developed in recent years to yield anthropometrists digital image of insole with pressure distribution and anthropometric information. In this paper, a hybrid algorithm containing gray level spatial correlation (GLSC) histogram and Shanbag entropy is presented for analysis of scanned foot images. An evolutionary algorithm is also employed to find the optimum parameters of GLSC and transform function of the membership values. Resulting binary images as the thresholded images are undergone anthropometric measurements taking in to account the scale factor of pixel size to metric scale. The proposed method is finally applied to plantar images obtained through scanning feet of randomly selected subjects by a foot scanner system as our experimental setup described in the paper. Running computation time and the effects of GLSC parameters are investigated in the simulation results.

Image Segmentation based on FUZZY GLSC Histogram with Dynamic Similarity Discrimination Factor

Image processing applications performs image segmentation as pre-processing technique to extract the features for next stage. The application performance depends on image segmentation, to process the foreground or background objects. The image segmentation plays a vital role in computer vision and image processing applications. In spite of having many thresholding techniques in literature they have their own limitations. This paper proposes a new method of thresholding using Gray Level Spatial Correlation (GLSC) histogram with a dynamic similarity discrimination factor ( ) and Fuzzy logic in deciding the threshold using Shannon’s entropy. The similarity discrimination factor ( ) is made dynamic by considering the absolute difference between the global and local mean of the image. Calculating the threshold in the Fuzzyfied region makes the segmentation process the most time efficient than the existing methods. Experimental results prove better efficiency ( ) than the existing methods. The technique out performs in case of low contrast images. General Terms—Segmentation, histogram, Fuzzy technique.

F2Cor: fast 2-stage correlation algorithm for FCS and DLS

We present a new multiple-tau correlation algorithm which is the fastest to date. The resulting curve is identical to that obtained with the conventional multiple-tau algorithm, but the calculation time is much shorter. It combines two approaches. For short values of the lag-time a very simple correlation histogram is used, while for higher lag-time values the traditional multiple-tau bin-and-multiply approach is used. The lag-time limit between these two stages depends on the count rate. The computation time scales linearly with the count rate and is as fast as 0.1 µs/photon.

Image encryption using HC-128 and HC-256 stream ciphers

In this paper, we surveyed HC-128 and HC-256 as methods for protecting the distribution of digital images in an efficient and secure way. We proposed the Hongjun Cipher (HC) image encryption algorithm based on column-wise raster scanning of the plain image. Then, we performed a series of tests and some comparisons to justify the efficiency of surveyed algorithms for image encryption. These tests included key space analysis, visual test and histogram analysis, randomness analysis, information entropy, encryption quality, correlation analysis, differential analysis, sensitivity analysis and performance analysis. Based on all analysis and experimental results, it can be concluded that the two variants of HC scheme are efficient, feasible and trustworthy to be adopted for image encryption.

Image Retrieval Based on Limited Hue Value on Information Entropy

A new image retrieval algorithm based on color histogram information entropy is presented.Because the traditional method has not considered the symmetrical character of entropy,so it is easy to create mistakes.By using a limited hue value on information entropy the new method can solve this problem well.As to evaluate the accuracy,we used histogram analysis and correlation coefficients,from qualitative and quantitative of two views to make sure accuracy is dependable.The results show that use this method to search for a similar image is possible and have a high accuracy,the correlation coefficients is above 0.95,and in practical application this method also has a good possibility.

Improved Entropic Threshold based on GLSC Histogram with Varying Similarity Measure

Image segmentation is a necessary task in computer vision and digital image processing applications, where foreground objects are to be separated from background. Many thresholding techniques are found in literature with their own limitations. The Gray level Spatial Correlation (GLSC) Histogram is used in entropic techniques to decide the threshold. In this paper we propose an improved GLSC Histogram, computed with varying similarity measure ( ) by considering local and global characteristics, because Yang Xiao et. al. used a constant 4 as the similarity measure by considering the image local properties only, which does not suits for all types of images and probability error is minimized by redistributing the missing probability amount in floating precisions. For low contrast images contrast enhancement is assumed. Experimental results demonstrate a quantitative improvement against existing techniques by calculating the parameter efficiency η based on the misclassification error and variations in various yielding towards ground truth threshold on two dimensional histogram of image. General Terms Image processing, entropy

Negative edges and soft thresholding in complex network analysis of resting state functional connectivity data

Complex network analyses of functional connectivity have consistently revealed non-random (modular, small-world, scale-free-like) behavior of hard-thresholded networks constructed from the right-tail of the similarity histogram. In the present study we determined network properties resulting from edges constrained to specific ranges across the full correlation histogram, in particular the left (negative-most) tail, and their dependence on the confound signal removal strategy employed. In the absence of global signal correction, left-tail networks comprised predominantly long range connections associated with weak correlations and were characterized by substantially reduced modularity and clustering, negative assortativity and γ<1 Deconvolution of specific confound signals (white matter, CSF and motion) resulted in the most robust within-subject reproducibility of global network parameters (ICCs~0.5). Global signal removal altered the network topology in the left tail, with the clustering coefficient and assortativity converging to zero. Networks constructed from the absolute value of the correlation coefficient were thus compromised following global signal removal since the different right-tail and left-tail topologies were mixed. These findings informed the construction of soft-thresholded networks, replacing the hard thresholding or binarization operation with a continuous mapping of all correlation values to edge weights, suppressing rather than removing weaker connections and avoiding issues related to network fragmentation. A power law adjacency function with β=12 yielded modular networks whose parameters agreed well with corresponding hard-thresholded values, that were reproducible in repeated sessions across many months and evidenced small-world-like and scale-free-like properties.

An image feature-based approach to automatically find images for application to clinical decision support

The illustrations in biomedical publications often provide useful information in aiding clinicians’ decisions when full text searching is performed to find evidence in support of a clinical decision. In this research, image analysis and classification techniques are explored to automatically extract information for differentiating specific modalities to characterize illustrations in biomedical publications, which may assist in the evidence finding process. Global, histogram-based, and texture image illustration features were compared to basis function luminance histogram correlation features for modality-based discrimination over a set of 742 manually annotated images by modality (radiological, photo, etc.) selected from the 2004–2005 issues of the British Journal of Oral and Maxillofacial Surgery. Using a mean shifting supervised clustering technique, automatic modality-based discrimination results as high as 95.57% were obtained using the basis function features. These results compared favorably to other feature categories examined. The experimental results show that image-based features, particularly correlation-based features, can provide useful modality discrimination information.

New entropic thresholding approach using gray-level spatial correlation histogram

Two-dimensional entropic thresholding methods apply gray-level spatial correlation to thresholding and achieve much better performance than 1-D methods, while suffering from large time consumption. To utilize gray-level spatial correlation in thresholding with less time consumption, we define and describe a new entropic thresholding approach employing the gray-level spatial correlation (GLSC) histogram. The GLSC histogram is determined using the gray value of the pixels and the number of their neighboring pixels of similar gray value, which is different from a 2-D histogram. During the entropic criterion function computation, the entropy yielded by different elements in the GLSC histogram is weighted by a nonlinear weighting function, which we suggest. In the experiment, Kapur's 1-D method and three 2-D methods reported by Abutaleb and Sahoo are employed for comparision. Experiments on many real-world images demonstrate that the proposed method yields equivalent or even better results than 2-D ones while saving time remarkably and significantly outperforms Kapur's 1-D method without too much more time consumption generally.

Dominant multipoles in WMAP5 mosaic data correlation maps

The method of correlation mapping on the full sphere is used to study the properties of the ILC map, as well as the dust and synchrotron background components. An anomalous correlation of the components with the ILC map in the main plane and in the poles of the ecliptic and equatorial coordinate systems was discovered. Apart from the bias, a dominant quadrupole contribution in the power spectrum of the mosaic correlation maps was found in the pixel correlation histogram. Various causes of the anomalous signal are discussed.

An optical flow and inter-frame block-based histogram correlation method for moving object detection

Moving object detection is very important for video surveillance. In this paper, we present a new real time motion detection algorithm that is based on the integration of optical flow and inter-frame block-based histogram correlation method to achieve better performance. The optical flow method is used to detect any movement under the background and the inter-frame block-based histogram correlation method is used to eliminate the background information and separate the moving object from it. The biggest advantage of this algorithm is that it does not need to learn the background model from hundreds of images and can handle quick image variations without prior knowledge about the object size and shape. The algorithm has high capability of anti-interference and preserves high accurate rate detection at the same time. The effectiveness of the proposed algorithm for motion detection is demonstrated in a simulation environment and the evaluation results are reported in this paper.

3P-033 フォトンカウンティングヒストグラム法と蛍光相関分光法を用いたAβ多量体形成の解析(蛋白質・機能,第46回日本生物物理学会年会)

3P-033 フォトンカウンティングヒストグラム法と蛍光相関分光法を用いたAβ多量体形成の解析(蛋白質・機能,第46回日本生物物理学会年会)

Wavelet correlogram: A new approach for image indexing and retrieval

In this paper, a new algorithm for content-based image indexing and retrieval is presented. The proposed method is based on a combination of multiresolution image decomposition and color correlation histogram. According to the new algorithm, wavelet coefficients of the image are computed first using a directional wavelet transform such as Gabor wavelets. A quantization step is then applied before computing one-directional autocorrelograms of the wavelet coefficients. Finally, index vectors are constructed using these one-directional wavelet correlograms. The retrieval results obtained by application of our new method on a 1000 image database demonstrated a significant improvement in effectiveness and efficiency compared to the indexing and retrieval methods based on image color correlogram or wavelet transform.

SU‐DD‐A4‐01: Multi‐Day Multi‐Modality Image Co‐Registration

Purpose: To develop a methodology for multi‐day co‐registration of multi‐modality images taken throughout the course of radiotherapy, for the assessment of tumor response to treatment. Method and Materials: CT and PET image data sets of canines with various tumors were taken on a PET/CT scanner and were automatically co‐registered to each other by the scanner software. Using a commercial software package, ROI segmentation was performed on the CT data to define the tumor boundaries. Each PET data set was then masked by the ROI of its aligned CT image and then rigidly co‐registered with the masked PET data from other days. To align more specific uptake regions within the tumor, image warping was performed via landmarks placed on the co‐registered multi‐day PET data. Results: We found that ROIs can be roughly aligned by rigid registration of the outer tumor region as defined by the segmentation of CT data. Histogram correlation plots, aided by visual assessment, illustrated that normalized mutual information and Euclidean distance were the best‐suited criteria for rigid registration, and that increased accuracy in co‐registration was gained from image warping via landmarks placed on specific structures within the day‐to‐day rigidly co‐registered PET data. A review of warping techniques on the co‐registered PET data and generated data phantoms demonstrated that thin‐plate spline warping provided the most complete warping of landmarked structures, while flow warping was not as aggressive with non‐landmarked structures. Conclusion: Using this methodology of rigid registration of tumor boundaries defined from CT data, followed by inner feature warping of PET data proves to be an accurate and usable way to perform multi‐day multi‐modality image co‐registration. Further investigations include additional contouring of segmented regions, adding scaling parameters to the initial registrations, and refining landmark placement and warping techniques.

Automated event clustering and quality screening of consumer pictures for digital albuming

In this paper, algorithms for automatic albuming of consumer photographs are described. Specifically, two core algorithms namely event clustering and screening of low-quality images, are introduced and their performance is evaluated. Event clustering and image quality screening have many applications including albuming services, image management and organization, and digital photofinishing. These are difficult tasks because there is, in general, none (or very limited) contextual information about picture content, and the final interpretation could be subjective. A novel event-clustering algorithm is created to automatically segment pictures into events and subevents for albuming, based on date/time metadata information, as well as color content of the pictures. A block-based color histogram correlation technique is developed for image content comparison of general consumer pictures. A new quality-screening algorithm is developed based on object quality measures, to detect problematic images caused by underexposure, low contrast, and camera defocus or movement.

On the direct determination of three-dimensional crystallographic phases at low resolution: crambin at 6 A.

Using a pseudo-atom approach, the three-dimensional crystallographic phases for the protein crambin (a = 40.76, b = 18.49, c = 22.33 A, beta = 90.61 degrees, space group P2(1)) were determined to 6 A by direct methods. First, the centrosymmetric h0l set was assigned phases by symbolic addition, and the initial solution was then refined by Fourier methods. Phase values of strong reflections were then permuted, and the decision to change the phase value for two of these was made by consulting a cross-correlation of the experimental density histogram to the theoretical or known histogram for the protein. The two-dimensional basis was then extended by the Sayre equation into three dimensions by assigning a phase to a third allowed hkl origin-defining reflection and an algebraic value to another axial reflection. The correct solution was again identified by the histogram correlation, yielding a solution in which the mean phase error for all 98 reflections was 61.5 degrees or 23.1 degrees for the 21 most intense reflections. A parallel study with another protein indicates this method may have general utility.

Mechanisms of Neural Synchrony in the Septohippocampal Pathways Underlying Hippocampal Theta Generation

Using urethane-anesthetized rats, 18 simultaneously recorded septohippocampal cell pairs (36 individual cells), each classified as theta-related according to the criteria of, were studied during four spontaneously occurring hippocampal field conditions: (1) large amplitude irregular activity (LIA) only; (2) the transition from LIA to theta; (3) theta only; and (4) the transition from theta to LIA. The main objective was to study the temporal relationships and degree of neural synchrony between the discharges of the cell pairs, using both time-averaged and time-dependent joint peristimulus time histogram correlation techniques, during the four conditions, to determine their contribution to the control of oscillation and synchrony (theta) in the hippocampus. The study demonstrated that the transition from the LIA state to the theta field state in the hippocampus required a temporal sequence of changes in theta-related cellular activity occurring on average 500 msec preceding the transition: (1) the medial septum inhibits hippocampal theta-OFF cells; (2) medial septal tonic theta-ON cells provide tonic depolarizing inputs to initiate membrane potential oscillations (MPOs) in hippocampal phasic theta-ON cells, whereas medial septal phasic theta-ON cells synchronize the MPOs of hippocampal phasic theta-ON cells and the discharges of hippocampal tonic theta-ON cells. Much of the time preceding the LIA to theta transition is accounted for by recruitment of these theta-related cell populations. Conversely, "turning off" the theta state occurs abruptly and involves the medial septal disinhibition of hippocampal theta-OFF cells.

Correlation of late radiation rectal damage and dose surface histograms in prostate patients treated by high dose conformal radiotherapy

Correlation of late radiation rectal damage and dose surface histograms in prostate patients treated by high dose conformal radiotherapy

Chapter 20 A framework for the analysis of neuronal networks

The object of this work is to consider the application of some methods of spike train analysis that are not widely known, and are concerned with the description of the interaction between spike trains and the determination of causal connections between them. The notation and terminology follow conventions established in the statistical literature. The examples given are based on in-continuity recordings of the spontaneous activity of single Ia afferents from the soleus muscle and single motor units from the same muscle. Cumulant densities are shown to be simple extensions of the traditional cross-correlation methods, and are useful in characterizing the pattern of activity in one spike train that influences that in another, and to reveal interactions between spike trains that would not be apparent from the correlation histogram alone. Parameters based on the Fourier transforms of the spike trains are shown to be useful in determining timing relations between them, and in inferring patterns of connectivity not possible by correlation methods alone.