binary segmentation

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A model based circular binary segmentation algorithm for the analysis of array CGH data

A model based circular binary segmentation algorithm for the analysis of array CGH data

Circular Binary Segmentation (CBS) performs consis- tently in detecting change-points and thus provides us a good framework for further improvements. The frame- work of CBS is mainly constituted of three steps: candi- date location, significance evaluation, and edge effect correction. The first step, candidate location, locates candidate change-points by a maximal-t statistic. The second step, significance evaluation, approximates the significance of change-points by permutations or a hybrid method; the last step, edge effect correction, removes errors near the edges due to a circling process. Of these steps, significance evaluation was the major component that made the algorithm time-consuming. A time consumption study (shown in Table 1) on the hybrid CBS that involved analyzing ten breast cancer microarrays supported the above statement: among the
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Convolutional Neural Networks in Application to Segmentation of Fingerprint Images

Convolutional Neural Networks in Application to Segmentation of Fingerprint Images

To setup the training phase, we manually label out all fingerprint images taken from FVC (2002 [2], 2004[16]). For this purpose, for each image, the foreground area was highlighted. The result of this selection is a manually crafted ground truth binary segmentation mask (a binary image of the same dimension encoding foreground and background pixels by 1 and 0, respectively). The resulting mask is divided into non-overlapping blocks of a certain size (Figure 4, right). Then, each block is classified by majority voting.

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Fast and Hybrid Image Segmentation Based on Level set and Normalized Graph Cut

Fast and Hybrid Image Segmentation Based on Level set and Normalized Graph Cut

Image segmentation which is based on graph cut method is a binary segment, i.e. the image is divided into foreground and background. In the beginning, our approach combines level set algorithm with graph cut theory which is used to make binary segmentation for gray images. However, this method is not good for color image segmentation. During this process, we are going to improve this method through multiple iteration, so the extended approach not only deals with binary segmentation, but also handle multi-value segmentation, which suits more for gray or color image .Our main goalis to build a new graphic model, and extend the existing binarysegmentation method using normalized graph cut and level set algorithm to solve the multi-value segmentationproblems which can deal with both gray and color image. It will be discussed in below sessions. Experiment results say that our proposed methods conduct a better segmentation compared to existing binary segmentation methods.
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An Enchanced Area Optimized Sobel Edge Detection

An Enchanced Area Optimized Sobel Edge Detection

The Propose of Sobel edge detection algorithm is described in VHDL. Fig. 6 depicts edge detected image using XSG, Design and testing of individual module has been carried out. the final output consists of only basic components of the image Fig 7 depicts the simulation results when there is no edge in the image and Fig 8 depicts simulation results when the edges present in the image. Fig 9, 10 depicts the simulation results of binary segmentation module as gradient value is compared with the user defined threshold value. Fig. 11, 12 shows RTL and technological schematic of top module. Table 1,2 shows design of Xilinx and XSG.
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Comparative Analysis of Chromosomal Variation Altered New Region for Detecting Biomarker in Liver Cancer

Comparative Analysis of Chromosomal Variation Altered New Region for Detecting Biomarker in Liver Cancer

A lot of automated algorithms to detect biomarkers in CNAs have a solid restriction it is the noise generated from signals [16]. To fill this obstacle, Circular Binary segmentation (CBS) and Discrete Stationary Wavelet transforms (DSWT) are utilized, that aids in the perception of the genomic basis of the disease progression and improve a method to find biomarkers for early identification of liver cancer.

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A double cascaded framework based on 3D SEAU-Net for kidney and kidney tumor Segmentation

A double cascaded framework based on 3D SEAU-Net for kidney and kidney tumor Segmentation

Baseline Network For a 3D convolutional neural network, it is necessary to take the balance between model complexity and memory consumption into ac- count. Generally speaking, the deeper or wider a CNN model, the performance of the model becomes more better. However, due to memory limitations, it may not be the best way to increase performance [22]. Thus, some researches have focused on improving the performance of the model via some mechanisms, such as residual block [6], squeeze-and-excitation network (SENet) [8] and attention- aware [21, 18, 5]. Based on that, we attempt to find a good compromise between the depth and width. Afterwards, we develop a 3D convolutional neural network, namely 3D SEAU-Net shown in Fig. 2, for our each binary segmentation task. Our proposed network is inspired by a typical 2D U-Net architecture [17] and we go through minor modifications that the max-pooling operation is replaced by a convolutional block with stride of 2 in each down-sampling and the dilation rate is accordingly increased in all convolution layers of network. In addition, we extend the previous 2D SENet [8] and 2D residual attention mechanism [21] into 3D structure, and embed them to improve representation ability of our baseline network. One important modification in our architecture is that
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TEXTURE SEGMENTATION APPROACH BASED ON ENTROPY BASED LOCAL BINARY PATTERN OPERATOR

TEXTURE SEGMENTATION APPROACH BASED ON ENTROPY BASED LOCAL BINARY PATTERN OPERATOR

The first-order statistics estimate properties like the average and variance of individual pixel values, ignoring the spatial interaction between image pixels, second- and higher-order statistics on the other hand estimate properties of two or more pixel values occurring at specific locations relative to each other. Co-occurrence features and gray level differences (Weszka et al., 1976) are the most widely used statistical methods, which inspired a variety of modifications later on (Ojala and Pietikyinen, 2004) including signed differences (Ojala et al., 2001) and the Local Binary Pattern (LBP) operator (Ojala et al., 1996). LBP operator combines statistical and structural approaches to texture analysis by incorporating occurrence statistics of simple local microstructures. Autocorrelation function, which has been used for analyzing the regularity and coarseness of texture (Kaizer, 1955; Emerson et al., 1999; Lam et al., 2002; Al-Hamdan, 2004), and gray level run lengths (Galloway, 1975) are examples of other statistical approaches (Ojala and Pietikyinen, 2004). . The method involves weighting each pixel by the surrounding entropy such that each element in the final image is in high relief but lacks abrupt contrast changes due to the different light sources that might introduce spurious lines and other artifacts. The method also allows for fast addition and removal of images from the collective images (A. German, M. R. Jenkin, and Y. Lesp´erance, 2005).
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Survey  An exploration of various techniques for sign detection in 
		traffic panels

Survey An exploration of various techniques for sign detection in traffic panels

The shift descriptor is used to recognise the symbol, numbers and single characters [5] and the HMM is used to find the whole word. The different elements in the panel is separated by vertical edges and horizontal edges of the image and it leads to extract the foreground objects correctly. The same authors [27] used blue and white color masks for the segmentation and to detect a traffic panel BOVW approach is applied on each frame. The comparison of different descriptor is shown and the three descriptor namely SIFT, Hue Histogram and TCH achieve better panel detection for different panels. The SVM and Naive Bayes are compared to choose for the better classification and the Naive Bayes computation time is less than SVM. The system choose the TCH for recognise the symbol and the Naive Bayes for classification.
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Histological Quantification in Temporal Lobe Epilepsy

Histological Quantification in Temporal Lobe Epilepsy

The validation study performed to verify the accuracy and reproducibility generated satisfactory results in locating neurons within a histology slide. The DSC value is a simple and useful summary measure of spatial overlap, which can be applied to studies of reproducibility and accuracy in image segmentation. In comparing manual segmentations between two raters, the DSC value was greater than 0.7 in all cases, indicating a good overlap. We observed satisfactory results when comparing the spatial overlap between manual and automated segmentations, with only one instance of a slide having a DSC > 0.7 [20]. This indicates firstly that the automated segmentation performs well compared with the manual annotations but also that the protocol used for manual annotations is reproducible and provides a accurate way to delineate neurons within a slide.
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Binary Partition Tree as an Efficient Representation for Image Processing, Segmentation, and Information Retrieval

Binary Partition Tree as an Efficient Representation for Image Processing, Segmentation, and Information Retrieval

straightforward since the descendants of a node to be removed have not necessarily to be removed. An example of such criteri- on is the region perimeter. Fig. 16 illustrates this case. If we fol- low either Path A or Path B in Fig. 16, we see that there are some oscillations of the remove/preserve decisions. In practice, the non-increasingness of the criterion implies a lack of robustness of the operator. For example, similar images may produce quite different results or small modifications of the criterion thresh- old involve drastic changes on the output. In [16], a similar issue is discussed in the context of Max-tree representation for anti-extensive connected operators. The proposed solution con- sists in applying a transformation on the set of decisions. The transformation should create a set of increasing decisions while preserving as much as possible the decisions defined by the cri- terion. This problem may be viewed as dynamic programming issue that can be efficiently solved with a Viterbi algorithm. A similar solution is used here for binary partition trees.
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Machine Visual Location Method Research in Robot Unstacking

Machine Visual Location Method Research in Robot Unstacking

Abstract. Work pieces’ location based on machine visual in robot unstacking job has been researched. Adaptive threshold segmentation and modified Hough Transform are used to extract the target region needs further processing. A new method is proposed to choose the appropriate threshold to binary different images with different brightness. Watershed algorithm and Morphology algorithm are used to detect and connect the edges of work pieces. With the distance sensor and camera calibration to complete the location of pallet and work pieces, guiding robot to finish unstacking job successfully.
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Recognition of Printed and Handwritten Kannada Characters using SVM Classifier

Recognition of Printed and Handwritten Kannada Characters using SVM Classifier

Step 1: Pre-processing of the image: An image is considered and it is converted from RGB - color image into gray scale image. Then median filter-2 is used to filter the image then it is converted to binary image of 0's and 1's form using thresholding. Then BWareaopen is used to remove noise from the binirized image and after completion, the image is passed for segmenting the image.

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A Robust Rapid Approach to Image Segmentation with Optimal Thresholding and Watershed Transform

A Robust Rapid Approach to Image Segmentation with Optimal Thresholding and Watershed Transform

Due to advent of various techniques, image processing has been an attractive topic amongst enthusiasts. But it is equally challenging as an image represents various shapes, colors, tonal gradations, intensities and this information should be preserved in processing. It largely depends on features extracted, their uniqueness and their correctness. Though the features can vary from image to image, there are few common features, such as edge or boundary between object and background. Segmentation is one useful method for processing such images.
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Plasmodium life cycle stage classification based quantification of malaria parasitaemia in thin blood smears.

Plasmodium life cycle stage classification based quantification of malaria parasitaemia in thin blood smears.

Moreover, Khan et al., (2011) also counted the number of infected RBCs based on the number of parasites which is unacceptable in medical cases,(DPDx, 2013). Sio et al., (2007) stated that the infected RBC will be counted one, regardless of the number of parasites in it. Accordingly, Sio et al (2007) addressed the problems of clumped and overlapped RBCs by using the method developed by Kumar et al., (2006); however, in dense clumps of RBCs, the method affects the accuracy. The segmentation of RBCs based on nucleic approach exposes the problem, as RBCs have no nuclei and the studies consider the parasites as nuclei. The studies based on nucleic approach are seriously affected when the RBCs become really nucleated, such as when the RBCs lifespan is near the end or the RBCs are highly matured. The nucleic approach is followed by Kumarasamy et al., (2011); Khawaldeh (2013); Zoh et al., (2010) in the segmentation of RBCs. The segmentation based on chromatin dots offers no surety that on the basis of maximum and minimum intensity levels that they will be the same in all images and in addition, these studies are highly susceptible to noise (Saba et al., 2014; Rehman et al., 2011). On the same grounds, Somasekar, (2011); Makkapati and Rao, (2009) addressed the segmentation of the parasites. However, single RBCs can also have noisy chromatin dots, single dots aren't taken into consideration with the aid of experts as parasites, fake consequences might be pronounced and accuracy might be under threat (Saba, 2017; Saba et al., 2018a,b, Sadad et al., 2018).
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Unveiling the invisible: mathematical methods for restoring and interpreting illuminated manuscripts

Unveiling the invisible: mathematical methods for restoring and interpreting illuminated manuscripts

Despite being very popular and widely used in applica- tions, the Chan-Vese model and its extensions present intrinsic limitations. Firstly, the segmentation result is strongly dependent on the initialisation: in order to get a good result, the initial condition needs to be chosen within (or sufficiently close to) the domain one aims to segment. Secondly, due to the modelling assumption (1), the Chan-Vese model works well for images whose inten- sity is locally homogeneous. If this is not the case, the contour curve C may evolve along image information dif- ferent from the one we want to detect. Images with sig- nificant presence of texture, for instance, can exhibit such problems. Furthermore, the model is very sensitive to the length and area parameters µ and ν , which may make the segmentation of very small objects in the image difficult.
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Contributions à la calibration d'algorithmes d'apprentissage : Validation-croisée et détection de ruptures

Contributions à la calibration d'algorithmes d'apprentissage : Validation-croisée et détection de ruptures

Regression shows a similar picture. For LpO, non-asymptotic expressions of the bias were proved by Celisse (2008) for projection and kernel estimators, and by Arlot and Celisse (2011a) for regressograms when the design is fixed. More recently Vidoni (2015) has derived an asymptotic quantification of the bias of the LpO estimator with maximum likelihood estimators. For V-FCV and RLT, an asymptotic expansion of the bias was yielded by Burman (1989) for least squares in linear regression, and extended to spline smoothing (Burman, 1990). Note that Efron (1986) proved non-asymptotic analytic expressions of the expectations of the L1O and GCV estimators in regression with binary data (see also Efron, 1983). Classification. For discriminating between two populations with shifted distributions, Davison and Hall (1992) compared the asymptotical bias of L1O and bootstrap. L1O is less biased when the shift size is n −1/2 : As n tends to infinity, the bias of L1O stays of order n −1 , whereas that of bootstrap worsens to the order n −1/2 . On synthetic and real data, Molinaro et al. (2005) compared the bias of L1O, V-FCV and .632+ bootstrap: The bias decreases with n − p , and is generally minimal for L1O. Nevertheless, the 10-fold CV bias is nearly minimal uniformly over their experiments. Furthermore, .632+ bootstrap exhibits the smallest bias for moderate sample sizes and small signal-to-noise ratios, but a much larger bias otherwise. In binary classification, Celisse and Mary-Huard (2015) has derived an upper bound on the bias of the LpO estimator for the k-nearest neighbor classification rule (for 1 ≤ k ≤ n − 1) with the {0, 1}-loss.
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Lung Cancer Detection using Modified Gabor filter, Gradient operators and Morphological segmentation tool

Lung Cancer Detection using Modified Gabor filter, Gradient operators and Morphological segmentation tool

By using the MGF, gradient operators and morphological segmentation tool an efficient lung cancer detection system is developed. The modifications in Gabor filter (MGF) include the consideration of spatial aspect ratio at the kernel size directly instead of at the initial stage. This modification results in reduction of distortion of the image at the beginning and helps to obtain clear images at the initial stage. Applying dual tree CWT on the gradient operators for extracting features it has many advantages like smoothing effect to remove noise present in the images through non- maximal suppression and improves the PSNR. Here real part and imaginary parts of the image are added to get the total gradient image. For the image segmentation watershed transform is used i.e. texture watershed, in this stage maxima is calculated (i.e. lowest deepest point) to apply watershed. Finally the segmented image is superimposing with the original image, depending on the segmented image the presence of lung cancer is identified. For analysis a normal X-ray lung image and abnormal lung image is selected from the database and results are found to be fruitful. The presence of the cancer cells is decided by the next stage by using neural networks. Hence, this proposed method of lung cancer detection using MGF, gradient operators and morphological segmentation tool helps in early detection of cancerous cells present in lungs.
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A SEGMENTATION PROBLEM IN QUANTITATIVE ASSESSMENT OF ORGAN DISPOSITION IN RADIOTHERAPY

A SEGMENTATION PROBLEM IN QUANTITATIVE ASSESSMENT OF ORGAN DISPOSITION IN RADIOTHERAPY

In order to identify the boundary of the bladder (or the region of the organ of interest) in each 2D slice, and to perform image segmentation we consider a curve evolution approach. In particular, we use a level set based, or implicit active contour, approach which is a PDE-based techniques (Osher and Sethian, 1988; Sethian, 2003; Osher and Paragios, 2003). In level set methods, a contour (or more generally a hypersurface) of interest is embedded as the zero level set of a level set function (LSF). Then the contour is moved by suitable image driven forces to the boundaries of the desired objects. In Fig. 4 we show an example of curve evolution, the black line is the starting contour, while the blue line is the final contour (the boundary of the object). Other lines in figure other represent intermediate stages of the contour evolution.
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Automated Pulmonary Lung Nodule Detection using an Optimal Manifold Statistical Based Feature Descriptor and SVM Classifier

Automated Pulmonary Lung Nodule Detection using an Optimal Manifold Statistical Based Feature Descriptor and SVM Classifier

The binary image is partitioned into four regionsin Fig.3 (i.e. A P Yx P xY , B P XY P YX , C P yX P Xy, and D P xy P yx ) by employing these extreme points. Only these extreme points are processed while finding the vertex. By orderly monotone scanning increase then temporary convexity is extracted. The entire convexity is obtained by continuously improving the momentary convexity. Theseconvex hull algorithm processes less storage space and time for scanned areas are less and only the vertices of temporary convexity require storage. After applying improved convex hull algorithm, the coarse segmented lung image is subtracted from the result of modified convex hull algorithm. As the resultant image contains some small responses and objects at the border, morphological erosion with a spherical kernel of size seven and connected label filtering are then applied to remove these responses. The eroded image is subtracted from the result of the modified convex hull algorithm to extract the final lung region. Fig. 2 (e) shows the extracted binary mask of lung and it can be observed that the missing juxta-pleural nodules are added to the segmented lung. Finally, the extracted lung region is presented in Fig. 2 (f). Candidate Nodule Detection
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LINKED OPEN GOVERNMENT DATA AS BACKGROUND KNOWLEDGE IN PREDICTING FOREST FIRE

LINKED OPEN GOVERNMENT DATA AS BACKGROUND KNOWLEDGE IN PREDICTING FOREST FIRE

A number of segmentation algorithms have been proposed for image coding till date, each claiming to be different or superior in some way. The first segmentation-based coding methods were suggested in the early 1980s [11]. These algorithms partition the image into complex geometric regions using a contour-texture coding method (1982) [15] over which it is approximated using low-order polynomials. One of the most popular segmentation based coding schemes investigated by researchers in the early days were the Quadtree-based image compression (1991) [16], which recursively divides the image signal into simpler geometric regions. Many variations of the ‘Second Generation’ coding schemes have since been announced that exploit the geometry of curve singularities of an image [17], [18], [19]. In one of the outstanding ‘Second Generation’ methods, Froment and Mallat (1992) constructed multi-scale wavelet-like edge detectors and showed how a function from the responses of a sparse collection of these detectors can be reconstructed [20]. They reported good coding results at low bit-rates. Cand`es and Donoho (2001) constructed, a bivariate transform called Curvelets intended to capture local multi-scale directional information [21]. Cohen and Matei (2001) also presented a discrete construction of an edge- adapted transform [22] which is closely related to nonlinear Lifting (2003) [23]. In a later work (2003), the authors enhance classical wavelet coding by detecting and coding the strong edges separately and then using wavelets to code a residual image [4]. Do and Vetterli’s construction of Contourlets (2005) [24], is similar but is a purely discrete construction. Coding algorithms that are geometric enhancements of existing wavelet transform based methods, where wavelet coefficients are coded using geometric context modelling also exist [25]. But all of these constructions are redundant, i.e., the output of the discrete transform implementations produces more coefficients than the original input data. Research on the possibility of using these new transforms to outperform wavelet based coding is still on-going.
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