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This paper presents Magnetic Resonance Imaging (MRI) brain tumor detection utilizing Fuzzy C Means strategy with an upgraded noise filtering calculation. A novel technique is proposed to enhance the execution of cerebrum tumor dis...
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This paper presents Magnetic Resonance Imaging (MRI) brain tumor detection utilizing Fuzzy C Means strategy with an upgraded noise filtering calculation. A novel technique is proposed to enhance the execution of cerebrum tumor discovery. A new calculation for noise filtering is adapted to extract the correct area of tumor, where execution is enhanced by upgrading the threshold task in wavelet filtering strategy as a preprocessing step. Trial results demonstrate that by utilizing proposed calculation, the filtering procedure gives better execution when contrasted with the current methods. The average value of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) for Gaussian noise is improved by 40% and 41.06% and for Rician noise, which is 13.73% and 25.39% higher than the state-of-art methods. After filtering, segmentation is done to point out the tumor region. For segmentation, Otsu and FCM methods are adapted here and a comparison is made between these two methods. Experimental results show that Jaccard and Dice coefficient of Fuzzy C Means (FCM) with enhanced filtering is increased by 3.6% and 1.3% compared to the methods available in the literature.
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In this paper, a coarse-to-fine hierarchical classification method based on the features derived from adaptive cellular color decomposition is proposed. The proposed method is general and can be applied to all kinds of color image...
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In this paper, a coarse-to-fine hierarchical classification method based on the features derived from adaptive cellular color decomposition is proposed. The proposed method is general and can be applied to all kinds of color image databases as long as a sample set of images have been classified. In addition, the number of classes can be as versatile as required. To achieve the goal mentioned above, our method consists of two phases: color quantization and classification. In the color quantization step, cellular decomposition is used to adaptively quantize color images in the HSV color space since H and S components construct a hexagon structure that is same as the cellular pattern. In the classification step, a coarse-to-fine strategy is employed. In the coarse stage, five image-based features extracted directly from the quantization results of the query images are used to prune irrelevant database images. In the fine stage, two cluster-based features are extracted from a small set of candidate images using closest-cluster matching. On the other hand, according to feature evaluation, one image-based and two cluster-based features are selected to derive individual-based similarity measure, which, in turn, is used to measure image-to-image similarity. In addition, class-based similarity measure using class characteristics is proposed to evaluate image-to-class similarity. Candidate images are then sorted according to the similarity measure, which is a combination of individual-based and class-based similarity measures. Finally, k-NN rule is used to assign the query image to a single class according to the sorting results. The effectiveness and practicability of the proposed method have been demonstrated by various experimental results.
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In this paper, we present a new full-reference objective image quality measure—IQM2, based on structural similarity index and steerable pyramid wavelet transform. IQM2 is tested using different number of orientation kernels and s...
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In this paper, we present a new full-reference objective image quality measure—IQM2, based on structural similarity index and steerable pyramid wavelet transform. IQM2 is tested using different number of orientation kernels and seven subjective databases. Finally, IQM2 measure is compared with twelve commonly used full-reference objective measures. Results show that proposed IQM2 measure, using kernel with 2 orientations, provides good correlation with the results of subjective evaluation while keeping computational time lower than other similar performing objective measures.
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Glaucoma is one of the leading causes of vision loss worldwide. It leads to reduced quality of life for individuals and substantial economic loss for society. This problem can be reduced by the early and reliable diagnosis of glau...
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Glaucoma is one of the leading causes of vision loss worldwide. It leads to reduced quality of life for individuals and substantial economic loss for society. This problem can be reduced by the early and reliable diagnosis of glaucoma. The traditional instrument-based methods are nonautomated and laborious. Recently, many computer-based approaches have been proposed for glaucoma detection. However, none of the existing approaches can be efficiently used for the classification of glaucoma stages. In this study, we proposed a novel method to classify the glaucoma stages (healthy, early-stage, and advanced-stage) using a 2-D compact variational mode decomposition (2-D-C-VMD) algorithm. In this work, the preprocessed input images are first decomposed into several variational modes (VMs) employing 2-D-C-VMD. Next, various features, namely, Kapur entropy (KE), Renyi entropy (RE), Shannon entropy (SE), Yager entropy (YE), energy (En), and fractal dimension (FD) features, which are extracted from the first VM. Then, linear discriminant analysis (LDA) has been used for dimensionality reduction. Finally, a trained multiclass least-squares-support vector machine (MC-LS-SVM) classifier has been utilized for classification purpose. The proposed approach has been tested on two different public glaucoma database. Our method achieved the highest classification accuracy of 98.11% with tenfold cross-validation. The experimental results show that the proposed approach performed far better as compared to state-of-the-art approaches.
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Picture archiving and communications systems (PACS) and the complications of database design and communications constraints are described. Both relational and object-oriented database approaches are examined, as are the centralize...
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Picture archiving and communications systems (PACS) and the complications of database design and communications constraints are described. Both relational and object-oriented database approaches are examined, as are the centralized and distributed approaches. Data retrieval techniques, image compression, storage media and communication are discussed.
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Finding point-wise correspondences between images is a long-standing problem in image analysis. This becomes particularly challenging for sketch images, due to the varying nature of human drawing style, projection distortions and ...
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Finding point-wise correspondences between images is a long-standing problem in image analysis. This becomes particularly challenging for sketch images, due to the varying nature of human drawing style, projection distortions and viewport changes. In this paper, we present the first attempt to obtain a learned descriptor for dense registration in line drawings. Based on recent deep learning techniques for corresponding photographs, we designed descriptors to locally match image pairs where the object of interest belongs to the same semantic category, yet still differ drastically in shape, form, and projection angle. To this end, we have specifically crafted a data set of synthetic sketches using non-photorealistic rendering over a large collection of part-based registered 3D models. After training, a neural network generates descriptors for every pixel in an input image, which are shown togeneralize correctly in unseen sketches hand-drawn by humans. We evaluate our method against a baseline of correspondences data collected from expert designers, in addition to comparisons with other descriptors that have been proven effective in sketches. Code, data and further resources will be publicly released by the time of publication.
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In this paper, we propose an Image Decomposition Sensor based on Conditional Adversarial Model (CAM). The presented device decomposes intrinsic image into reflectance layer and shadow layer and the Conditional Adversarial Model (C...
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In this paper, we propose an Image Decomposition Sensor based on Conditional Adversarial Model (CAM). The presented device decomposes intrinsic image into reflectance layer and shadow layer and the Conditional Adversarial Model (CAM) trains the intrinsic image decomposition to obtain high quality reflectance image and shadow image. By enhancing the shadow image, we can get a larger range of contrast images. Besides, we adopt reduced lookup table based pipeline architecture to greatly reduce the overall computing time. The image decomposition device solves the problems such as limited contrast and color distortion and is used for application of image contrast enhancement. The sensor circuit provide excellent auxiliary effects for system that need to rely on high-speed image processing.
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One of the fundamental problems in Content-Based Image Retrieval (CBIR) has been the gap been low level visual features and high level semantic concepts. To narrow down this gap, relevance feedback is introduced into image retriev...
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One of the fundamental problems in Content-Based Image Retrieval (CBIR) has been the gap been low level visual features and high level semantic concepts. To narrow down this gap, relevance feedback is introduced into image retrieval. With the user provided information, a classifier can be learned to discriminate between positive and negative examples. However, in real world applications, the number of user feedbacks is usually too small comparing to the dimensionality of the image space. Thus, a situation of overfitting may occur. In order to cope with the high dimensionality, we propose a novel supervised method for dimensionality reduction called Maximum Margin Projection (MMP). MMP aims to maximize the margin between positive and negative examples at each local neighborhood. Different from traditional dimensionality reduction algorithms such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the global Euclidean structure, MMP is designed for discovering the local manifold structure. Therefore, MMP is likely to be more suitable for image retrieval where nearest neighbor search is usually involved. After projecting the images into a lower dimensional subspace, the relevant images get closer to the query image, thus the retrieval performance can be enhanced. The experimental results on a large image database demonstrates the effectiveness and efficiency of our proposed algorithm.
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Photosketcher is an interactive system for progressively synthesizing novel images using only sparse user sketches as input. Photosketcher works on the image content exclusively; it doesn't require keywords or other metadata assoc...
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Photosketcher is an interactive system for progressively synthesizing novel images using only sparse user sketches as input. Photosketcher works on the image content exclusively; it doesn't require keywords or other metadata associated with the images. Users sketch the rough shape of a desired image part, and Photosketcher searches a large collection of images for it. The search is based on a bag-of-features approach that uses local descriptors for translation-invariant retrieval of image parts. Composition is based on user scribbles: from the scribbles, Photosketcher predicts the desired part using Gaussian mixture models and computes an optimal seam using graph cuts. To further reduce visible seams, users can blend the composite image in the gradient domain.
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In objective image quality metrics, one of the most important factors is the correlation of their results with the perceived quality measurements. In this paper, a new method is presented based on comparing between the structural ...
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In objective image quality metrics, one of the most important factors is the correlation of their results with the perceived quality measurements. In this paper, a new method is presented based on comparing between the structural properties of the two compared images. Based on the mathematical concept of the singular value decomposition (SVD) theorem, each matrix can be factorized to the products of three matrices, one of them related to the luminance value while the two others show the structural content information of the image. A new method to quantify the quality of images is proposed based on the projected coefficients and the left singular vector matrix of the disturbed image based on the right singular vector matrix of the original image. To evaluate this performance, many tests have been done using a widespread subjective study involving 779 images of the Live Image Quality Assessment Database, Release 2005. The objective results show a high rate of correlation with subjective quality measurements.
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