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Segmentation of cell nuclei is an important step towards automatic analysis of microscopic images. This paper presents an automated technique for nuclear segmentation in skin histopathological images. The proposed technique first detects nuclear seeds using a bank of generalized Laplacian of Gaussian (gLoG) kernels. Based on the detected nuclear seeds, a multi-scale radial line scanning (mRLS) method...
In this manuscript we propose a novel method for jointly page stream segmentation and multi-page document classification.The end goal is to classify a stream of pages as belonging to different classes of documents. We take advantage of the recent state-of-the-art results achieved using deep architectures in related fields such as document image classification, and we adopt similar models to obtain...
Early detection of abnormality in image of skin is now considered the crucial contributor for successful treatment. We explored how constructing a sparse neighborhood net of pixels and distinction of patches in the image feature analysis play a role for diagnostic ability of lesion segmentation. Since image patches are considered like circumstance of many factors including skin tone, skin aberrations...
Spectral clustering is a suitable technique to deal with problems involving unlabeled clusters and having a complex structure, being kernel-based approaches the most recommended ones. This work aims at demonstrating the relationship between a widely-recommended method, so-named kernel spectral clustering (KSC) and other well-known approaches, namely normalized cut clustering and kernel k-means. Such...
Hypertension poses a serious atherosclerotic risk as it causes both macro- and micro circulation damage. Nailfold capillaroscopy is a valuable yet simple tool to assess microcirculation of blood capillaries. This technique is important in detecting early occurrences of scleroderma spectrum disorders and evaluating Raynaud's Phenomenon. Here it is used in detecting hypertension in patients. Current...
Because the contrast of the image for guiding the high-speed infrared air-to-air missile is low, its signal to noise ratio is poor and the target and its background gray-scale coupling is strong, the paper analyzes the reasons why the threshold value segmentation method and the fuzzy C-means clustering method have the over-segmentation and under-segmentation in segmenting the above type of image....
Glossoscopy is an important part of Traditional Chinese Medicine (TCM). To analyze the tongue properties objectively, we need extract the tongue region from images. This paper presents a method to segment the tongue images based on kernel FCM (Fuzzy Cluster means). Firstly we pre-processed the tongue images by gray-level integral projection. Secondly the features were extracted to form a feature vector...
Brain tumor segmentation, an essential but challenging task, has long attracted much attention from the medical imaging community. Recently, successful applications of sparse coding and dictionary learning has emerged in various vision problems including image segmentation. In this paper, a superpixel-based framework for automated brain tumor segmentation is introduced. The kernel trick is adopted...
Research on iris recognition have observed that iris texture has inherent radial correlation. However, currently, there lacks a deeper insight into iris textural correlation. Few research focus on a quantitative and comprehensive analysis on this correlation. In this paper, we perform a quantitative analysis on iris textural correlation. We employ steering kernels to model the textural correlation...
Edge detection is an active and critical topic in the field of image processing, and plays a vital role for some important applications such as image segmentation, pattern classification, object tracking etc. In this paper, an approach using varied local edge pattern descriptor is proposed for edge detection. This method contains the following steps: firstly, Gaussian filter is used to smooth the...
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Images with weak contrast, overlapped noise and texture of the object and background make many PDE based methods disabled. To address these problems, this paper presents a novel combined multi-scale variational framework level set segmentation model. Its level set formulation consists edge-based term, region-based term and shape constraint term. The edge-based term is constructed using a newly defined...
Classification of long duration speech, represented as varying length sets of feature vectors using support vector machine (SVM) requires a suitable kernel. In this paper we propose a novel segment-level pyramid match kernel (SLPMK) for the classification of varying length patterns of long duration speech represented as sets of feature vectors. This kernel is designed by partitioning the speech signal...
Automated segmentation of retinal blood vessels in label-free fundus images entails a pivotal role in computed aided diagnosis of ophthalmic pathologies, viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases. The challenge remains active in medical image analysis research due to varied distribution of blood vessels, which manifest variations in their dimensions of physical...
Fully automatic localization of lumbar vertebrae from clinical X-ray images is very challenging due to the variation of X-ray quality, scale, contrast, number of visible vertebrae, etc. To overcome these challenges, we present a novel framework, where we accelerate a scale-invariant object detection method using Support Vector Machines (SVM) trained on Histogram of Oriented Gradients (HOG) features...
A new algorithm for apple disease image segmentation is proposed. A fuzzy factor for weighted balance is introduced in the algorithm to describe the coefficient of spatial constraints between pixels in neighborhood. For enhancing the integrality of neighbor information, the space distance constraints and the spatial gray constraints are considered. The fuzzy factor in the neighborhood is used to keep...
Diagnosis of rice planthopper pests based on imaging technology is an efficient means to develop intelligent agriculture. Effective contour automation extraction is an important pretreatment technology at the early stage for identifying and classifying rice planthoppers. For the curtain as the background contained texture structures and resulted in a heterogeneous texture in the sensed image, which...
Separating the foreground objects from the complex background in a static image is one of the research hot spots in computer vision. Due to lack of motion information, most of the current approaches only explore local object cues in the segment-level which easily suffer from not only the view and illumination changes, but also deformation and occlusion. This paper proposed a new multi-class object...
The field of medical image analysis has grown and these advances have facilitated the development of processes for high-throughput extraction of quantitative features that result in the conversion of images into mineable data and the subsequent analysis of these data for decision support. This paper presents a simple yet efficient image processing approach by proposing a new image feature detector...
Fast and accurate segmentation of musculoskeletal ultrasound images is an on-going challenge. Two principal factors make this task difficult: firstly, the presence of speckle noise arising from the interference that accompanies all coherent imaging approaches; secondly, the sometimes subtle interaction between musculoskeletal components that leads to non-uniformity of the image intensity. Our work...
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