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Fluorescence microscopy image segmentation is a central task in high-throughput applications such as protein expression quantification and cell function investigation. In this paper, a multiple kernel local level set segmentation algorithm is introduced as a framework for fluorescence microscopy cell image segmentation. In this framework, a new local region-based active contour model in a variational...
Image denoising is a classical linear inverse prob- lem with applications in remote sensing, medical imaging, astronomy and surveillance. This article addresses the image denoising problem using a non-local noise estimation based on the spatial redundancy offered by natural images. A low dimensional signal subspace is estimated using the statisti- cal strength of singular value decomposition (SVD),...
Hashing algorithm is an efficient approximate searching algorithm for large-scale image retrieval. Learning binary code is a key step to improve its performance and it is still an ongoing challenge. The inputs of Hashing affects its performance. This paper proposes a method to improve the efficiency of learning binary code by improving the suitableness of the Hashing algorithms inputs by employing...
We propose a new, hierarchical, aggregation-based deep neural network to learn aging features from facial images. Our deep-aging feature vector is designed to capture both local and global aging cues from facial images. A Convolutional Neural Network (CNN) is employed to extract region- specific features at the lowest level of our hierarchy. These features are then hierarchically aggregated to consecutive...
A Bilateral filter is basically an edge-preserving and smoothing, non-linear filter. It consists of two kernels, namely spatial and range kernels which can be constant or arbitrary. Algorithms for bilateral filtering with constant time computational complexity are present today, but their execution time is too high for real time applications. Also, hardware latency and throughput sometimes reduce...
The accurate segmentation of biomedical images has become increasingly important for recognizing cells that have the phenotype of interest in biomedical applications. In order to improve the conventional deterministic segmentation models, this paper proposes a novel graph-cut cell image segmentation algorithm based on Bayes theorem. There are two segmentation phases in this method. The first phase...
Some of the most difficult image segmentation problems involve an unknown number of object instances that can touch or overlap in the image, e.g. microscopy imaging of cells in biology. In an important set of cases, the nature of the objects and the imaging process mean that when objects overlap, the resulting image is approximately given by the sum of intensities of individual objects; and, in addition,...
Fluorescence microscopy image segmentation is a challenging task in fluorescence microscopy image analysis and high-throughput applications such as protein expression quantification and cell function investigation. In this paper, a novel local level set segmentation algorithm in a variational level set formulation via a correntropy-based k-means clustering (LLCK) is introduced for fluorescence microscopy...
Image processing and analysis is a useful tool for monitoring of activated sludge wastewater treatment plant. However its effectiveness is dependent on performance of the segmentation algorithms. The activated sludge plant is monitored by image processing and analysis of images acquired through trinocular microscope. The sample observed under microscope is collected from aeration tank of the plant...
In this paper we present a novel low-bitrate 3D model compression algorithm called the Plane-Tree. This algorithm is based on octree subdivision and stores a plane at each leaf node which better approximates the surface within the node. Quantitative evaluations show that this method is competitive with state of the art transform based methods and outperforms them at low-bitrates.
Anti-nuclear antibody (ANA) indirect immunofluoresence (IIF) human epithelial type-2 (HEp-2) testing provides important clinical information for the diagnosis of systemic autoimmune rheumatic diseases (SARD). Recent developments in computer aided diagnosis (CAD) systems aim to improve the reliability and reproducibility of ANA IIF laboratory testing by providing ANA classification. The limited prior...
The importance of pre-clinical research of single photon emission computed tomography(SPECT) imaging is now widely recognized. For the demand of high resolution and high detection efficiency, SPECT has been developed in several ways and it is simultaneously making demand for high quality imaging. This paper introduces the newly proposed dual-head L-SPECT system and investigates the initial performance...
Many different image stitching algorithms, and mechanisms to assess their quality have been proposed by different research groups in the past decade. However, a comparison across different stitching algorithms and evaluation mechanisms has not been performed before. Our objective is to recognize the best algorithm for panoramic image stitching. We measure the robustness of different algorithms by...
Multi-focus image fusion is an important technique that extracts sharpness regions from multiple images and composites them into a fully focused image. In this paper, a novel spatial domain based fusion algorithm for multi-focus image through gradient based decision map construction using morphology and active contour model is proposed. Firstly, the original focus maps are constructed based on the...
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