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Diabetic macular edema (DME) is a typical fundus disease that can cause blindness in severe cases. The morphology of the inner limiting membrane (ILM) to the retinal pigment epithelium (RPE) layer in the retina and the macular edema (ME) area are important features for the diagnosis of DME. Doctors usually use non‐invasive and high‐resolution optical coherence tomography (OCT) to examine the fundus...
We propose a simple, efficient and effective method using deep convolutional activation features (CNNs) to achieve stat- of-the-art classification and segmentation for the MICCAI 2014 Brain Tumor Digital Pathology Challenge. Common traits of such medical image challenges are characterized by large image dimensions (up to the gigabyte size of an image), a limited amount of training data, and significant...
Image processing bears some fuzziness in nature, as a effective mathematical tool for handling the ambiguity, Fuzzy set theory is introduced in the paper to define a new kind of fuzzy entropy, namely two-dimension fuzzy Tsallis entropy (TFTE) and applied in image segmentation following the maximum entropy principle. To overcome the huge calculational burden when generalizing one-dimension entropy...
This paper proposes an image enhancement algorithm based on fuzzy theory. The average of all adjacent points is as enhancement threshold and affiliation of each point is obtained by counting the average. Then, enhancement images are obtained with inverse transformation. Experiments have proved that contrast of vein and background is enhanced and effect of image segmentation and extraction is improved...
An image compression method based on adaptive segment and adaptive quantified is presented in this paper. The image is divided into smooth and detail areas using the multi-threshold-segmentation method based on the potential function. The quantization table varied with the different image. The 2-D DCT is decomposed into 1-D fast DCT based on base-2 FFT. The Compression-to-PSNR Rate can evaluate the...
In this paper, a novel view-based 3D object recognition method is proposed, which consists of three steps. First, employing wavelet transform to decompose view images of the object into different frequency sub-images. Second, for each sub-image, the features are extracted using singular-value decomposition (SVD) approach, and the features extracted from sub-images are combined to construct the feature...
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