This paper introduces a proposed method based on a backpropagation artificial neural network using Scaled Conjugate Gradient (SCG) training algorithm so as to gain the edges of any image. A new training image model is suggested to train this artificial neural network, then using this network to find the edges of any image. Computer experiments are carried out for extracting edge information fro...

Image segmentation plays a crucial role in many medical imaging applications and is an important but inherently difficult problem. This paper discusses the method that classifies unsupervised image using a Kohonen self-organizing map neural network. This method has two problems: training time of the network is too long and the classified result and quantity are much easily influenced by the noi...

We compare a set of convolutional neural network (CNN) architectures for the task of segmenting and detecting human sperm cells in an image taken from a semen sample. In contrast to previous work, samples are not stained or washed to allow for full sperm quality analysis, making analysis harder due to clutter. Our results indicate that training on full images is superior to training on patches ...

We propose a smooth formulation of multiple-point statistics that enables us to solve inverse problems using gradient-based optimization techniques. We introduce a differentiable function that quantifies the mismatch between multiple-point statistics of a training image and of a given model. We show that, by minimizing this function, any continuous image can be gradually transformed into an ima...

The detection and classification of anomalies relevant for disease diagnosis or treatment monitoring is important during computational medical image analysis. Often, obtaining sufficient annotated training data to represent natural variability well is unfeasible. At the same time, data is frequently collected across multiple sites with heterogeneous medical imaging equipment. In this paper we p...

Deep metrics have been shown effective as similarity measures in multi-modal image registration; however, the metrics are currently constructed from aligned image pairs in the training data. In this paper, we propose a strategy for learning such metrics from roughly aligned training data. Symmetrizing the data corrects bias in the metric that results from misalignment in the data (at the expens...

During the intraoperative radiograph generation process with mobile image intensifier systems (C-arm) most of the radiation exposure for patient, surgeon and operation room personal is caused by scattered radiation. The intensity and propagation of scattered radiation depend on different parameters, e.g. the intensity of the primary radiation, and the positioning of the mobile image intensifier...

Single image super-resolution (SR) via deep learning has recently gained significant attention in the literature. Convolutional neural networks (CNNs) are typically learned to represent the mapping between low-resolution (LR) and highresolution (HR) images/patches with the help of training examples. Most existing deep networks for SR produce high quality results when training data is abundant. ...

Usually, the low-level representation of images is unsatisfied for image classification due to the well-known semantic gap, and further hinders its application for high-level visual applications. To deal with these problems, in this paper, we propose a simple but effective image representation for image classification, which is denoted as the responses to a set of exemplar image classifiers. Ea...

Automatic target detection is of great importance in high-resolution synthetic aperture radar (SAR) images processing. In this paper, we proposed a hybrid HMMTSVM model to detect targets in SAR images. Our proposed SAR image target detection system is made up of three steps. In this first step, the testing/training SAR images are preprocessed, and image visual features are extracted through 2DP...