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According to the statistics, melanoma accounts for just 11 % of all types of skin cancer, it is responsible for most of the deaths. Melanoma is visually difficult for clinicians to differentiate from Clark nevus lesions which are benign. The application of image processing techniques to these lesions may be useful as an educational tool for teaching physicians to differentiate lesions, as well as...
We introduce a new representation of cortical regions via distribution functions of their features. The distribution functions are estimated non-parametrically from the data and are observed to be non Gaussian. Cortical pattern matching is enabled by using the information-based Jensen-Shannon divergence as a measure between features. Our approach explicitly avoids pairwise registrations between brains,...
Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. As a result, machine learning is frequently used in cancer diagnosis and detection. In this paper, support vector machines, K-nearest...
This paper presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of the Alzheimer type dementia. The proposed methodology is based on the calculation of the skewness to each m-by-m sliding block of the transaxial slices of the SPECT brain images. We replace the center pixel in the m-by-m block by the skewness value and build a new 3-D brain image which will...
In this work we present a method for automated classification of endoscopic images according to the pit pattern classification scheme. Images taken during colonoscopy are transformed to the wavelet domain using the pyramidal discrete wavelet transform. Then, Gaussian Markov random fields are used to extract features from the resulting wavelet coefficients. Finally, these features are used for a classification...
A previously proposed approach based on RBF neural networks for detecting anomaly location is extended to estimate the anomaly size. First, a predefined number of threshold values are selected in the range of possible anomaly sizes. Next, RBF neural networks are used as classifiers to classify the anomaly size as being smaller or larger than each threshold value. The inputs of the classifiers are...
We propose a methodology for discriminating between various types of normal and diseased brain tissue in medical images that utilizes vector quantization (VQ), an image compression technique, to extract discriminative texture features. Rather than focusing on images of the entire brain, we direct our attention to extracting local descriptors for individual regions of interest (ROIs) as determined...
We present in this paper part of our work inside the ESCALE project dedicated to the design of a complete 3D and color wound assessment tool using a simple hand held digital camera. The computation of a 3D model for wound measurements using uncalibrated vision techniques has already been investigated in the project. This article presents our methodology to classify wound tissues in the color images,...
In this paper, we propose and evaluate methodologies for the classification of images from thin-layer chromatography. Each individual sample is characterized by an intensity profile that is further represented into a feature space. The first steps of this process aim at obtaining a robust estimate of the intensity profile by filtering noise, reducing the influence of background changes, and by fitting...
Statistical discrimination methods are suitable not only for classification but also for characterization of differences between a reference group of patterns and the population under investigation. In the last years, statistical methods have been proposed to classify and analyze morphological and anatomical structures of medical images. Most of these techniques work in high-dimensional spaces of...
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