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With a wide array of multi-modal, multi-protocol, and multi-scale biomedical data available for disease diagnosis and prognosis, there is a need for quantitative tools to combine such varied channels of information, especially imaging and non-imaging data (e.g. spectroscopy, proteomics). The major problem in such quantitative data integration lies in reconciling the large spread in the range of dimensionalities...
This paper presents a feature-selection-based data fusion method to follow up the evolution of brain tumors under therapeutic treatments with multi-spectral MRI data sequences. The fusion of MRI data is proposed to use a feature selection method to choose the most important features to classify tumor tissues and non-tumor tissues. Our system consists of three steps for each MRI examination (one examination...
Fully automated high-throughput and high-content experiments need reusable general modules, that can be combined in a flexible way to build the solution. Even though the biological objects or structures may not share any common features, the transformations that act on the structures (like rotations, translations or deformations) are nearly the same in every experiment. In the talk I will show general...
Prostate cancer is considered to be one of the main causes of cancer related death for men in the United States. Automated methods for prostate cancer localization based on multispectral magnetic resonance imaging (MRI) haver recently emerged as a non invasive technique for this purpose as an alternative to transrectal ultrasound. However, the automated methods developed to this date require a manual...
Coronary angiography is routinely used to screen patients both prior to and during angioplasty. Each angiography study results in a collection of video sequences or “runs” that depict coronary arteries from different viewpoints. A key problem to be addressed in the automatic interpretation of coronary angiography videos is the identification of images depicting coronary arteries in these sequences...
This paper compares the performance of redundant representation and sparse coding against classical kernel methods for classifying histological sections. Sparse coding has been proven an effective technique for restoration, and has recently been extended to classification. The main issue with histology sections classification is inherent heterogeneity, which is a result of technical and biological...
The objective of this research is to develop algorithm to recognize black germ wheat based on image processing. The sample used for this study involved wheat from major producing areas of China. Images of wheat were acquired with a color linear CCD machine vision system. Each image was pre-processed to correct color offset. Then double-threshold method was used to segment black germ from background...
Automatic annotation of images is a challenging task in computer vision because of “semantic gap” between highlevel visual concepts and image appearances. Therefore, user tags attached to images can provide further information to bridge the gap, even though they are partially uninformative and misleading. In this work, we investigate multi-modal visual concept classification based on visual features...
The combination of local features, complementary feature types, and relative position information has been successfully applied to many object-class recognition tasks. Stacking is a common classification approach that combines the results from multiple classifiers, having the added benefit of allowing each classifier to handle a different feature space. However, the standard stacking method by its...
In this paper, a new classification scheme of fully polarimetric SAR images is proposed. This is based on the joint use of the Freeman-Durden decomposition and generalized discriminant analysis, a new method for Feature extraction. After getting the powers of the three scattering mechanism components through Freeman-Durden decomposition, the Feature extraction algorithm is introduced to well exploit...
Natural image classification is an important task. SIFT descriptors and bag-of-visterms (BOV) method have achieved very good results based on local image representation. Many studies use the support vector machine to classify and identify the image category after finished representation of the image. However, due to support vector machine (SVM) its own characteristics, it shows inflexible and less...
Based on multiple kernel learning (MKL) support vector machine and decision tree combined strategy, a multi-class classification method is proposed to classify lower limb motions using electromyography (EMG) signals. According to the framework of multiple kernel learning, the MKL-based multi-classifier is constructed using binary tree decomposition method. Four-channel surface EMG signals are firstly...
In many researches, valuable studies have been done for feature extraction from images data-base, but because of weak classifiers using, good results have not been achieved. In this paper, different classifiers are compared in order to increase image retrieval system precision. Five different classifiers are used in the paper: the support vector-machine, the MLP neural network, the K-nearest neighbor,...
In Computer Vision, problem of identifying or classifying the objects present in an image is called Object Categorization. It is challenging problem, especially when the images have clutter background, occlusions or different lighting conditions. Many vision features have been proposed which aid object categorization even in such adverse conditions. Past research has shown that, employing multiple...
This paper proposes a novel liver cancer identification method based on PSO-SVM. First, the region of interest (ROI) is determined by Lazy-Snapping, and various texture features are extracted from ROI. Afterwards, F-score algorithm is applied to select relevant features, based on which liver cancer classifier is designed by combining parallel Support Vector Machine (SVM) with Particle Swarm Optimization...
Analysis of fertile material such as flowers and fruit is a key factor in the proper identification of plant species. Despite object recognition being a mature research area, the use of it in automated plant identification is still relatively new. This paper describes a novel method of detecting fertile material in plant images using rectangular features. Rectangular features are obtained for the...
Classification is an important task in Hyperspectral data analysis. Hyperspectral images show strong correlations across spatial and spectral neighbors. Theoretically, classifier designed with a joint spectral and spatial correlations can improve classification performance than classifier which only utilize one of the correlations. Gaussian Processes(GPs) have been used for Hyperspectral imagery classification...
In order to reduce the relativity and improve the separability of prototype pattern vectors, a spectral-based synergetic network learning algorithm is proposed in this paper. The most attractive feature of the new method is that its complexity is linear with data dimension. To approximate the optimal cut and prevent instability due to information loss, all eigenvectors are used. The eigenvalues and...
In this paper, we present a novel visual codebook learning approach towards compactness and scale-invariance for dense patch image encoding. Firstly, each image is described as a bag of orderless gridding local patches, each of which is expressed in three scales. Then a unified objective function is proposed to simultaneously enforce the codebook compactness and select the optimal scale for each local...
In this paper, we present a direct application of Support Vector Machine with Augmented Features (AFSVM) for video concept detection. For each visual concept, we learn an adapted classifier by leveraging the pre-learnt SVM classifiers of other concepts. The solution of AFSVM is to re-train the SVM classifier using augmented feature, which concatenates the original feature vector with the decision...
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