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Computer aided diagnosis (CADx) systems for magnetic resonance imaging of prostate have shown potential to increase accuracy for detection of cancer. The purpose of this study is to introduce a method for CADx to detect prostate cancer based on texture features extracted from a grid placed on diffusion weighted imaging (DWI) parametric maps. Texture maps of DWI parametric maps (monoexponential: ADC...
Curve-skeletons encode both geometrical and topological information of 3D volumes (sets of voxels), and are key to many applications. However, due to the complexity and variability of the shapes, there is a variety of algorithms yielding skeletons suitable for certain objects, but inappropriate for others. In this article, we are interested in filtering skeletons of digital tubular objects with varying-diameter...
Robust automatic detection of moving objects in a marine context is a multi-faceted problem due to the complexity of the observed scene. The dynamic nature of the sea caused by waves, boat wakes, and weather conditions poses huge challenges for the development of a stable background model. Moreover, camera motion, reflections, lightning and illumination changes may contribute to false detections....
Color correction is an important problem in image stitching. There is a color inconsistency issue between the images (good quality as a reference image and bad quality as a test image) to be stitched. This paper presents a color correction approach with histogram specification and global mapping. The proposed algorithm can make images share the same color style and obtain color consistency. There...
During the last decade leukemia and lymphomas have been a hot topic in the biomedical area. Their diagnosis is a time-consuming task that, in many cases, delays treatments. On the other hand, discrete orthogonal moments (DOMs) are a tool recently introduced in biomedical image analysis. Here, we propose a combination of DOMs to help in the diagnosis of leukemia and lymphomas. We classify the IICBU2008-lymphoma...
Numerous micro-channels have recently been discovered in the human temporal bone by x-ray micro-CT-scanning. After a preliminary study suggesting that these micro-channels form a separate blood supply for the mucosa of the mastoid air cells, a structural analysis of the micro-channels using a local structure tensor was carried out. Despite the high-resolution of the micro-CT scan, presence of noise...
Skin appearance is almost universally the object of gender-related expectations and stereotypes. This not with standing, remarkably little work has been done on establishing quantitatively whether skin texture can be used for gender discrimination. We present a detailed analysis of the skin texture of 43 subjects based on two complementary imaging modalities afforded by a visible-light dermoscope...
In this work, four well known convolutional neural networks (CNNs) that were pretrained on the ImageNet database are applied for the computer assisted diagnosis of celiac disease based on endoscopic images of the duodenum. The images are classified using three different transfer learning strategies and a experimental setup specifically adapted for the classification of endoscopic imagery. The CNNs...
In this paper we examine the use of deep convolutional neural networks for semantic image segmentation, which separates an input image into multiple regions corresponding to predefined object classes. We use an encoder-decoder structure and aim to improve it in convergence speed and segmentation accuracy by adding shortcuts between network layers. Besides, we investigate how to extend an already trained...
In comparison with the standard three-channel colour images, spectral retinal images provide more detailed information about the structure of the retina. However, the availability of spectral retinal images for the research and development of image analysis methods is limited. In this paper, we propose two approaches to reconstruct spectral retinal images based on common RGB images. The approaches...
Computer aided diagnostic and segmentation tools have become increasingly important in reducing the workload of medical experts performing diagnosis, monitoring and documentation of various eye diseases such as age-related macular degeneration (AMD), diabetic retinopathy (DR) and glaucoma. Supervised methods have been developed for the segmentation and detection of lesions, and the reported performance...
Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or non-rigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural...
In this paper we introduce an automatic monitoring system for the detection and the evaluation of the evolution of hemangiomas using a fuzzy logic system based on two parameters: area and redness. We have considered pairs of images (from two different moments in time) that show hemangiomas either evolving, stationary or regressing. The starting points of the algorithm are the rectangular regions of...
We propose a computer vision-based de-identification pipeline that enables automated segmentation of humans in videos and effective protection of their identities. Due to the ubiquity of video surveillance, many jurisdictions implement strict regulations for the protection of personal data in publicly collected video sequences, requiring the data to be de-identified. However, soft biometric and non-biometric...
We have investigated the classification of micro-calcification clusters in mammograms by combining two existing approaches. One of the approaches involves extracting and using topological information (connectivity) about micro-calcification clusters as feature vectors to classify them as being benign or malignant. The other approach involves extracting and using location details of micro-calcification...
In this paper, we describe a face verification method which is based on non-linear class-specific discriminant subspace learning. We follow the Kernel Spectral Regression approach to this end and employ a prototype-based approximate kernel regression scheme in order to scale the method for large-scale nonlinear discriminant learning. Experiments on two publicly available facial image databases show...
In this paper, an information fusion system for tree species recognition through leaves is proposed. This approach consists in training sub-classifiers (Random forests) with attributes extracted from leaf photos. The database is incomplete, partial and some data is conflicting. A hierarchical fusion system based on Belief functions theory allows the fusion of data provided by different sub-classifiers...
In this paper, we propose to compare different methods for tumor segmentation in positron emission tomography (PET) images. We first propose to tackle this problem under the umbrella of shape optimization and 3D deformable models. Indeed, 2D active contours have been widely investigated in the literature but these techniques do not take advantage of 3D informations. On the one hand, we use the well-known...
Reliability and accuracy of the features extracted from fingerprints are essential for the performance of any fingerprint comparison algorithm. Image Enhancement as a pre-processing step allows to extract features more accurately by enhancing the quality of the fingerprint signal. This work proposes to use De-Convolutional Auto-Encoders for fingerprint image enhancement. Its performance is compared...
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