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In this paper, we propose new prognostic methods that predict 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this prediction using a set of features extracted from the CT image and segmentation maps of multiple anatomic structures. We explore two approaches: 1) a unified framework based on two state-of-the-art deep learning...
Unilateral peripheral facial paralysis (UPFP) is a form of facial nerve paralysis and clinically classified according to facial asymmetry. Prompt and precise assessment is crucial to the neural rehabilitation of UPFP. For UPFP assessment, most of the existing assessment systems are subjective and empirical. Therefore, an objective assessment system will help clinical doctors to obtain a prompt and...
Automatic segmentation of retinal blood vessels from fundus images plays an important role in the computer aided diagnosis of retinal diseases. The task of blood vessel segmentation is challenging due to the extreme variations in morphology of the vessels against noisy background. In this paper, we formulate the segmentation task as a multi-label inference task and utilize the implicit advantages...
The structural analysis of nerve fibers of the human brain is an important topic in current neuroscience. To obtain information about neural connections with micrometer resolution, polarized light imaging (3D PLI) of histological brain sections is well suited. In our application, both high-resolution (HR, 64µm in-plane pixel size) and ultra-high resolution (ultra-HR, 1.3µm) 3D PLI data of human brain...
We report an automated method for characterization of microvessel morphology in micrographs of brain tissue sections to enable the facile, quantitative analysis of vascular differences across large datasets consisting of hundreds of images with thousands of blood vessel objects. Our objective is to show that virtual 3D parametric models of vasculature are adequately capable of representing the morphology...
Computerized prenatal ultrasound (US) image segmentation methods can greatly improve the efficiency and objectiveness of the biometry interpretation. However, the boundary incompleteness and ambiguity in US images hinder the automatic solutions severely. In this paper, we propose a cascaded framework for fully automatic US image segmentation. A customized Fully Convolutional Network (FCN) was utilized...
Adrenal lesions include a wide variety of benign and malignant neoplasms of the adrenal gland, and are seen in up to 5% of computed tomography (CT) examinations of the abdomen. Better identification of these lesions is important for effective management and patient prognosis. Detection on low-contrast CT images, however, even for experienced physicians can be difficult and error-prone, because the...
This work addresses Transfer Learning via Convolutional Neural Networks (CNN's) for the automated classification of colonic polyps in eight HD-endoscopic image databases acquired using different modalities. For this purpose, we explore if the architecture, the training approach, the number of classes, the number of images as well as the nature of the images in the training phase can influence the...
Modality corresponding to medical images is a vital filter in medical image retrieval systems. This article presents the classification of modalities of medical images based on the usage of principles of hyper-dimensional computing and reservoir computing. It is demonstrated that the highest classification accuracy of the proposed method is on a par with the best classical method for the given dataset...
Ultrasonography is a valuable diagnosis method for thyroid nodules. Automatically discriminating benign and malignant nodules in the ultrasound images can provide aided diagnosis suggestions, or increase the diagnosis accuracy when lack of experts. The core problem in this issue is how to capture appropriate features for this specific task. Here, we propose a feature extraction method for ultrasound...
This paper describes an artificial neural network (ANN) method that employs a feature-learning algorithm to detect the lumen and MA borders in intravascular ultrasound (IVUS) images. Three types of imaging features including spatial, neighboring, and gradient features were used as the input features to the neural network, and then the different vascular layers were distinguished using two sparse autoencoders...
A wide range of biomedical applications require detection, quantification and modelling of curvilinear structures in 3D images. Here we propose a 3D contrast-independent approach to enhance curvilinear structures based on the 3D Phase Congruency Tensor concept. The results show that the proposed method is insensitive to intensity variations along the 3D curve, and provides successful enhancement within...
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