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This study developed a computer-aided detection (CAD) scheme for pulmonary embolism (PE) detection and investigated several approaches to improve CAD performance. In the study, 20 computed tomography examinations with various lung diseases were selected, which include 44 verified PE lesions. The proposed CAD scheme consists of five basic steps: 1) lung segmentation; 2) PE candidate extraction using...
The principal goal of the segmentation process is to partition an image into classes or subsets that are homogeneous with respect to one or more characteristics or features. In medical imaging, segmentation is important for feature extraction, image measurements, and image display. This study presents a new version of complex-valued artificial neural networks (CVANN) for the biomedical image segmentation...
Accurate and automated Lung Field (LF) segmentation in volumetric computed tomography protocols is highly challenged by the presence of pathologies affecting lung borders, also affecting the performance of computer-aided diagnosis (CAD) schemes. In this work, a three-dimensional LF segmentation algorithm adapted to interstitial lung disease patterns (ILD) patterns is presented. The algorithm employs...
Identification of lobar fissures in human lungs is a non-trivial task due to their variable shape and appearance, along with the low contrast and high noise in computed tomographic (CT) images. Pathologies in the lungs can further complicate this identification by deforming and/or disrupting the lobar fissures. Current algorithms rely on the general anatomy of the lungs to find fissures affected by...
Autofluorescence bronchoscopy (AFB) has been utilized over the past decade, proving to be a powerful tool for the detection and localization of premalignant and malignant lesions of the airways. AFB is, however, characterized by low specificity and a high rate of false positive findings (FPFs). The majority of FPFs are due to inflammations, as they often fluoresce at the same wavelengths with cancer...
A pulmonary nodule is relatively round lesion, or area of abnormal tissue located within the lung that can be seen in thoracic CT scans. Because noise and same like disturbance of blood vessels and tracheas, detection of the lung nodule is difficult. A three-dimensional pulmonary nodule detection method for thoracic CT scans is proposed in this paper. First, bounding box method and three-dimensional...
We propose a novel approach for on-line treatment verification using cine EPID (electronic portal imaging device) images for hypofractionated lung radiotherapy based on a machine learning algorithm. Hypofractionated lung radiotherapy has high precision requirement, and it is essential to effectively monitor the target making sure the tumor is within beam aperture. We model the treatment verification...
Detection of abnormal areas such as lung nodule, ground glass opacity on multi detector computed tomography images is a difficult task for radiologists. It is because subtle lesions such as small lung nodules tend to be low in contras, and a large number of computed tomography images require a long visual screening times. To detect the abnormalities by use of computer aided diagnosis (CAD) system,...
Automated 3D lung modeling involves analyzing 2D lung images and reconstructing a realistic 3D model of the lung. This paper presents a review of the existing works on automatic formation of 3D lung models from 2D lung images. A common framework for 3D lung modeling is proposed. It consists of eight components: image acquisition, image pre-processing, image segmentation, boundary creation, image recognition,...
In this study, three dimensional medical images of the lungs and brain are recognized and extracted by artificial neural networks. The neural networks used in this paper are the conventional sigmoid function neural network trained using back propagation (BP) algorithm and radial basis function (RBF) neural network. We compared the recognition results of these neural networks and determine which neural...
This paper aims at developing a CAD system used for the detection of Ground Glass Opacity (GGO) nodules in chest CT images. In our scheme, we apply Gabor filter on the CT image in order to enhance the detection process. After this we perform some morphological operations including threshold process and labeling to extract the objects having high intensity values. Then, some feature analysis is used...
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