abstract solitary pulmonary nodule (spn) is explained as a separate, well-determined obscurity with the maximum size of 3cm which is enclosed by typical lung tissue and is not related to any other anomalies in the lung or adjacent lymph nodes. among imaging modalities mri has more benefits such as high spatial resolution and usage of nonionizing radiation, however, the efficacy of this imaging ...

With the superb spatial resolution of modern multislice CT scanners and their ability to complete a thoracic scan within one breath-hold, software algorithms for computer-aided detection (CAD) of pulmonary nodules are now reaching high sensitivity levels at moderate false positive rates. A number of pilot studies have shown that CAD modules can successfully find overlooked pulmonary nodules and...

An automated pulmonary nodule detection system is necessary to help radiologist to identify and detect the nodules at early stage. In this paper, a novel pulmonary nodule detection system is proposed using Artificial Neural Networks (ANN) based on hybrid features consist of 2D and 3D Geometric and Intensity based statistical features. The lung volume is segmented using thresholding, 3D connecte...

We propose a computer-aided detection (CAD) system which can detect small-sized (from 3mm) pulmonary nodules in spiral CT scans. A pulmonary nodule is a small lesion in the lungs, round-shaped (parenchymal nodule) or worm-shaped (juxtapleural nodule). Both kinds of lesions have a radio-density greater than lung parenchyma, thus appearing white on the images. Lung nodules might indicate a lung c...

OBJECTIVE The aims of this study were to assess the sensitivity of various magnetic resonance imaging (MRI) sequences for the diagnosis of pulmonary nodules and to estimate the accuracy of MRI for the measurement of lesion size, as compared to computed tomography (CT). METHODS Fifty patients with 113 pulmonary nodules diagnosed by CT underwent lung MRI and CT. MRI studies were performed on 1....

This paper summarizes the literature on computer-aided detection (CAD) systems used to identify and diagnose lung nodules in images obtained with computed tomography (CT) scanners. The importance of developing such lies fact that process manually detecting is painstaking sequential work for radiologists, as it takes a long time. Moreover, pulmonary have multiple appearances shapes, large number...

Machine learning approaches hold great potential for the automated detection of lung nodules in chest radiographs, but training the algorithms requires vary large amounts of manually annotated images, which are difficult to obtain. Weak labels indicating whether a radiograph is likely to contain pulmonary nodules are typically easier to obtain at scale by parsing historical free-text radiologic...

Automatic diagnosing lung cancer from Computed Tomography (CT) scans involves two steps: detect all suspicious lesions (pulmonary nodules) and evaluate the wholelung/pulmonary malignancy. Currently, there are many studies about the first step, but few about the second step. Since the existence of nodule does not definitely indicate cancer, and the morphology of nodule has a complicated relation...

BACKGROUND To compare the capability of lung nodule detection and characterization between dual-energy radiography with color-representation (DCR) and conventional gray scale chest radiography (GSR). METHODS A total of 130 paired chest radiographs (DCR and GSR) obtained from 65 patients (14 with normal scans and 51 with pulmonary nodules) were evaluated. After analysis, 45 non-calcified and 2...