A Fully Automated Method for Lung Nodule Detection From Postero-Anterior Chest Radiographs

Abstract

In the past decades, a great deal of research work has been devoted to the development of systems that could improve radiologists' accuracy in detecting lung nodules. Despite the great efforts, the problem is still open. In this paper, we present a fully automated system processing digital postero-anterior (PA) chest radiographs, that starts by producing an accurate segmentation of the lung field area. The segmented lung area includes even those parts of the lungs hidden behind the heart, the spine, and the diaphragm, which are usually excluded from the methods presented in the literature. This decision is motivated by the fact that lung nodules may be found also in these areas. The segmented area is processed with a simple multiscale method that enhances the visibility of the nodules, and an extraction scheme is then applied to select potential nodules. To reduce the high number of false positives extracted, cost-sensitive support vector machines (SVMs) are trained to recognize the true nodules. Different learning experiments were performed on two different data sets, created by means of feature selection, and employing Gaussian and polynomial SVMs trained with different parameters; the results are reported and compared. With the best SVM models, we obtain about 1.5 false positives per image (fp/image) when sensitivity is approximately equal to 0.71; this number increases to about 2.5 and 4 fp/image when sensitivity is = 0.78 and = 0.85, respectively. For the highest sensitivity (= 0.92 and 1.0), we get 7 or 8 fp/image.

Automated Lung Segmentation from HRCT Scans with Diffuse Parenchymal Lung Diseases

“…These methods can be generally divided into the following four major categories: threshold method [11][12][13][14][15][16][17][18], deformable boundary models [19][20][21][22][23][24], edge-based methods [25][26][27][28], and registration-based method [29,30]. Lungs appear as dark regions in CT scans, since they are essentially bags full of air inside the body.…”

“…Then, an edge tracking procedure using the Laplacian of Gaussian (LoG) operator at different scales was used to find a continuous external lung contour that was further integrated with the initial outline to produce the final lung segmentation from PA chest radiographs [25]. The ROIs from PA chest radiographs are rectangular areas which surround each lung field as closely as possible through an iterative procedure.…”

An X-ray CAD system with ribcage suppression for improved detection of lung lesions

“…Only a few of the recently published ones are [6][7][8][9][10][11][12]. A brief description of them can be found in [13].…”

Computational methods for the analysis of functional 4D-CT chest images.

“…El-Baz et al [63,64] The edge-model-based lung segmentation is performed using spatial edge-detector filters, or wavelet transforms. Campadelli et al [65] detect an inial outline of lung borders by using the first derivative of Gaussian filters taken at four different orientations. Then, an edge tracking procedure using the Laplacian of Gaussian (LoG) operator at three different scales is used to find a continuous external lung contour, which is further integrated with the initial outline to produce the final lung segmentation from PA chest radiographs.…”

“…In the application to CT chest images, most of the earlier segmentation techniques [32,35,47,52,[65][66][67] presume only the lungs are darker than the other chest tissues, which might result in failure to detect nodules in the case of severe lung pathologies. To avoid such failures, more recent lung segmentation methods, which will be briefly reviewed in this section, consider visual appearances [1,357], shapes [55,[358][359][360][361][362], or hybrid techniques [60,62,[363][364][365] to account for normal and pathological tissues.…”