Lung segmentation on standard and mobile chest radiographs using oriented Gaussian derivatives filter

Abstract

Background: Unsupervised lung segmentation method is one of the mandatory processes in order to develop a Content Based Medical Image Retrieval System (CBMIRS) of CXR. The purpose of the study is to present a robust solution for lung segmentation of standard and mobile chest radiographs using fully automated unsupervised method.

Methods: The novel method is based on oriented Gaussian derivatives filter with seven orientations, combined with Fuzzy C-Means (FCM) clustering and thresholding to refine the lung region. In addition, a new algorithm to automatically generate a threshold value for each Gaussian response is also proposed. The algorithms are applied to both PA and AP chest radiographs from both public JSRT dataset and our private datasets from collaborative hospital. Two pre-processing blocks are introduced to standardize the images from different machines. Comparisons with the previous works found in the literature on JSRT dataset shows that our method gives a reasonably good result. We also compare our algorithm with other unsupervised methods to provide fairly comparative measures on the performances for all datasets.

Results: Performance measures (accuracy, F-score, precision, sensitivity and specificity) for the segmentation of lung in public JSRT dataset are above 0.90 except for the overlap measure is 0.87. The standard deviations for all measures are very low, from 0.01 to 0.06. The overlap measure for the private image database is 0.81 (images from standard machine) and 0.69 (images from two mobile machines). The algorithm is fully automated and fast, with the average execution time of 12.5 s for 512 by 512 pixels resolution.

Conclusions: Our proposed method is fully automated, unsupervised, with no training or learning stage is necessary to segment the lungs taken using both a standard machine and two different mobile machines. The proposed pre-processing blocks are significantly useful to standardize the radiographs from mobile machines. The algorithm gives good performance measures, robust, and fast for the application of the CBMIRS.

Figure 1

Image processing flow for the proposed lung segmentation method. The diagram is divided into two main sections: the pre-process (with contrast adjustment and cropping block) and segmentation (with Gaussian Derivatives, global thresholding and Fuzzy C-Means algorithms).

Figure 2

Example of different projection and positioning in chest radiographies with their respective histograms. (a) PA erect from standard machine; (b) AP sitting; and (c) AP Supine from portable machines.

Figure 3

The outputs of the contrast adjustment block. The two images are from different portable machines (a) to (c) and (d) to (f). (a) and (d) are the original images, (b) and (e) are after inverting the image and (c) and (f) are the results after correcting the contrast.

Figure 4

The outputs of the cropping block. (a) – (f): original image, thresholded image, after dilation, outside wordings removal, mapped to original image, final output (cropped).

Figure 5

Output of the GD responses after thresholding. (a) – (g) thresholded responses for θ=0°,30°,60°,90°,120°,150° and 180°, and (h) output of the combined responses after the ‘cleaning’ processes with rule-based algorithms.

Figure 6

Filling the lung outline based on global thresholding and convex hull. (a) Input image L _outline (b) smoothed I _HFEF, (c) thresholded I _HFEF (I _th), (d) convex hull of L _outline (L _CH), (e) ROI of I _th within L _CH (f) I _th-roi, (g) I _th-roi + L _outline, and (h) final estimated lung mask, L _mask.

Figure 7

Output of different number of clusters for FCM. (a) highlighted ground truth region (orange) overlapped with L _mask, (b) I _mask, (c) n = 3, (d) n = 4, (e) n = 5, (f) n = 6, (g) n = 7 and (h) n = 8.

Figure 8

Process of refining the lung region using FCM cluster images for n = 8. (a) – (h) cluster image I _c1, I _c2, I _c3, I _c4, I _c5, I _c6, I _c7, I _c8, (i) processed I _c5, (j) processed I _c6, (k) I _c234, (l) I _c678, (m) final output, L _final and (n) highlighted ground truth region (orange) overlapped with L _final.

Figure 9

Performance measures of the proposed method for each image using the public JSRT dataset (247 images).

Figure 10

Segmentation outputs (contours and confusion matrix) using the public JSRT dataset. Results are shown for the best ((a) to (f)) and worst ((g) to (l)) 3 of 247 images. TN pixels are dark grey, TP are light grey, FP are white and FN are black.

Figure 11

Performance measures of the proposed method for each image using the private SH: Siemens FD-X dataset (79 images).

Figure 12

Segmentation outputs (contours and confusion matrix) using the private Siemens FD-X dataset. Results are shown for the best ((a) to (f)) and worst ((g) to (l)) 3 of 79 images. TN pixels are dark grey, TP are light grey, FP are white and FN are black.

Figure 13

Performance measures for each image of both private mobile datasets (CR0975 and ADC5146) with 46 images in total.

Figure 14

Segmentation outputs (contours and confusion matrix) on combined private mobile dataset (CR0975 and ADC5146). Results are shown for the best ((a) to (f)) and worst ((g) to (l)) 3 of 46 images. TN pixels are dark grey, TP are light grey, FP are white and FN are black.