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. 2011 Apr;38(4):1844-58.
doi: 10.1118/1.3561504.

Development and evaluation of a computer-aided diagnostic scheme for lung nodule detection in chest radiographs by means of two-stage nodule enhancement with support vector classification

Sheng Chen  1 , Kenji Suzuki, Heber MacMahon
Affiliations

Development and evaluation of a computer-aided diagnostic scheme for lung nodule detection in chest radiographs by means of two-stage nodule enhancement with support vector classification

Sheng Chen et al. Med Phys. 2011 Apr.

Abstract

Purpose: To develop a computer-aided detection (CADe) scheme for nodules in chest radiographs (CXRs) with a high sensitivity and a low false-positive (FP) rate.

Methods: The authors developed a CADe scheme consisting of five major steps, which were developed for improving the overall performance of CADe schemes. First, to segment the lung fields accurately, the authors developed a multisegment active shape model. Then, a two-stage nodule-enhancement technique was developed for improving the conspicuity of nodules. Initial nodule candidates were detected and segmented by using the clustering watershed algorithm. Thirty-one shape-, gray-level-, surface-, and gradient-based features were extracted from each segmented candidate for determining the feature space, including one of the new features based on the Canny edge detector to eliminate a major FP source caused by rib crossings. Finally, a nonlinear support vector machine (SVM) with a Gaussian kernel was employed for classification of the nodule candidates.

Results: To evaluate and compare the scheme to other published CADe schemes, the authors used a publicly available database containing 140 nodules in 140 CXRs and 93 normal CXRs. The CADe scheme based on the SVM classifier achieved sensitivities of 78.6% (110/140) and 71.4% (100/140) with averages of 5.0 (1165/233) FPs/image and 2.0 (466/233) FPs/image, respectively, in a leave-one-out cross-validation test, whereas the CADe scheme based on a linear discriminant analysis classifier had a sensitivity of 60.7% (85/140) at an FP rate of 5.0 FPs/image. For nodules classified as "very subtle" and "extremely subtle," a sensitivity of 57.1% (24/42) was achieved at an FP rate of 5.0 FPs/image. When the authors used a database developed at the University of Chicago, the sensitivities was 83.3% (40/48) and 77.1% (37/48) at an FP rate of 5.0 (240/48) FPs/image and 2.0 (96/48) FPs/image, respectively.

Conclusions: These results compare favorably to those described for other commercial and non-commercial CADe nodule detection systems.

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Figures

Figure 1
Figure 1
Main diagram for our CADe scheme.
Figure 2
Figure 2
Lung segmentation by using an M-ASM. Each blue point represents the transitional landmarks between two boundary types (e.g., the heart and the diaphragm, the aorta, and the apex of the left lung).
Figure 3
Figure 3
Background-trend-correction for the lung fields. (a) Lung fields fitted by a second-order bivariate polynomial function. (b) Preprocessed image (background-trend-corrected image).
Figure 4
Figure 4
Enhancement images by using gray-level morphologic filters with a nodule template (a) and rib templates (b). (a) Nodule-like-pattern-enhanced image. (b) Rib-like-pattern-enhanced image.
Figure 5
Figure 5
Templates used for gray-level morphologic enhancement. (a) Nodule template containing a bivariate normal distribution. (b) Rib templates containing lines with seven different orientations.
Figure 6
Figure 6
Nodule-enhanced image obtained by using our first-stage nodule enhancement.
Figure 7
Figure 7
Nodule enhancement by using the gray-level morphologic filter. (a) ROI with a nodule. (b) Enhancement of the nodule by using the gray-level morphologic filter with the nodule (Gaussian) template. (c) Enhancement of ribs by using the gray-level morphologic filter with the rib (line) templates. (d) Nodule enhanced by subtracting (c) from (b).
Figure 8
Figure 8
Nodule likelihood map obtained by using our second-stage nodule enhancement.
Figure 9
Figure 9
Schematic diagram for our nodule likelihood calculation for a point of interest O.
Figure 10
Figure 10
Nodule candidate segmentation by using our clustering watershed segmentation method. (a) ROI with a nodule. (b) First-stage nodule enhancement image by using background-trend correction and a gray-level morphologic filter. (c) Regions obtained by thresholding of the image (b) with a low positive threshold value. (d) Regions after erosion. (e) Connected region representing a rough nodule candidate. (f) Candidate region after dilation. (g) Inverted image. (h) Result with watershed segmentation alone. One region was divided into multiple small segments (catchment basins). (i) Nodule candidate segmented by our clustering watershed segmentation. (j) Nodule contour.
Figure 11
Figure 11
Criterion used for our cluster merging, where P1 and P2 are peak values in the corresponding clusters and min is the minimum value between the peaks.
Figure 12
Figure 12
(a) Nodule region. (b) Surrounding region.
Figure 13
Figure 13
FROC curves indicating the performance of the nodule candidate detection part of our CADe scheme for the JSRT database and the U of C database.
Figure 14
Figure 14
FROC curves indicating the performance of our CADe scheme with SVM or LDA for the JSRT database.
Figure 15
Figure 15
FROC curves indicating the performance of our CADe scheme for the JSRT database.
Figure 16
Figure 16
FROC curves indicating the performance of our CADe scheme for the U of C database.
Figure 17
Figure 17
FROC curves indicating the performance of our CADe scheme by nodule subtlety for the JSRT database.
Figure 18
Figure 18
FROC curves indicating the performance of our CADe scheme by nodule size for the JSRT database.
Figure 19
Figure 19
FROC curves indicating the performance of our CADe scheme by pathology for the JSRT database.
Figure 20
Figure 20
Illustrations of true positives (arrows) and false positives of our CADe marks (circles). (a) Cases from the U of C database. (b) Cases from the JSRT database.
Figure 21
Figure 21
Illustrations of false negatives (arrows) and false positives of our CADe marks (circles). (a) Cases from the JSRT database. (b) Cases from the U of C database.
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