Abstract
In this paper, a landmark based approach, using five different interpolating polynomials (linear, cubic convolution, cubic spline, PCHIP, and Makima) for modeling of lung field region in 2D chest X-ray images have been presented. Japanese Society of Radiological Technology (JSRT) database which is publicly available has been used for evaluation of the proposed method. Selected radiographs are anatomically landmarked using 17 and 16 anatomical landmark points to represent left and right lung field regions, respectively. Local, piecewise polynomial interpolation is then employed to create additional semilandmark points to form the lung contour. Jaccard similarity coefficients and Dice coefficients have been used to find accuracy of the modeled shape through comparison with the prepared ground truth. With the optimality condition of three intermediate semilandmark points, PCHIP interpolation method with an execution time of 5.04873 s is found to be the most promising candidate for lung field modeling with an average Dice coefficient (DC) of 98.20 and 98.54% (for the left and right lung field, respectively) and with the average Jaccard similarity coefficient (JSC) of 96.47 and 97.13% for these two lung field regions. While performance of Makima and cubic convolution is close to the PCHIP with the same optimality condition, i.e., three intermediate semilandmark points, the optimality condition for the cubic spline method is of at least seven intermediate semilandmark points which, however, does not result in better performance in terms of accuracy or execution time.
Highlights
The chest X-ray imaging is still one of the most preferred techniques that radiologists and medical practitioners use to diagnose the lung diseases in their daily routine checkups due to its low cost and easy availability
We have presented an effective method of anatomical landmark point selection and their minimization and modeling of the lung field shape using five different interpolation techniques, namely, linear, cubic convolution, cubic spline, Piecewise cubic Hermite interpolation polynomial (PCHIP), and Makima
For PCHIP and Makima interpolation methods, an incremental change in Jaccard similarity coefficient (JSC) and Dice coefficient (DC) is observed as the number of intermediate semilandmark points between each consecutive anatomical landmark pair increases from one to three intermediate semilandmark point(s)
Summary
The chest X-ray imaging is still one of the most preferred techniques that radiologists and medical practitioners use to diagnose the lung diseases in their daily routine checkups due to its low cost and easy availability. Due to this reason, the accurate detection and segmentation of the lung field region are of prime importance for any biomedical image analysis procedures [1,2,3]. Delineation of the lung field is a prerequisite for any chest-image analysis procedure. An automated solution for the lung field segmentation is needed [4]. As chest X-rays are low contrast images, the lung regions cannot be differentiated from the background and the classical approaches
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