Automated mediastinal lymph node detection from CT volumes based on intensity targeted radial structure tensor analysis

Abstract. This paper presents a local intensity structure analysis based on an intensity targeted radial structure tensor (ITRST) and the blob-like structure enhancement filter based on it (ITRST filter) for the mediastinal lymph node detection algorithm from chest computed tomography (CT) volumes. Although the filter based on radial structure tensor analysis (RST filter) based on conventional RST analysis can be utilized to detect lymph nodes, some lymph nodes adjacent to regions with extremely high or low intensities cannot be detected. Therefore, we propose the ITRST filter, which integrates the prior knowledge on detection target intensity range into the RST filter. Our lymph node detection algorithm consists of two steps: (1) obtaining candidate regions using the ITRST filter and (2) removing false positives (FPs) using the support vector machine classifier. We evaluated lymph node detection performance of the ITRST filter on 47 contrast-enhanced chest CT volumes and compared it with the RST and Hessian filters. The detection rate of the ITRST filter was 84.2% with 9.1 FPs/volume for lymph nodes whose short axis was at least 10 mm, which outperformed the RST and Hessian filters.


Introduction
Lung cancer is the leading cause of cancer-related deaths in the United States 1 and China. 2 It is also the leading cause of cancerrelated deaths among men worldwide. 3 There are several methods of treatment: surgery, chemotherapy, and radiotherapy. To choose the best treatment procedure, cancer staging based on the TNM staging system 4 is required. Three factors are focused on in staging: T (tumor), N (lymph nodes), and distant M (metastasis). In the preoperative diagnosis of lung cancer, radiologists check mediastinal lymph nodes on computed tomography (CT) volumes. However, because lymph nodes are small and their silhouettes are not clear, they might be overlooked. To prevent medical doctors from overlooking them and to lighten their burden, a computer-aided detection system for automated lymph node detection is strongly desired.
There are various approaches for detecting lymph nodes from CT volumes: random forest statistical classifier, 5 local intensity structure analyses based on Hessian matrix, [6][7][8] or radial structure tensor (RST). 9 Three-dimensional (3-D) Haar-like features are 3-D feature point detection algorithms that can detect bloblike structures in volumetric images. Barbu et al. 10 introduced 3-D Haar-like features for axillary, pelvic, and abdominal lymph nodes. Feulner et al. 11 utilized them for mediastinal lymph node detection. The random forest statistical classifier is a supervised machine learning technique that can be utilized for enhancing target objects in image volumes. Cherry et al. 12 utilized random forest statistical classifiers for abdominal lymphadenopathy detection.
Local intensity structure analysis based on the Hessian matrix has been widely used for many algorithms of automated detection and segmentation of organs [13][14][15] and lesions. [16][17][18][19] The Hessian matrix is computed for each location and describes the local intensity structure as a blob, line, or sheet around the location, and whether it is brighter or darker than surrounding regions. The bright blob-like structure enhancement filter based on the Hessian matrix (Hessian filter) responds with a high value at the central part of the blob-like regions, which are brighter than surrounding regions. Feuerstein et al. 20 proposed a mediastinal lymph node detection algorithm using this. Another algorithm proposed by Liu et al. 21 is also based on Hessian analysis. Random forest 5 and support vector machine (SVM) 22 classifiers were introduced to improve performance. Roth et al. 23 introduced deep convolutional neural networks 24 for further improvement.
Another detection algorithm is through local intensity structure analysis based on RST. 9 Nimura et al. 25 introduced the bright blob-like structure enhancement filter based on RST (RST filter) for detecting the abdominal lymph nodes. Its benefit is that it can enhance the entire region of the target object, in contrast to the Hessian filter, which enhances only the central part of the region. The RST filter can capture the lymph node shape more properly than the Hessian filter. The features extracted can be used to determine whether each candidate region is a true positive (TP) or a false positive (FP) using machine learning techniques. However, the RST filters have so far not performed well on mediastinal lymph node detection. The current RST filter fails when tissues have largely varying intensity distributions close to the target, for instance, in the case of air and contrasting blood vessels.
Lymph nodes on CT volumes typically show the following characteristics: 1. slightly higher intensity than surrounding regions, 2. spherical shape, 3. narrow intensity range similar to soft tissue.
To identify lymph nodes in CT images, the RST filter is designed to detect the regions with characteristics (1) and (2). However, mediastinal lymph node detection is a challenging problem in medical imaging because mediastinal lymph nodes are closely surrounded by many structures, such as contrastenhanced blood vessels or air, as shown in Fig. 1. Although there are several works on lymph node detection on CT volumes, they fail to detect such lymph nodes. To overcome this problem, this paper proposes a new filter called the intensity targeted radial structure tensor (ITRST) filter, that is able to detect lymph nodes located around anatomical structures of extremely higher or lower intensities. The idea of the ITRST filter is to ignore extremely higher or lower intensity regions in RST computation to meet the requirement of characteristic (1). This allows us to detect lymph nodes neighboring regions with extremely high or low intensities.
The contribution of this paper is (a) to propose a new lymph node detection filter called the ITRST filter and (b) to evaluate its performance using artificial and clinical CT volumes.
In Sec. 2, we propose the automated mediastinal lymph node detection algorithm based on the ITRST filter and SVM classifier. In Sec. 3, we present two experiments showing the efficacy of the ITRST filter. The results are presented and discussed in subsequent sections.

ITRST Filter
This paper introduces a new blob-like structure enhancement filter called the ITRST filter, which is a modified version of the RST filter. Before explaining the ITRST filter, we will give a brief overview of the RST filter. The RST is given as the second-order tensor matrix (3 × 3 matrix) and is given by ; t e m p : i n t r a l i n k -; e 0 0 1 ; 3 2 6 ; 5 6 4 TðxÞ ¼ where x ¼ ðx; y; zÞ T is the location of a voxel where the RST is computed, r i denotes an i'th search direction from x, and g i;j is a local gradient vector of Iðx i;j Þ. α i;j is an opacity, given by E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 2 ; 3 2 6 ; 4 8 7 where i is the index of search directions, j is the index of search steps of each search, x ij is a voxel located in the j'th search step on the i'th search direction, t min and t max (t min < t max ) are parameters for controlling the sensitivity of the gradient. When an accumulated opacity β i ¼ P j α i;j ≃ 1 or a search length becomes t len or larger, a search for the i'th search direction is terminated.
Eigenvalues λ 0 , λ 1 , λ 2 ðjλ 0 j ≥ jλ 1 j ≥ jλ 2 jÞ of TðxÞ þ T T ðxÞ represent the magnitude of the gradient directing the corresponding eigenvector around x. If all eigenvalues are negative, IðxÞ is brighter than the surrounding region. The larger the magnitude of the eigenvalue, the larger the gradient. The eigenvalues are utilized to enhance the bright blob-like structure regions that have the condition λ 1 ≃ λ 2 ≃ λ 3 ≪ 0 using an evaluation formula. For example, a simple evaluation formula E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 3 ; 3 2 6 ; 2 6 2 was proposed by Li et al. 8 Such formulas produce high responses in the bright blob-like regions. However, if some of the radial searches incorporate regions whose intensities are extremely high or low, huge intensity gradients of some specified directions are summed into the RST TðxÞ, according to Eq. (1). The eigenvalues calculated in such regions may become λ 0 ≪ λ 1 ≤ λ 2 ≤ 0 or λ 1 ≤ λ 2 ≤ 0 ≪ λ 0 , and the responses of an evaluation formula such as Eq. (3) become low.
To prevent the effect of the huge intensity gap explained above, we propose the ITRST filter. A schematic illustration showing the difference between the RST and the ITRST filters is summarized in Fig. 2. The ITRST filter introduces the prior knowledge of the target region to prevent summing huge intensity gradients into the ITRST. Intensity gradients at the location of higher or lower intensity than the thresholds are not summed into the ITRST. The ITRST is defined by modifying Eq. (1) as E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 4 ; 6 3 ; 4 2 1 T 0 ðxÞ ¼ where γ i;j is a function that classifies whether all points utilized for computing g i;j have the intensity within a predetermined range or not, which is defined as E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 5 ; 6 3 ; 3 4 5 γ i;j ¼ 1 if t dark ≤ Iðx 0 Þ ≤ t bright for ∀ x 0 ∈ N ði; jÞ 0 otherwise; ; (5) where N ði; jÞ is a set of six-neighborhood voxels around x i;j . In Eq. (5), t dark and t bright are upper and lower limits of target intensity range, respectively. The parameters t dark and t bright affect the enhancement results as follows. If we lower t dark , this causes FPs of lymph nodes neighboring air regions. If we set t bright higher, this setting produces FPs of lymph nodes neighboring contrastenhanced blood vessels (intensities of 150 to 300 H.U.). Choosing higher t dark or lower t bright makes the filter responses lower because it reduces the intensity gradients summed into the ITRST. When an accumulated opacity β 0 i ¼maxð P j α i;j ;γ i;j Þ≃ 1 or a search length becomes t len or larger, a search for the i'th search direction is terminated. Eigenvalues of T 0 ðxÞ þ T 0T ðxÞ, λ 0 0 , λ 0 1 , λ 0 2 ðjλ 0 0 j ≥ jλ 0 1 j ≥ jλ 0 2 jÞ can be utilized in the same manner as those of the RST filter.

Overview
This section explains a mediastinal lymph node detection algorithm from CT volumes, which is based on the ITRST filter. In this algorithm, we assume that the input of the algorithm is a chest CT volume. The output is mediastinal lymph node detection results. Detection targets are the mediastinal lymph nodes with the specified least short axis or above. This is because enlarged lymph nodes have high possibility to be metastasized. The entire process consists of (1) preprocessing, (2) obtaining candidate regions by thresholding against the responses of the ITRST filter, and (3) FP reduction using machine learning.
The input volume I is a chest CT volume. The ground-truth binary volume I g of mediastinal lymph nodes is required if I is utilized for training only. The output is a binary volume I out , which has lymph node regions denoted by the value 1. We describe the size of each lymph node using principal components analysis. We focus on the detection of lymph nodes above a specified size, defined by a short axis length of at least r target (mm).

Preprocessing
First, we interpolate an input volume to generate an isotropic resolution volume. We apply the cubic interpolation on I to obtain a volume with isotropic resolution w reso ðmmÞ × w reso ðmmÞ × w reso ðmmÞ per voxel. 26 Furthermore, we apply a Gaussian smoothing filter with standard deviation σ smooth (mm) for reducing noise and making local gradients more stable. We denote the preprocessed input volume as I 0 .

Target region of detection
We restrict the target region for lymph node detection to inside the mediastinal region, which we define as the area between the lungs. A lung region A lung is segmented by an automatic segmentation algorithm. We use a lung segmentation algorithm similar to Hu et al. 27 First, we obtain air regions A air in I 0 as regions having lower intensities than a given threshold t air and not touching the boundary of the input CT volume. Then, we select the first and second largest connected components from the air regions detected. If the second largest component of the air regions is less than 20% of the largest one, we choose the largest component of the air regions as A lung . Finally, the mediastinum region A media is obtained by A lung using Procedure 1. The function maxðaÞ represents the maximum value of a in this procedure.
We assume that the x-axis of the input volume I corresponds with the right-to-left direction of the body, the y-axis corresponds with the front-to-back direction, and z-axis corresponds with the head-to-foot direction. We scan the lung region A lung from each pair of ðy; zÞ toward the right-to-left direction (along the x-axis). For each scan, we check whether no fewer than two components exist in the scan line. This means that there are right and left lung regions in the scan line. If no fewer than two components exist, we fill the gap between each component with the value 1.

Initial lymph node detection using ITRST filter
We obtain the candidate regions using the ITRST filter. First, we apply the ITRST filter with the evaluation Eq. (3) to I 0 . Since each point of I 0 has one response, we obtain the volume of filter response F. Because responses of the ITRST filter sometimes become zero in the blob-like regions such as an individual hole, we apply a median filter of w hole × w hole × w hole (voxels) to F to normalize such points. Then, we perform thresholding on the result of the median filter F 0 as with the threshold value t blob . Regions of connected components whose volumes are less than that of the sphere with radius t small (mm) and ones that are not touching the mediastinum region A media are eliminated. The remaining are denoted as candidate regions.

FP reduction
The ITRST filter detects many FP regions as initial lymph node candidate regions. The SVM classifier is utilized to classify each candidate region into TP or FP regions. ð10 þ 7jDjÞ-dimensional features are utilized for each candidate region, as shown in Table 1. D is the permutation of radii utilized for computation of features related to the intensity. The number of elements of D is jDj, index of D is pð1 ≤ p ≤ jDjÞ, and one of its elements is written as d ∈ D. When d > 0 mm, the target region for computing the intensity features is obtained by dilation of the candidate region, by using a structure element of a sphere whose radius is d mm. The target region represents a neighbor of the candidate region, and larger values of d make the target region thicker. When d ¼ 0 mm, the target region is the same as the candidate region. Features are computed for both the training step and the testing step, and utilized as follows.
• Training step Feature vectors obtained from candidate regions of all volumes in the training dataset are used for training of the SVM. 22 The set of lymph node regions whose short axis is at least r target w train mm in the ground truth I g is written as G. r target is a parameter representing the minimum length of the short axis of target lymph nodes. w train ð0 < w train < 1Þ is the parameter for setting the smallest size of a lymph node that is utilized for training. If the center of a candidate region is in one of the lymph node regions of G, its feature vector is utilized as a positive sample. If the center of a candidate region is outside of the lymph node regions, its feature vector is utilized as a negative sample.
• Testing step A candidate region extracted from a test volume is classified into TP or FP with its feature vector by using an SVM trained as above. If a candidate region is predicted as TP, the values of the output binary volume I out are set to 1 in the candidate region.    as uniform of 50 H.U., and Gaussian smoothing of σ ¼ 1.0 mm is applied to make the spheres similar to lymph nodes of real CT volumes. After that square poles with thickness of 15 mm and length of 50 mm are drawn.

Filter response evaluation
We apply the ITRST, RST, and Hessian filters to the artificially generated volume explained above. First, we examine the filter responses. In this experiment, we obtain the eigenvalue profile on the two spheres shown in Fig. 3(b). The line A-B in Fig. 3(b) crosses one of the spheres overlapping a dark region. The line C-D is one of the spheres overlapping a bright region. Parameters used in the experiments are shown in Table 4(a). We set t bright ¼ 100 H:U: and t dark ¼ −100 H:U: since the spheres have comparability with lymph nodes, which range from −100 to 100 H.U. We also set t len ¼ 15 mm as the upper limit of the radius of lymph nodes to be detected in real CT volumes.

Materials
Forty-seven chest CT volumes were prepared for the experiments on mediastinal lymph node detection. These volumes   were authorized by the ethics committee of Nagoya University Hospital. The specifications of the volumes are shown in Table 2.
We evaluate the performance of lymph nodes for a range of minimum sizes: the short axis is at least r target ∈ f10; 7.5; 5 mmg. Ground-truth data are a set of mediastinal lymph node labels. Two technical researchers who have sufficient knowledge of lymph nodes first manually traced lymph node candidate regions on the CT volumes. Then, an expert radiologist confirmed these traced data including missing lymph nodes on CT slices. Table 3 shows the number of lymph nodes of each size category.

Initial detection performance evaluation
We compute FROC curves of initial detection results by changing the parameter t blob , for comparing the ITRST, RST, and Hessian filters. The filter output is binarized at different thresholds (t blob ¼ 20, 40, 80, and 160 for the ITRST filter, t blob ¼ 20, 40, 80, and 160 for the RST filter, and t blob ¼ 2000, 4000, 8000, and 16,000 for the Hessian filter), as explained in Sec. 2.2.4. Each point on FROC curves represents the averages of the detection rate and the number of FPs/volume among all volumes. The corresponding error bars represent the standard deviation of the detection rate. Our detection targets are mediastinal lymph nodes whose short axes are at least r target (mm). Each mediastinal lymph node is classified and counted as TP or FN. If at least one region produced by the filter overlaps with a mediastinal lymph node of the detection target, the lymph node is counted as TP. The detection rate of each volume is defined as The number of FPs in each volume is the count of regions produced by the filter that do not overlap with any lymph nodes or lung cancers.
The FROC curves are drawn for each value (5, 7.5, and 10 mm) of the least short axis parameter r target . Parameters of t bright , t dark , and t len are the same as the experiment of Sec. 3.1. Other parameters are set empirically [Table 4(b)]. The air region segmentation threshold t air is set as −200 H:U: This threshold is set to be sufficiently lower than lymph nodes or surrounding soft tissues, which have around −100 H:U: or above. Isotropic resolution parameter w reso is set as 0.625 mm. This equals the smallest pixel size of axial slices of the CT volumes ( Table 2). Standard deviation of Gaussian smoothing filter σ smooth is set as 1 mm. This setting is good for reducing noise on chest CT volumes without severely blurring edges. The parameter w hole , the size of median filter applied to the output of the ITRST filter, is set as three voxels. This is the smallest size of median filter.

Overall detection performance evaluation
We compute the FROC curves as the overall performance. This performance includes FP reduction by SVM. An FROC curve is obtained by changing the weighting parameter w F of negative samples for the SVM classifier 22 utilized in the FP reduction step (explained in Sec. 2.2.5). Leave-one-out cross validation is conducted to evaluate the performance of FP reduction for each volume. The SVM classifier is tested with the data not used in the training process.
We also conduct a statistical test (Fisher's exact test) of the detection rate obtained by the ITRST filter and the others. For fair comparison of detection rate between the filters, we draw the FROC curve with various values of w F and estimate the detection rate at the point of 10.0 FPs/volume on the FROC curve.
The threshold for the filter output is chosen as t blob ¼ 20 for the ITRST and RST filters, and t blob ¼ 2000 for the Hessian filter, since these settings of t blob gave the highest initial detection rate with each filter. To compute the FROC curves, the weighting parameter w F is changed as 0.025, 0.05, 0.075, 0.10, 0.125, 0.15, 0.20, 0.25, 0.30, 0.40, and 0.50. Other parameters utilized for the FP reduction step are set empirically [ Table 4(c)]. The parameter w train for tolerance of using feature vectors extracted from candidate regions of lymph nodes smaller than the target for training SVM is set as 0.5 for preventing false negatives of lymph nodes whose short axis is almost the same as r target . Permutation D representing the width of regions for computing feature values regarding intensity is set as f0; 1; 2gmm for focusing on the inside and neighboring regions of each candidate region. The LIBSVM 3.17 28 library is utilized as an SVM implementation.

ITRST Filter
The responses of the ITRST, RST, and Hessian filters for the artificially generated volume are shown in Figs. 4(a)-4(c), respectively. The responses were higher in most of the sphere regions than those of the RST and Hessian filters, despite the overlapping of square poles.
The eigenvalue profiles obtained by the ITRST, RST, and Hessian filters are shown in Figs. 5(a)-5(c), respectively. By using the ITRST filter, all eigenvalues were negative, and their magnitudes do not differ much in the entire part of both spheres. By using the RST filter, λ 1 became far smaller or larger than other eigenvalues in the spheres touching the bright and dark square poles, respectively. Eigenvalues of the Hessian filter also became positive in the parts near the square poles.

Initial detection evaluation
The FROC curves of initial detection are shown in Fig. 6. As shown in Table 5, a higher detection rate was achieved by the proposed algorithm (ITRST filter) than by the RST filter. For instance, when r target ¼ 10 mm and t blob ¼ 20, 97.1% of lymph nodes were detected with 692.  Table 5 and Fig. 7 show the overall performance calculated from the output of the mediastinal lymph node detection algorithm, with parameters t blob ¼ 20 (t blob ¼ 2000 for Hessian filters) and w F ¼ 0.075. For example, when r target ¼ 10 mm, 84.2% of lymph nodes were detected with 9.1 FPs/volume by the proposed algorithm (ITRST filter). Table 6 displays the results of Fisher's exact test at 10.0 FPs/volume. Performance of the proposed algorithm was not always significantly better. It was shown that detection rates of ITRST and RST filters were significantly different (p < 0.05) with all settings (5, 7.5, and 10 mm) of the least short axis. On the other hand, detection rates of the ITRST and Hessian filters were significantly different when the least short axis was 5 mm.

ITRST Filter
It is clear that responses of the ITRST filter were higher in most of the sphere regions than those of the RST filter, even if the square poles were overlapping, as shown in Fig. 4. It suppressed the negative effect of the regions with much higher or lower intensity than the detection target. The Hessian filter yielded positive values only on some parts of the spheres.
The magnitude of λ 1 computed from the ITRST filter was similar to λ 2 and λ 3 , and it was negative on almost all of A-B or C-D, as shown in Fig. 5(a). Huge intensity gradients were not summed into the ITRST, and the eigenvalues followed the condition of the bright blob-like structure. In contrast, as shown in Fig. 5(b), the magnitude of λ 1 computed from the RST filter was far larger than λ 2 and λ 3 in the sphere. This is because huge intensity gradients directed from the sphere to the square pole were accumulated into the RST. Figure 5(c) shows the magnitude of λ 1 computed from the Hessian filter, which becomes large in the part near the square poles in the Hessian filter. The eigenvalues did not follow the condition of the bright blob-like structure (λ 1 ≃ λ 2 ≃ λ 3 ≪ 0) in the part with large magnitude of λ 1 , and the responses became low according to Eq. (3).
By comparing the FROC curves of initial detection shown in Fig. 6, it is clear that the ITRST filter had a higher detection rate for both large and small lymph nodes than the RST filter. Results after FP reduction of the proposed algorithm (ITRST filter) were also better than those of the RST filter. The ITRST filter is more useful than the RST filter for mediastinal lymph node detection.

Efficacy of ITRST filter
The lymph node shown in Fig. 8(a) was properly detected by the proposed algorithm (ITRST filter) and the Hessian filter, while the RST filter was not able to detect it. This was likely due to the presence of contrast-enhanced blood vessels and the air region adjacent to the lymph node. In contrast to the RST filter, the ITRST filter reduced the impact of the large intensity gradients around the lymph node so Table 5 Comparison of lymph node detection performances. Note that symbol * represents performance shown in publication. Their experiments were performed using different datasets or criteria from ours.

Algorithm
Least short axis (mm) that the eigenvalues still followed the condition of the bright blob-like structure. This lymph node was not surrounded entirely by extremely high or low intensity regions, so some intensity gradients derived from soft tissue could still be utilized to descibe the bright blob-like structure. The lymph node shown in Fig. 8(b) was detected initially by all algorithms. The candidate region obtained by the RST filter was poorly segmented because of the negative effect of the contrast-enhanced blood vessels and the air, and it was removed by SVM. However, the candidate region obtained by the ITRST filter covered most of the lymph node region, and it was classified as a lymph node. The ITRST filter prevented the negative effect of surrounding regions and contributed to accurate classification. Although the candidate region obtained by the Hessian filter was a little smaller than that of the ITRST filter, it was also properly classified.

False negatives still produced
Some lymph nodes were still missed by the ITRST filter, as shown in the FROC curves in Fig. 6. This is because they tend to be sandwiched by extremely high or low intensity regions. For instance, the lymph node shown in Fig. 8(c) could not be detected by the ITRST or the RST filters. It was sandwiched by the contrast-enhanced blood vessels and the air region, and a very small amount of soft tissue was touching the lymph node. Most gradient vectors around it were not summed into the ITRST, so the magnitude of eigenvectors did not follow the condition of the bright blob-like structure. In contrast to the ITRST or RST filters, the Hessian filter did not strongly suffer from intensity differences between lymph nodes and neighboring regions. The small candidate region detected by the Hessian filter was finally classified into lymph node class after the FP reduction process. It remains as future work to improve the ITRST filter such that it can segment lymph nodes surrounded by extremely high or low intensity regions.
The lymph node shown in Fig. 8(d) was initially detected by the ITRST filter, but it was removed by the SVM classifier. To prevent generating such false negatives, we will improve the classification accuracy by introducing deep learning techniques in future work. Note that the candidate region of the ITRST filter was properly classified as a lymph node with w F ¼ 0.025.

Promise for application to segmentation
As shown in Fig. 8(e), some lymph nodes are detected by all filters (ITRST, RST, and Hessian). However, the ITRST filter produced more proper segmentation results of lymph nodes than other filters. In the future, the ITRST filter can be improved for application to segmentation of lymph nodes, not only for detection. This will assist radiologists in measuring the size and shape of each lymph node.

Lung Area Segmentation
We have extracted mediastinum regions from CT volumes by extracting lung regions. There is some possibility to fail in lung region segmentation in a pathological lung with cancer, as shown in Fig. 9. However, that does not affect the subsequent processes since lung segmentation is only for obtaining the mediastinum region sandwiched between the lungs. The lung cancer region is merged into the target region.

Conclusions
In this paper, we proposed a mediastinal lymph node algorithm based on the ITRST filter. The conventional RST filter cannot detect some lymph nodes because of neighboring regions of lymph nodes, which have extremely high or low intensities. We proposed the ITRST filter by modifying the RST filter to prevent such negative effects by introducing knowledge about the intensity range of the detection target. It allows us to exclude neighboring regions for computing the filter response and increase the detection rate of lymph nodes. We evaluated the efficacy of the ITRST filter by applying it to both an artificially generated volume and chest CT volumes. In an experiment on synthetic data, the ITRST filter produced high responses in the spheres neighboring bright or dark square poles, while responses of the RST filter were very low or zero. These results showed that the ITRST filter can prevent the negative effect caused by such neighboring regions, in contrast to the RST filter.
Furthermore, experimentation with real clinical images for mediastinal lymph node detection showed that the ITRST filter outperformed the RST filter. This is because most of the mediastinal lymph nodes adjacent to air or contrast-enhanced blood vessels in the chest CT volumes can be detected using the ITRST filter. The detection performance after FP reduction is also better than the RST filter. The proposed ITRST filter could potentially be used for detection of other organs or tissues of interest in medical imaging.

Disclosures
There is no conflict of interest in this paper. Kensaku Mori is a professor of Graduate School of Information Science, Nagoya University and the director of Information Technology Center of Nagoya University and MICCAI fellow. He received MEng in information engineering and PhD in information engineering from Nagoya University in 1994 and 1996, respectively. He was also involved in many international conference organizations, including SPIE Medical Imaging, CARS, and MICCAI, as a general chair or program committee members.
Biographies of the other authors are not available.