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Proceedings of 2014 Zone 1 Conference of the American Society for Engineering Education (ASEE Zone 1) MR Image Assisted Drug Delivery in Respiratory Tract and Trachea Tissues Based on an Enhanced Level Set Method Mohhammad Daneshzand, Reza A. Zoroofi, and Miad Faezipour, Member, IEEE capacitance vessels of nasal mucosa [1]. Antigen challenge Abstract— In medical diagnosis and therapy, finding an can be moderated by some drugs such as histamines and
appropriate method to evaluate the effect of various drugs is arachidonic acid metabolites. Histamines and antigens effect
crucial. There are several ways to qualify a drug for a specific
on nasal mucosa has been evaluated by some previous disease and one way is through medical image analysis. This methods [2]. A new method for drug evaluation on various
process varies with the tissues we want to analyze and the tissues is through image analysis and numerous works have
imaging technique that is employed. For hydrous tissues such as
developed this method [3],[4],[5]. nasal and trachea, Magnetic Resonance Imaging can be helpful
Image guided drug delivery is a concept in which the for further evaluations. Trachea can be challenged by an antigen
specific effect of a drug is evaluated through medical images. which will increase both nasal vascular permeability and The imaging techniques are consisted of MRI, CT, PET and
intranasal pressure. Another effect of antigen challenge into Ultrasound [6], [7]. Another issue in medical image analysis
nasal cavity which may cause nasal blockage, is swelling of nasal
for drug evaluation is how to process the image in order to achieve the maximum similarity to manual segmentations of mucosa and a decrease in nasopharyngeal airway. In this paper,
doctors. Hence, finding an optimum method for processing is we study the effect of an antihistamine drug on swelling of a critical task. We suggest that our enhanced image processing
mucosa. This antihistamine is called Azelastine and is injected to
technique should be applied for the tissue we want to analyze guinea pig to evaluate the swelling changes of nasal and trachea
[8], [9], and [10]. In this article, we combine some mucosa. After 20 minutes of injection, a MR image of the segmentation algorithms, offering a new way to maximize the
motionless animal is taken and this imaging will continue for 30,
efficiency of drug evaluation which can also be compared to 40, 50 and 60 minutes from injection. Due to the ambiguous the physician's results of drug evaluation [11], [12], [13].
nature of respiratory tract, finding a precise method for
The paper is organized as follows. In section II, we discuss processing has useful results. Watershed algorithm has two image processing techniques. In section III, a fusion of
widespread function in medical images but its defects in these methods is presented for evaluation of MR images of
segmentation can be modified by different methods. An enhanced
nasopharyngeal. We also define a modified active contours level set method is used here; a nonparametric active contour for
algorithm to detect trachea. We implement our proposed nasal and trachea detection. This automatic image segmentation
method on MR images of guinea pig to evaluate the effect of and tissue detection can help physicians evaluate the effect of a
the Azelastine drug in the same section. The results of this specific drug from medical images.
method is compared with manual processing and previous works. Section IV is devoted to defining a 3D model of KeywordsNasal and Trachea, Magnetic Resonance Imaging, trachea and trachea airways in order to have a better
Azelastine, Watershed Algorithm, Level Set Method.
comprehension of the effect of our desired drug. Finally, in section V, we discuss the whole idea of our method on image guided drug delivery issues and some viewpoints for future works are presented. One of the most important symptoms of allergic rhinitis is nasal blockage. Nasal blockage happens when the nasal mucosa starts to swell and this is a reaction to antigens. The II. RELATED WORK antigen challenge will increase vascular permeability dilate the A. Watershed Algorithm The watershed idea has been introduced in 1979 by S. M. Daneshzand is with the Department of Computer Science & Beucher and C. Lantuéjoul and its concept in image Engineering, University of Bridgeport, CT, 06604, USA (e-mail: mdaneshz@my.bridegport.edu). processing deals with how a drop of water falls through R. A Zoroofi is with the School of Electrical and Computer Engineering, topographic relief until it finally reaches to a minimum. When University of Tehran, Tehran, Iran. (e-mail: zoroofi@ut.ac.ir). water fills local minimum, many regions will appear and by M. Faezipour (corresponding author) is with the Departments of Computer increasing water, these regions may merge which at this point, Science & Engineering and Biomedical Engineering, University of Bridgeport, CT, 06604, USA (phone: 203-576-4702; fax: 203-576-4765; e- some dam can prevent this merging. These dams are mail: mfaezipo@bridgeport.edu). 978-1-4799-5233-5/14/$31.00 2014 IEEE watershed lines [14], [15]. The main procedure of watershed Where H(f) is a Heaviside function [20]. Consequently the algorithm first happens with a selection of a point, then variable level set function f should satisfy Equation 6. neighboring pixels of this point are inserted into a priority c + c query with a priority level corresponding to the gray level of v f the pixel. The pixel which has the highest gray value will be c + c selected and labeled with its neighbors that have the same max( I(x) − label. This will continue until all pixels get into priority query In this equation, the first term is a Signed Pressure Force (SPF) and is constructed based on the above equations [20]. This term can control the direction of contours so they can B. Level Set Method shrink outside and expand inside of a desired object in image I. Also, v represents the speed of contours. The enhanced level The level set method which was presented by Osher and set method is a combination of watershed algorithm as a Sethian in 1987, is a simple method for computing and preprocessing step and the new level set method which is analyzing the motion of the interface in two or three summarized as follows: dimensions. Level sets are used to implement force curves. These curves are represented as Equation 1. If a curve C moves in a normal direction with speed v, the level set • Implement a watershed transform on image function F will satisfy Equation 2 [18], [19]. • Regionally segment image with Watershed algorithm and highlight the specific segmented parts C={(x, y) f(x, y) =0} according to Equations 4 and 5 • Extract the level set function in 6 ∂ =∇ f • If f>0, then f=1, else f=-1 and a local segmentation will One of the most important problems of level set is that it needs to be re-initialized which may cause the side effect of In our MR set of images, there are some slices containing moving the zero level set away from its interface. In addition, nasal respiratory tract parts. Due to complexity of these parts, the re-initialization step is a highly costly and very time we applied an active contours method based on local energy consuming operation. A new way to solve this problem is fitting [23]. Therefore, we define an energy function as presented in [20] that uses a Gaussian filter to make the level set function regular. E LIF (φ) = ∫ Ω (X ) [F] − I (X ) dx III. PROPOSED METHOD Our method here uses a preprocessing step in order to signify the boundaries of the image. This step is consisted of a where I(x) and represent our image and our modified distance transform on the image for using watershed algorithm image with a Gaussian window, respectively. Then, we used a which will segment and highlight the boundaries of the level set function with a modified Dirac functionδε (φ ): processed image. After this step, a level set method can be implemented on the new image, however, we use a new level ∂ = (I ILFI ) set which is devised by Kaihua Zhang in [20]. According to Zhang's work, two constraints and which are average At this point, we can use a same algorithm as the previous one intensities inside and outside of the contour, should be with our new level set function: evaluated by minimizing the energy function such as below • Extract the level set function in Equation 8 1 ∫ I x c 2 dxI x c 2 dx • If >0, then =1, else =-1 and a local segmentation Therefore, we have: The main goal of this article is to find out how a drug can ∫I(x H ). ( f )dx c ( f ) make changes in trachea and nasopharyngeal airways. Data ∫H( f )dx are gathered by University of Osaka in Japan. The dataset consisted of 5 sets of Nasal and trachea MR images. Each set has 29 slices of images and represents a specific time after the ∫I(x 1 ).( − H ( f ))dx c ( f ) drug was injected into the subject. Image matrix of each data ∫ 1(− H( f ))dx set is 256×256×29 with voxel size of 0.14×0.14×1mm .




Fig. 1.Nasopharyngeal airway (green arrow) and trachea mucosa (red arrow) of guinea pig. The subject in these data are guinea pigs which are sensitized by aerosol inhalation of antigen and this antigen challenge into Fig. 2. Block diagram of our proposed method for drug evaluation in trachea the nasal cavity increased both nasal vascular permeability and and nasal respiratory tract. intranasal pressure. Another effect of antigen challenge into nasal cavity which may cause nasal blockage is swelling of sequences of the images. Second, the total effect of Azelastine nasal mucosa and a decrease in nasopharyngeal airway [22]. should be evaluated and compared against previous works Here, we study the effect of Azelastine drug, an antihistamine on swelling of mucosa. A slice of guinea pig's nasopharyngeal and it's mucosa is shown in Figure 1. Antigen would swell the mucosa (red arrow) and antihistamine would inhibit that (green arrow). B. Method Execution In order to implement our method, we should follow the block diagram in Figure 2. In this block diagram, first we must insert our data for a preprocessing step which is a watershed method implementation. In the next step, level set algorithm is applied to these data and results of trachea area segmentation is extracted. With these results of segmentation, we can calculate the trachea area for 20, 30, 40, 50 and 60 minutes after Azelastine injection. This process will continue for every slice of a MR image. For example, if a data set has 6 sets of 10 slices of MR images, we should implement our method on every set (which is related to a specific time after injection of Azelastine) and also on every slices of the desired set. After using our method, and finding nasopharyngeal airway area of each slice of an image, we will build a 3D model of nasopharyngeal airway area and its total volume will be extracted. After injection of Azelastine, 5 sets of images were taken. The first set had been taken after 20 minutes of injection, while the second set after 30 minutes, the third set after 40 minutes, the fourth set after 50 minutes, and the last set after an hour of injection were taken. Therefore, in order to evaluate Fig. 3.The Enhanced Level set method for finding nasopharyngeal of guinea pig. (a) Original image. (b) Watershed regional segmentation. (c) Watershed the effect of Azelastine, we should consider two points. First, region and boundary finding. (d) Level set after70 iterations. (e) Final image the trachea variations should be calculated through time with specified trachea area.


TABLE I AZELASTINE EFFECT IN NASOPHARYNGEAAL AIRWAY AREA AND VOLUME: A COMPARISON OF OUR PROPOSED METHOD WITH PREVIOUS STUDIES.
Trachea after antigen inhalation Azelastine effect in Trachea area ( 70.62 74.87 77.9 79.05 81.4 9.95 70.2 72.35 75.9 76.6 77.82 13.9 67.43 70.4 75.09 79.74 82.69 Fig. 4. Trachea area ( ) before and after 20, 30, 40, 50 and 60 minutes of injection of Azelastine.
As mentioned in the previous section, we would implement trachea area from a slice of a MR image. These methods are our Enhanced level Set method on our MRI data to segment and find out it's efficiency for finding nasopharyngeal airway enhanced level set, region growing and manual segmentation. of guinea pig. Figure 3 shows the steps of our method as discussed in the proposed method section. At first, the The exact area of trachea is shown in Table I with watershed algorithm is applied on our image shown and the comparison to manual detection of trachea area and the simple result of local segmentation can be seen in Figure3.b. After Region Growing method. Table I shows that our method this step, the boundaries are highlighted and the new image is produces similar results to manual detection of airway areas ready for the level set method (Figure 3.c). At the beginning of while Region Growing method has sporadic results for the last evolving, the level set function, images. In addition, in this table, the total effect of Azelastine according to Equations 4 and 5. Then, by using Equation 6, after 60 minutes is compared to previous works [22]. Note that the level set function is calculated (Figure3.d), and finally by the actual area of trachea before any inhalation is 90. 4 setting the positive level set to 1 and otherwise to -1, the local segmentation of nasopharyngeal will be achieved as can be seen in Figure 3.e. In this task, the contours are stopped after According to Table I and Figure 4, we can say that our 70 iterations as can be observed in Figure 3.d. This is while method has results with most similarity to Yamasaki work more iterations would be time consuming, and less iterations [22]. It is also notable that the Enhanced Level Set method has would lead to global segmentation. faster results than manual segmentation results, especially By finding the trachea area now, we can estimate the when we face large amount of data for processing. An variation through time, total variation and total volume change important point from these results is that various methods had which is discussed above. proven that after some time has passed, the Azelastine will First, we study the effect of Azelastine after injection as reduce and quell the swelling effect of antigen. In Table, I the time goes on. The area of trachea after injecting antihistamine trachea area is increasing for consecutive images through time will vary and these changes can be seen in Figure 4. In this and this increase is proven with different methods. Moreover, figure, the trachea area size after 20 minutes of Azelastine we can realize that this increase is a demonstration for injection until 60 minutes after injection is depicted. As can be decrease in swelling of nasal mucosa which is a result of seen in Figure 4, three different methods are used to find antihistamine administration.


Fig. 5.Segmentation of nasal respiratory tract airways based on local image fitting [23].
Fig. 6. 3D model of trachea volume of guinea pig before injection of Azelastine and after 20, 40 and 60 minutes of injection (from right to left). Using active contours based on local image fitting allows us to level set method to find trachea areas of the same image and segment nasal respiratory tract parts of our images which is again a 3D model is constructed based on these segmentation shown in Figure 5. After detecting nasal respiratory tract results. Finally, we overlap these two 3D models to find out an through continuous slices of MR images, we can create a 3D intuitive concept of nasopharyngeal volume and also to model of the trachea. compare the model which is based on our enhanced level set method with the model rooted from manual segmentation. Figure 7 represents this concept, and as it can be seen, our IV. 3D VISUALIZATION method has meaningful overlaps with the manual segmented method. Another way to compare our results with previous works is The total volume of trachea area before and after injection to find a three dimensional model of trachea area. This way of Azelastine is shown in Table I for different methods. This we can evaluate the total effect of Azelastine drug and figure volume can help the physician to understand the effect of out how the volume of trachea area will vary through time Azelastine in the trachea area. In this table, the total volume sequences. Figure 6 shows trachea volume in three different before Azelastine administration (antigen inhalation) and after times. The first trachea volumetric model is before injection of 1 hour of Azelastine administration is calculated. Note that the Azelastine, and the two others are after 20, 40 and 60 minutes total volume of trachea has an increase before and after of drug injection. With this 3D model, we can estimate the Azelastine administration which shows that swelling of nasal volumetric changes of trachea in order to evaluate the effect of mucosa has a decrease after the injection of Azelastine. The same procedure can be used in order to create a 3D Volumetric comparison of trachea area can be done by model of nasal respiratory tract airways based on our overlapping the results of our proposed method and manual segmentation results for each slices. Therefore results of our volumetric model. For this aim, first a 3D model is detected nasal respiratory tract area from consecutive slices constructed based on manual segmentation of trachea areas can be visualized into a 3 dimensional model as shown in from slices of the MR image. After that, we use our enhanced


In this paper, we presented a new fusion of segmentation methods for evaluating the trachea area. Our method combines active contour algorithm with the watershed method. The aim of this work is to find out how an antihistamine drug can prevent nasal mucosa swelling. This work can help the physician to define and query the effect of a drug from medical images and thus they can anticipate the probability of allergic rhinitis. This diagnosis is extremely crucial, because the developed forms of allergic rhinitis could lead to asthma. The conclusion process in this work is based on the sequential variations in the trachea area and its total final area after histamine drug (Azelastine) injection. In addition, we extracted a 3 dimensional model of trachea area in order to observe the total volume changes due to Azelastine administration. 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