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Bull. Soc. Photogr. Imag. Japan. (2017) Vol. 27 No. 1: 7–12

Original Paper

Volume Measurement of the Leg by Using Kinect Fusion for Quantitative Evaluation of Edema
Kaoru Kiyomitsu*, Akihiro Kakinuma**, Hiroshi Takahashi**, Naohiro Kamijo**, Keiko Ogawa***,Norimichi Tsumura*

Abstract:I n this paper, we propose to evaluate the edema of a leg by volume measurement using Microsoft Kinect. The volume is calculated
from a measured point cloud by our improved voxelization technique. Additionally, the depth camera of Microsoft Kinect is feasible
for use in home care systems due to its very low cost compared with other depth cameras. In an experiment, we measured the legvolumes of three healthy subjects in the morning and evening for three days. For each subject, the mean volume values were obtained
from five measured values. The increase of volume was confirmed in all experiments from morning to evening. These experimental
results meet the physiological expectation. Because the changes in leg volumes are greater for a patient with serious edema than for ahealthy person, we conclude that the proposed system can appropriately evaluate leg edema.

Key words:

edema, health monitoring, Kinect, point cloud, volume measurement

1. Introduction
Edema is swelling of the leg, hand, or face due to the accumulation of body serum. Many healthy people have minor
edema. However, some patients who are treated for cancer
or other illnesses have serious edema. To relieve edema,
continual long-term treatments such as massage or exercise
are needed1-2). Simultaneously, quantitative evaluation of
edema can help motivate patients by preventing the boredom of extra treatments.
Volume measurement of a limb is one of the practicalmethods for quantitative evaluation of edema3). The volume
of a limb increases or decreases according to the volume
change of body serum. In other words, an increase or decrease of volume indicates whether the edema is getting
worse or better. Currently, hospitals use a measuring tape
to measure the circumference of a patient’s limb instead ofits volume value3-5). Although this method is easy, it has poor
repeatability and accuracy.
In recent years, methods using a depth camera have been
proposed to obtain the leg volume6,7). The first step of thesemethods is acquisition of a 3D model of a subject’s leg. A 3D
model for the entire leg can be obtained by two types ofimaging procedures. One is performed by moving a single

camera around the subject’s leg. The other is performed by
setting several cameras around the subject’s leg. The next
step is a calculation of the volume by computations on the
3D model. Some previous methods have used a depth camera for the volume measurement. However, it is not feasible
to use a depth camera as a home care system because mostdepth cameras are too expensive.
In this paper, we propose to evaluate the edema of a leg
by using Microsoft Kinect for the volume measurement. Kinect, shown in Fig. 1, is an RGB-D system that can obtain adepth map as well as an RGB image8). Kinect is already wellknown throughout the world as a game controller. “Kinect
for Windows SDK” contains Kinect Fusion9,10), which is anapplication to easily obtain a 3D model.
The purpose of our study is the quantitative evaluation of
edema. Therefore, it is necessary to precisely measure the

Received 26th, April 2017; Accepted 9th, June 2017.

*Graduate School of Advanced Integration Science, Chiba University
1-33, Yayoi-cho, Inage-ku, Chiba-shi, Chiba, 263-8522 Japankiyomitsu@chiba-u.jp**Ricoh Company, Ltd.

***Department of Japanese-Traditional (Kampo) Medicine, Kanazawa University Hospital

Fig.1 Microsoft Kinect

8Bull. Soc. Photogr. Imag. Japan. Vol. 27 No. 1 (2017)

amount of change of the volume. We constructed a reproducible measurement system whose output value has lessdispersion in repetitive measurements. In this paper, weonly measured the volume of a leg because the leg shows ahigher degree of edema compared to the rest of the body.
In Section 2, we describe our proposed method to measure the volume of a leg. In Section 3, we describe the experiments and results of this study. In Section 4, we present
the conclusion of our study.

2. Proposed Method
In this study, we measure the volume of the leg by using
Kinect. First, we scan the leg with the Kinect Fusion technique, and then we obtain the point cloud of the leg in realtime. Second, the point cloud is processed to remove unnecessary parts. To decide the measurement range, we set thecoordinate axis along the leg length. Finally, the volume value is obtained by voxelization.
2.1 Acquisition of a Point Cloud
We use Kinect Fusion9,10) for acquisition of the point cloudof the leg. Kinect Fusion is one of the applications containedin Kinect for Windows SDK. We can obtain a point cloud inreal time by scanning around the leg with a Kinect sensor,
which used as a hand-held depth camera. Fig. 2 shows ascreenshot of the recording procedure by Kinect Fusion. We
can move the Kinect sensor while looking at the reconstructed point cloud on the monitor in real time, so that it ispossible to scan the leg while checking for errors, such as a
missing surface or a hole of the point cloud.
2.2 Removal of Unnecessary Parts
The point cloud obtained by Kinect Fusion includes not
only the leg but also other parts, such as a chair and thefloor. We need to remove these unnecessary parts. First, weremove the points except the leg and the floor. For thisprocedure, we use MeshLab11), which is free software forediting a 3D model. With this software, we can remove,translate, rotate, and fill a hole of a point cloud. Fig. 3 shows
the result of removal of a chair by MeshLab. After this procedure, the floor still remains. It is difficult to remove floor
parts from a point cloud because the arch of the foot is attached to the floor. Therefore, we remove the floor parts

Fig.2 Screenshot of recording by Kinect Fusion

automatically, as shown in Fig. 4, by a plane model segmentation algorithm. Simultaneously, we translate and rotate
the point cloud to reset its coordinates by setting the XZ
plane along the floor. Thus, the Y-axis becomes the length ofthe leg. This is the preprocessing step for the following procedure, which is setting the measurement range.
2.3 Setting the Stable Range of the Measurement
We decide the stable range of the measurement according
to the height of the leg. The purpose of our study is themeasurement of the change of volume; therefore, it is important to set a stable range of the measurement. In thismethod, we extract the intended point cloud by masking
along the height of the leg. As stated in the previous subsection, the direction of the leg length corresponds to the Y-axis due to preprocessing. We carry out masking with theY-coordinate to set the range of the measurement.
2.4 Calculation of the Volume
The point cloud obtained by Kinect Fusion has errors
such as a hole or a missing surface. These errors tend tooccur on the back side of the leg. Additionally, undesired
points which should be treated as noise may easily appear
on the inside of the surface. For this reason, it is necessary
to consider these errors. Although some techniques for calculation of volume value are proposed, most of these methods are disabled by holes or noise. In this study, we use a
simple and stable method to correct these errors. Our method is modified method based on the previous methods proposed by Garcia et al.12) and Saito et al.13).
Here, we discuss the method proposed by Garcia et al.12).
They proposed the method of voxelization. First, they conducted voxelization on the surface of the point cloud. As
shown in Fig. 5, each point in the point cloud is assigned to
one voxel. Second, we extract the voxels that contain points.

Fig.3 Removal of unnecessary parts by MeshLab

Fig.4 Removal of the floor by the plane model segmentation algorithm

Kaoru Kiyomitsu, Akihiro Kakinuma, Hiroshi Takahashi, Naohiro Kamijo, Keiko Ogawa, Norimichi Tsumura

Fig.5 Voxelization of the surface of the point cloud. White voxels present thesurface of the point cloud, and black voxels present the blank region.

Volume Measurement of the Leg by Using Kinect Fusion for Quantitative Evaluation of Edema

9

Fig.8 Voxelization of the volume proposed by Garcia12)

Z
Z=2
Z=1
Z=0
Fig.6 Sliced planes along the Z-axis

Fig.7 Voxelization of the surface proposed by Garcia12)

In Fig. 5, the voxels that contain points are white, and theother voxels are black. The following procedure is implemented on planes sliced along the X-axis, Y-axis, and Z-axis.
Fig. 6 shows an example of the sliced planes along the Z-axis. The planes thus are each XY planes. Third, the surface ofthe connected voxels is constructed by interpolation between each white voxel. As shown in Fig. 7, each whitevoxel is chained to its nearest voxels, and, voxelization of
the surface is complete. The next procedure is voxelizationof the volume; voxelization is performed row by row of thevoxels. In each row, the black voxels between two whitevoxels become white. Fig. 8 shows the result of voxelizationof the voxels on the surface of Fig. 7. If the row lies on the
concave and convex curve line such as the toe, it may havemore than three white voxels. In such cases, the black voxels between the first white voxel and the second white voxel become white, then the black voxels between the third
white voxel and the fourth white voxel become white, andso on. This procedure assumes that every row has an evennumber of white voxels. However, in the case of the rows
having an odd number of white voxels, the black voxels
between the last white voxel and the rightmost voxel become white. This voxelization is implemented on all rows ineach sliced plane.

Fig.9 Voxelization of the volume proposed by Saito13)

Fig.10 Voxelization of the surface of the point cloud that has interior points

Next, we discuss the method proposed by Saito et al.13).
Saito’s method is based on Garcia’s method. Saito’s methodof voxelization for the surface is the same as Garcia’s, but
voxelization of the volume is implemented by labeling thevoxels. Fig. 9 shows an example of voxelization of the volume. The volume of the point cloud is obtained by counting
the number of white voxels.
Finally, we propose the improved voxelization technique,which is effective for the point cloud obtained by Kinect
Fusion. The previous two methods assume that all of thepoints are placed on the surface of the point cloud; in otherwords, no points are inside the surface. However, the point
cloud obtained by Kinect Fusion tends to have interior
points. Fig. 10 shows an example of the surface voxelizationin the case that some interior points are present. In thiscase, undesired lines and/or untreated holes may easily appear. It is noted that the surface voxelization is implemented on a sliced plane along only the Y-axis, whose direction is
the same direction as the leg length.
Volume voxelization is also affected by the interior points.
Fig. 11(a) shows the procedure of Garcia’s volume voxelization on the surface in Fig. 10. Gray voxels present the volume voxelized voxels which turn from black to white. Fig.
11(b) shows the result of this volume voxelization. In this10

Bull. Soc. Photogr. Imag. Japan. Vol. 27 No. 1 (2017)

case, the rows having an odd number of white voxels cause
an error. For this reason, our method detects the last whitevoxel in the scanning and keeps voxels as black after thelast white voxel is detected in the second scanning for eachrow. Fig. 11(c) shows the result of our volume voxelizationof Fig. 10. Our improvement prevents an error due to interior points. It should again be noted that the volume voxelization is implemented on a sliced plane along only the
Y-axis.
The next step is filling any hole of the point cloud. We
propose a method based on Saito’s method. Saito’s method
implements labeling on the assumption that a blank region
that is not a leg region includes the voxels on the upper-leftcorner and the upper-right corner of the sliced plane, namely, the leftmost voxel and the rightmost voxel of the top row.
Sometimes the point cloud obtained by Kinect Fusion has
neither an upper-left corner nor an upper-right corner.
Therefore, we add an outer frame to the sliced plane in advance, as shown in the left figure of Fig. 12. Due to the outer
frame, a blank region can constantly include an upper-leftcorner and an upper-right corner. Then, we can turn blackvoxels that are not in the blank region into white voxels. As
mentioned above, the surface and volume voxelizations are
implemented on a sliced plane along only the Y-axis. In contrast, the labeling method is implemented on a sliced plane
along the Y-, X-, and Z-axes. The right figure of Fig. 12 is theresult of our proposed voxelization. The volume value canbe calculated by the voxel number and the size of the unit
voxel.

3. Experimentsubjects’ legs from morning to evening.
3.1 Measurement with a Leg Model
We first measured the volume of a leg model that is notaffected by motion of the leg. This motion could influence
the accuracy, and so we use motionless model to verify theaccuracy. Fig. 13 shows a picture of the leg model. The operator held the Kinect sensor in the hand and scanned threetimes around the model leg. The distance between the Kinect sensor and the leg was kept at approximately 35 cm.
This procedure was repeated five times, so that we obtained
five point clouds for each measurement and then we calculated the volume values. In our study, the unit voxel was
determined to be a cube with a side of 1 mm.
We measured the volumes of the five point clouds within
the range from 0 cm to 30 cm to verify the accuracy. We
used the coefficient of variation as a parameter of the dispersion of the measured volume. The coefficient of variation is
calculated as the ratio between the standard deviation andthe mean value of the volumes. The smaller the variation ofcoefficients becomes, the better the accuracy. The coefficient of variation within the range from 0 cm to 30 cm was
1.04%.
Next, the range of the measurement was divided into four
regions. Fig. 14 shows the coefficients of variation of the fourparts. The range from 0 cm to 5 cm has the largest dispersion due to occlusion caused by the lumpy sole of the model’s foot. Therefore, we recalculated the coefficient of variation within the range from 5 cm to 30 cm, excluding therange from 0 cm to 5 cm. The result of the recalculation was
0.70%, which is a decrease compared with the result from 0

In this study, first we verified the accuracy of our method.
Second, we measured the change of the volume of healthy

(a)

(b)

(c)

Fig.11 Volume voxelization of the point cloud that has interior points. (a)shows the procedure of Garcia’s method12). Gray voxels present thevolume voxelized voxels which turn from black to white. (b) shows
the result of Garcia’s method12). (c) shows the result of our method.

Fig.13 Leg model with the scale of the height from the floor

Fig.12 Volume voxelization of our method

Fig.14 Coefficients of variation of five measured values in four measured
regions of the leg modelKaoru Kiyomitsu, Akihiro Kakinuma, Hiroshi Takahashi, Naohiro Kamijo, Keiko Ogawa, Norimichi Tsumura

cm to 30 cm. We found that we can measure the volume of
the leg stably by removing the region of the foot in the caseof the leg model.
3.2 Measurement with a Human Leg
We performed an experiment for the human leg by thesame procedure used for the leg model. The subjects were
two males and one female. Every subject was a healthy
adult. Each subject sat on a chair with a bare right leg,
which was posed to be a right angle with respect to thefloor. The experimental condition is shown in Fig. 3. In thisexperiment, we measured the volume of the leg in the morning (AM) and evening (PM) for three days. In each measurement, the mean value and the coefficient of variation wereobtained from the five measured values. We divided the leg
into four regions, as was done for the leg model discussed in
the previous section. In this section, the boundaries of theregions were (A) ankle, (B) boundary between ankle andcalf, (C) thickest point in the calf, and (D) point below theknee, as shown in Table 1. Fig. 15 shows the result of subject A for the four regions: 0–(A), (A)–(B), (B)–(C), and (C)–(D).
This result shows that region (A)–(B), namely, the region of
his ankle, tends to have the largest dispersion due to an occlusion caused by the complex shape of the subject’s ankle
bone. The same tendencies were seen in the results of theother two subjects. Fig. 16 shows the comparison between
the result with an ankle and the result without it for all
three subjects. Blue bars show the coefficient of variation
with the ankle and red bars show the coefficient of variation
without the ankle. Graph (a), (b) and (c) show the results ofSub. A, Sub. B and Sub. C, respectively. This graph showsthat the coefficients of variation can be reduced by removing the ankle from the measurements. The coefficients ofvariation of the volumes of human legs were within therange of 0.5%–2.5%. The coefficients of variation reflected
the influence of leg movement during scanning. The difference of the region removed was the difference of shape between the leg model and the human model.

Volume Measurement of the Leg by Using Kinect Fusion for Quantitative Evaluation of Edema

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3.3 Measurement of the Volume Change for Healthy Subjects’
Legs
In this experiment, we measured the volume of the leg forthe three healthy subjects in the morning (AM) and evening(PM) for three days. Fig. 17 shows the result of the measurement. Red lines show the mean value of five measured volume values which are shown by five blue points in morningand evening. Graph (a), (b) and (c) show the results of Sub. A,
Sub. B and Sub. C, respectively. Each volume was calculated
from the total sum of the three parts of the leg, except for
the region (A)–(B) in Table 1. During this experiment, every
subject was carrying out office work. It is known that theedema occurs on the human’s leg when the subjects are
doing office work; in other words, they are sitting in thechair for many hours. The edema causes the increase of thevolume of the leg because the body serum is accumulated in
the leg. Therefore we had expectation for office workers
that the volume of the leg is increased in the evening compared in the morning. It is noted that the increased volume
was confirmed in all experiments from morning to evening.
Therefore, we can conclude that our experimental results
meet the expectation for office workers. Our system is prac-

(a)

(b)

(c)

Fig.15 Coefficients of variation of the five volume values in four parts ofsubject A’s leg. This experiments were conducted six times.

Fig.16 Coefficients of variation of the five measured values of the leg ofsubjects. Blue bars show the coefficient of variation with the ankle
and red bars show the coefficient of variation without the ankle.
Graph (a), (b) and (c) show the results of Sub. A, Sub. B and Sub. C,
respectively.

12Bull. Soc. Photogr. Imag. Japan. Vol. 27 No. 1 (2017)

Table 1 Heights of four selected points from the floor (cm)(A)ankle(B)boundary between ankle and calf(C)thickest point in calf(D)point below kneeSub. A(Male)

Sub. B(Male)

Sub. C(Female)

20

21

20

40

35

8

33
37

10
34

8

27

(a)

(b)

evening for three days. The coefficients of variation of thevolumes of the human legs were within the range of 0.5%–
2.5%. Our experimental results meet the expectations of
their life style. Our future work is measurement of a leg ofa patient with edema. Additionally, we aim to measure a legof a patient lying with a bended knee. This technique will
make it possible to deal with bedridden people.

References(c)Fig.17 Results of the experiments for the three healthy subjects’ legs. Redlines show the mean value of five measured volume values which are
shown by five blue points in morning and evening. Graph (a), (b) and(c) show the results of Sub. A, Sub. B and Sub. C, respectively.

tical for measuring leg edema because the changes in leg
volumes are greater for a patient with serious edema thanfor a healthy person.

4. Conclusion
In this paper, we proposed a method of volume measurement using Microsoft Kinect for the evaluation of a leg edema. We obtained a point cloud of the leg by the Kinect Fusion technique and calculated its volume. We measured thevolume of the leg of three healthy subjects from morning to1) K. Katherine, A. Fleischer, and G. Yosipovitch, J. Am. Acad. Dermatol.,
59(2), 324-331 (2008).
2) Y. Ogawa, Annals of Vascular Diseases, 5(2), 139-144 (2012).
3) H.N. Mayrovitz, N. Sims, and J. Macdonald, Advances in Skin &Wound Care, 13(6), 272-276 (2000).
4) A. Tiwari, K.S. Cheng, M. Button, F. Myint, and G. Hamilton, Arch.
Surg., 138(2), 152-161 (2003).
5) C.W. Tan, F. Coutts, and C. Bulley, Physiotherapy, 99(3), 247-251(2013).
6) M. Hirai, Y. Nukumizu, H. Kidokoro, et al., Skin Res. Technol., 12(1),32-35 (2006).
7) N. Cau, S. Corna, V. Aspesi, et al., J. Nov. Physiother, 6, 303, (2016).
8) Microsoft, "Kinect for Windows Sensor", https://msdn.microsoft.com/
en-us/library/hh855355.aspx (Oct.15, 2016)9) S. Izadi, D. Kim, O. Hilliges, et al., UIST 2011, 559-568 (2011).
10) R.A. Newcombe, S.I Zadi, O. Hilliges, et al., ISMAR 2011, 127-136(2011).
11) P. Cignoni, M. Callieri, M. Corsini, et al., Sixth Eurographics Italian
Chapter Conference, 129-136 (2008).
12) F.D. Garcia and B. Ottersten, Proceedings of the International Conference on Pattern. Recogn., 2757-2762 (2014).
13) K. Saito, O. Sakai, and S. Miyagi, Forum on Information Technology,
14(3), 143-146 (2015) [ in Japanese ]