Journal of Clinical Imaging 28 (2004) 135–137

Animal study of renal volume measurement on abdominal

CT using digital image processing

Preliminary report

Jong Chul Kim*

Department of Diagnostic Radiology, Chungnam National University Hospital,

640 Daesa-dong, Jung-gu, Daejeon 301-721, Republic of Korea

Received 28 February 2003

Abstract

On abdominal computed tomography (CT) scans of two anesthetized Landrace pigs with 3-mm slice interval, kidneys were extracted

by two steps of digital image processing: automatic segmentation in a single slice using the character of pixel distribution of the kidney,

and removal of residual noise using batch processing with folding method. The measured renal volume by this method was compared

with the actual renal volume obtained by means of three consecutive water displacement measurements on surgically removed kidneys.

The mean percentage error was 2.9% between mean renal volume measured by our image processing (78.3 and 67.4 cm3 for right

and left, respectively) and mean actual renal volume (80 and 70 cm3, respectively). Renal volume measurement on abdominal CT in

pigs using this digital image processing was feasible and reliable with negligible error rate in comparison with actual renal volume.

D 2004 Elsevier Inc. All rights reserved.

Keywords: Kidney experimental studies; Kidney image processing; Kidney CT

1. Introduction

Many clinicians or patients are interested in the size of the

kidney and significant change of renal volume in clinical

fields. Enlargement of the kidney is caused by various

conditions or disorders; such as hyperplasia, associated

agenesis or hypoplasia on the opposite site, compensatory

hypertrophy, obstructive hydronephrosis, polycystic disease,

other cystic or dysplastic disorders, neoplasm, renal vein

thrombosis, acute infection, Waldenstrom’s macroglobuline-

mia, hemophilia, acute arterial infarction, and duplication of

the renal pelvis [1]. Conditions that characteristically cause

bilateral renal enlargement include acute glomerulonephritis,

lymphoma, leukemia in children, systemic lupus crythema-

tosus, amyloidosis, sarcoidosis, sickle cell disease, lipod

nephrosis, lobar glomerulonephritis, glycogen storage dis-

ease, hereditary tyrosinemia, and total lipodystrophy [2].

0899-7071/04/$ – see front matter D 2004 Elsevier Inc. All rights reserved.

doi:10.1016/S0899-7071(03)00115-3

* Tel.: +82-42-220-7835; fax: +82-42-253-0061.

E-mail address: jckim@cnuh.co.kr (J.C. Kim).

To detect the change of the kidney size or volume,

various cross-sectional imaging modalities such as ultra-

sonography, computed tomography (CT), or magnetic res-

onance image have been used.

To our knowledge, however, there have not been many

articles reporting volume determination or computation of

the kidney using CT [3–5]. In 2000, we reported a digital

image processing method of renal segmentation without

using contrast media on abdominal CT image [6]. To apply

this method widely and conveniently in clinical fields, the

accuracy and usefulness of this method in renal volume

measurement should be proved in the preceding experi-

mental animal study.

This study was performed to evaluate the efficacy and

accuracy of our digital image processing method in the

determination of renal volume of the pigs on abdominal CT.

2. Materials and methods

Two 4-month-old Landrace pigs (24 kg of body weight)

were anesthetized with Rumpum (xylazine 1 mg/kg, Korea

Fig. 1. Unenhanced abdominal CT scans of a 4-month-old Landrace pig

(upper raw) and their extracted images (lower raw). Both kidneys on CT

were extracted by two steps of image processing: automatic segmentation in

a single slice using the character of pixel distribution of the kidney, and

removal of residual noise using batch processing with folding method.

Table 1

Comparison of kidney volume of two pigs: volume by digital image

processing vs. actual volume

Site

of kidney

Volume by digital

image processing (cm3)

Actual

volume (cm3)

Error

(%)

Right 78.2 and 78.4 (mean 78.3) 79.2 and 80.4 (mean 80) 2.1

Left 67.9 and 66.9 (mean 67.4) 72.1 and 67.9 (mean 70) 3.7

Mean 2.8

J.C. Kim / Journal of Clinical Imaging 28 (2004) 135–137136

Pfizer, Seoul) after premedication of Combelen (acetroma-

zine 0.2 mg/Kg, Korea Bayer, Seoul). After proper setting of

anesthetized pigs on the CT scan table, unenhanced abdom-

inal CT scan with thin sections of 3-mm collimation was

performed, using CTi Standard (General Electric Medical

Systems, Milwaukee, WI).

The kidneys of the pigs on CT were extracted by two

steps of digital image processing: (a) Single slice process-

ing using the character of pixel distribution of the kidney,

and (b) removal of residual noise using batch processing

with folding method. Single slice processing was accomp-

lished by peak search, binary image, ratio calculation,

mesh generation, pixel trace, template generation, ratio

extraction, and filling. Batch processing was performed

through the processes of slice folding, template generation,

Fig. 2. A photograph of surgically excised specimens of both kidneys in a

4-month-old Landrace pig. The actual renal volume was obtained by

means of three consecutive water displacement measurements on these

renal specimens.

and noise removal. Because the template scope of opening

is larger than the volume of kidney, extraction method was

also used to remove the noise. After obtaining extracted

renal images of each slice in right and left kidneys (Fig. 1),

the volume of extracted kidney was calculated by the

following equation [6].

XN�1

i¼1

ðððLp�X �Y Þ of Si þ ðLp� X �Y Þ of Siþ1Þ=2Þ�D

N: the number of slices including the extracted organ; Is:

slice number; D: length of slice interval; Lap: the number

of pixels composing extracted organ; X: horizontal length

of one pixel; Y: Vertical length of one pixel.

After CT scanning, the pigs were sacrificed, and both

kidneys were removed surgically in animal operating room

(Fig. 2). Actual renal volume of the pigs was obtained by

means of three consecutive water displacement measure-

ments on surgically removed specimen of both kidneys.

Then the measured renal volume by our digital image

processing method was compared with the actual renal

volume of the pigs.

3. Results

The results of comparison of the renal volumes deter-

mined by this method with actual renal volume of the pigs

were summarized in Table 1.

In our study, as a result of comparison between the mean

renal volume of both kidneys in two pigs measured by

digital image processing (78.3 and 67.4 cm3; right and left,

respectively) and mean actual renal volume (80 and 70 cm3,

respectively), the mean percentage error was 2.8%.

4. Discussion

CT potentially offers the most accurate noninvasive

means of estimating in vivo volumes.

A new digital image processing method of renal

segmentation on unenhanced abdominal CT image was

presented by us in 2000 [6]. Our method has two steps of

image processing: (a) Single slice processing using the

character of pixel distribution of the kidney, and (b)

removal of residual noise using batch processing with

folding method.

J.C. Kim / Journal of Clinical Imaging 28 (2004) 135–137 137

Before practical and easy application of this digital image

processing in daily clinical fields, we performed experi-

mental animal study to estimate the accuracy and usefulness

of this method in renal volume measurement on abdominal

CT in two 4-month old pigs.

The result in our study revealed that the mean percent-

age error of the renal volume of both kidneys of two pigs

measured by digital image processing and actual renal

volume was less than 3.7%. The extraction method in our

study was based on the difference of density of the other

organs. Therefore, it was not easy or simple to extract

only the renal parenchyma from surrounding solid organs

with densities similar to those of kidneys on CT. Because

of the fact that the right kidney abuts the liver and left

kidney is near to the spleen on CT scan in the anterior–

posterior direction, partial volume effect can also be

produced. In fact, there was difficulty in detection of

anatomical position for automatic segmentation and noise

removal in our study, because the size of the kidney was

smaller than the size of the adjacent large organs such as

liver or spleen. Especially in binary image of the kidney, it

was often difficult to differentiate the kidney and hole

during the course of mesh image formation. Considering

the relative small size of the kidney, similar density to

adjacent liver or spleen, advertent overlapping of mesh

and pixel, and possible partial volume effect on CT, the

mean percentage error of 2.8% is considered to be

tolerable and negligible.

Breiman et al. [7] performed contiguous 1-cm-thick CT

scanning in dog kidneys in vivo, and reported that the

mean percentage error of volume calculations using the

sum-of-areas technique was 3.86% for eight dog kidneys.

Brenner et al. [4] reported that calculated CT volumes of

cadaver kidneys and spleens were within F 10% of

directly measured volumes, with accuracy affected by

respiratory movement, rapid change in vivo blood volume,

low CT number-gradient at the object’s periphery, observer

error in cursor tracing of the desired structure, and math-

ematical errors inherent in Simpson’s rule. Compared with

the results of above two articles, 2.8% discrepancy rate in

our study was estimated to be more relevant and reliable.

Lerman et al. [8] accomplished contrast enhanced cine CT

scans in 14 anesthetized dogs and the volume of right

kidneys was determined after boundary identification, and

the mean renal volumes (F S.E. of the mean) in vivo

(78.2F 2.4 cc) were compared with those of excised post-

mortem subjects (66.1F 2.2 cc) determined by fluid dis-

placement (r = 0.86; P < .001). If we also use cine CT scan,

our results would be better than before.

Our study, however, has critical limitation that the sample

size (i.e., two) is too small to be statistically significant in

scientific thesis. Another limitation is based on the fact that

the volume of surgically removed pig kidney is not exactly

equal to the volume of pig kidney in vivo. The difference

may be due to the blood, filtrate, and urine contents of the in

vivo kidney.

In conclusion, automatic segmentation and volume mea-

surement of the kidney using our digital image processing

on unenhanced abdominal CT in two pigs were feasible,

reliable and useful. The renal volume determined by this

digital image processing was almost as same as the actual

renal volume. The method used in this study will be

clinically applicable in clinical fields to determine renal

volume both in normal persons and patients.

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