Extraction of Liver Suspicious Regions from Computed
Tomography (CT) Images for Computed Aided Diagnosis

Bassel Al Samman, Maan Ammar
Abstract— This paper proposes an automatic Two-Phase
method which can perform extraction of liver region from
abdominal computed tomography image in the first phase, and
extraction of suspicious regions (SR) from extracted liver
region in the second phase. In particular, the proposed method
depends on the fact that liver takes up the largest area among
the various organs included in computed tomography
abdominal image, and uses various image processing
techniques like thresholding, morphological operations, and
component labeling to extract liver region. In addition, since
the intensity of a suspicious rejoins can vary greatly according
to the patient and imaging process conditions, a decision on the
threshold for extraction is not easy. Accordingly, the proposed
method extracts the suspicious rejoins by means of a Fuzzy CMeans clustering technique, which can determine the threshold
regardless of a changing intensity. Based on experimental
results, the proposed method achieved a very good extraction
rate with the advantage of automatic liver region extraction.
Keywords — Liver Cancer, Computer Aided Diagnosis,
Component Labeling, Morphological Operations, Fuzzy C-Means
Clustering.
I. INTRODUCTION
1.
computer-aided diagnosis (CAD) can be defined as a
Tdiagnosis
that is made by a radiologist who uses the
HE
output from a computerized analysis of medical images as a
‘second opinion’ in detecting lesions and in making
diagnostic decisions. The final diagnosis is made by the
radiologist [1]. The general CAD system, including liver
diagnosis, can be divided into three parts: (1) Extraction of
liver region; (2) Extraction of the SRs from the liver region;
(3) Diagnosis of suspicious regions. Wong D., et al. [2]
proposed a semi-automatic method based on 2D region
growing with knowledge-based constraints to segment
lesions from constituent 2D slices obtained from 3D CT
images.
K.Mala, et al. [3] proposed an automatic method used the
anatomic knowledge of the liver and histogram analyzing to
fix an intensity threshold to extract the liver pixels from the
CT abdominal image, and used the Fuzzy C-Means
clustering to extract the tumors regions in the extracted liver
region.
Kobashi M., et al. [4] used techniques depend on the prior
knowledge to achieve the extraction of the liver and
Bassel Al Samman is a Master student at Biomedical Engineering
Department, University of Damascus, Damascus, Syria (Mobile Phone:
+963 955 424240, e-mail: basselsamman@hotmail.com).
Mann Ammar is a Professor at Biomedical Engineering Department,
University of Damascus, Damascus, Syria (Mobile Phone: +963 966
992317, e-mail: maan_ammar@yahoo.com).
suspicious regions. These techniques include using templates
for the regions.
Haralick transform and Hopfield Neural Network were used
to segment 90% of the liver pixels correctly from the CT
abdominal image by John. E. Koss, et al. [5].
The CAD system proposed by Miltiades G., et al. [6]
consists of two basic modules: the feature extraction and the
classifier modules. In their work, region of interest (liver
tumor) were identified manually from the CT liver images
and then fed to the feature extraction module. The total
performance of the system was 97% for validation set and
100% for testing set.
Hong J., et al. [7] proposed a CAD system based on Fuzzy
C-Means Clustering for liver tumor extraction with an
accuracy of 91% using features like area, circularity and
minimum distance from liver boundary to tumor and Bayes
classifier for classifying normal and abnormal slices.
Achieving correct extraction of liver region is very effective
agent in the liver diagnosis CAD system. Any important loss
in liver region could affect the final diagnosis because the
lost parts could contain any kind of suspicious regions.
In the proposed method Component Labeling technique is
used to extract the liver region from CT abdominal image,
achieved good extraction and eliminated by far probability
of losing any important part of liver region. After extraction
of the liver region, the Fuzzy C-Means clustering technique
is used to extract suspicious regions in the liver region, e.g.,
malignant or benign primary tumors, metastasis tumors, and
Liver cysts.
II. METHDOLOGHY
In this work, the proposed method consists of two phases. In
the first phase region of the liver is automatically extracted
from CT abdominal image using the anatomic knowledge of
the liver, component labeling technique, and morphological
processing. In the second phase suspicious regions are
extracted from extracted liver region using Fuzzy C-Means
Clustering (FCM) technique.
A. Images Data:
The CT images dataset for this work is made of parallel image
slices of 512 x 512 pixels on 64-slice CT scanner using a
spiral abdomen imaging protocol with slice thickness of 0.8
mm. The dataset is composed of 12 images: 6 HCC
(Hepatocellular Carcinoma), 2 Metastasis Tumors, 1 Liver
Cyst, 2 Liver Suspicious Regions, and 1 Hale Liver image to
test the proposed method against hale images where liver region
does not contain any suspicious regions. All tumors, cyst, and
suspicious regions were manually segmented by a specialized
radiologist to use them as a reference to compare with the result
of the proposed method. CT Images were collected from Al
Salam Center for Radiology Imaging.
B. Liver Extraction:
Identifying liver from CT abdominal image has always been
a challenging task. This is mainly due to the fact that there
are other organs, with same intensity as liver, adjacent to the
liver makes segmentation more difficult [3].
In the proposed method liver region extraction phase
consists of following steps:
(1) Based on the anatomy of the liver and the histogram of
CT abdominal images we concluded that most of liver
region pixels belong to the gray levels range 100- 200.
Therefore we apply a thresholding process using eq. (1) to
extract the liver region from the CT abdominal image. Fig.
1(b) shows the result of this process on the original image in
Fig. 1(a).
1
𝑓(𝑖, 𝑗) = {
0
𝑖𝑓 100 ≤ 𝑓(𝑖, 𝑗) ≤ 200
the segmented liver in the CT abdominal image. Fig. 1(i)
shows the extracted original liver region.
C. Suspicious Regions Extraction:
From the extracted liver region, suspicious regions are
segmented using Fuzzy C-Means clustering technique. FCM
minimizes the object function through the iterative
optimization of the membership function based on the
similarity between the data and the center of a cluster. FCM
varies the threshold between clusters through an iterative
process. As a result, the threshold is determined
appropriately for every slice and the tumor region can be
successfully extracted [3].
Fuzzy C-Means (FCM) is a method of clustering which
allows one piece of data to belong to two or more clusters. It
is based on minimization of the following objective function
defined by eq. (2):
𝑁
𝐶
𝑚
𝐽𝑚 = ∑ ∑ 𝑢𝑖𝑗
‖𝑥𝑖 − 𝑐𝑗 ‖
(1)
𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
(2) Component Labeling technique is applied to group
extracted pixels in the previous step into separate groups.
Then by calculating the count of pixels in each group we can
specify the biggest one which contains the liver region. Fig.
1( c ) shows the result removing connected components of
less than 130 pixels area. In this way, the liver region is
extracted along with the fragments of other adjacent organs
with similar intensity.
(3) The liver region is isolated from the other regions by
applying a set of morphological opening and closing
operations. Mathematical morphological operations tend to
simplify image data preserving their essential shape
characteristics and eliminating irrelevancies. Opening an
image with a disk structuring element smoothes the contour,
breaks narrow isthmuses, and eliminates small islands and
sharp peaks or capes. Closing an image with a disk
structuring element smoothes the contours, fuses narrow
breaks and long thin gulfs, eliminates small holes, and fill
gapes on the contours [8]. At this stage, a closing operation
is applied to fill the small holes in the liver region, and then
two successive opening operations are applied to break
connections between regions in the image. In each
morphological operation a disk structuring element with
suitable size is used (radius=3 pixels for closing operation
and 13, 25, respectively, for opening operations). Fig. 1(d)
shows the result of the closing operation, and Figs 1(e) and
(f) show the result of opening and closing operations,
respectively. Fig. 1(g) shows the result of the second closing
operation.
(4) The small separated rejoins removed based on the area
condition. The area of the liver is large when it is compared
with the fragments of other organs. Fig. 1(h) shows the
extracted liver regions.
(5) After removing the small rejoins, the image obtained is
complemented and multiplied by the original image to get
2
, 1≤𝑚< ∞
(2)
𝑖=1 𝑗=1
Where m is any real number greater than 1, uij is the degree
of membership of xi in the cluster j, xi is the ith of ddimensional measured data, and cj is the d-dimension center
of the cluster, and ||*|| is any norm expressing the similarity
between any measured data and the center.
Fuzzy partitioning is carried out through an iterative
optimization of the objective function shown above, with the
update of membership uij and the cluster centers cj by:
𝑚
∑𝑁
1
𝑖=1 𝑢𝑖𝑗 . 𝑥𝑖
𝑢𝑖𝑗 =
,
𝑐
(3)
𝑗=
𝑚
2
∑𝑁
𝑖=1 𝑢𝑖𝑗
𝑚−1
‖𝑥 − 𝑐𝑗 ‖
∑𝑐𝑘=1 ( 𝑖
)
‖𝑥𝑖 − 𝑐𝑘 ‖
This iteration will stop when:
(𝑘+1)
(𝑘)
𝑚𝑎𝑥𝑖𝑗 {|𝑢𝑖𝑗
− 𝑢𝑖𝑗 |} < 𝜀
(4)
Where ε is a termination criterion between 0 and 1, whereas
k is the iteration step. This procedure converges to a local
minimum or a saddle point of Jm.
The pixels of the input image (extracted liver image) are
divided into three clusters according to intensity because the
intensity of the SR is higher or lower than that of normal
liver tissue. One of these clusters includes pixels in the
background (low intensity), another one includes pixels in the
suspicious regions (medium intensity) and the last one includes
pixels in the liver region (high intensity). The suspicious
regions are outputted for further analysis and diagnosis.
III. EXPERIMENTAL RESULTS
Fig. 1(a) is a CT scan image of a patient with a liver tumor. The
liver is located along the left half of the image and is light gray
in color. The white tubular structures are the normal liver blood
vessels. The liver region contains two tumors regions, the tumor
cells are dark gray in color. Most of the liver region pixels are
extracted by extracting pixels belong to the range from 100 to
200 (Fig. 1(b)).
Fig. 1(a) CT abdominal image
Fig. 1(e) Applying first opening operation
Fig. 1(b) Result of thresholding
Fig. 1(f) Applying closing operation to fill small holes
Fig. 1(c) Pixels of biggest region
Fig. 1(g) Applying second opening operation
Fig. 1(d) Applying Closing Operation to fill small holes
Fig. 1(h) Segmented liver region
REFERENCES
After applying Component Labeling technique and calculating
each region pixels count, the biggest region is given in Fig.
1(c). Liver region images after the morphological operations are
given in Fig. 1(d) – Fig. 1(g).
Segmented tumor regions after applying Fuzzy C-Means
Clustering technique and removing very small regions using
opening morphological operation is given in Fig. 1(i). Table 1
shows the results obtained by applying the proposed method to
our data.
Fig. 1(i) Segmented Suspicious Regions
Table 1: Experimental Results of Applying
Proposed Method to our Data
SR Kind (Disease)
No. of Cases Correct Extraction
%
HCC
6
90
Metastasis Tumor
2
80
Liver Cyst
1
100
Other SRs
2
100
The image of no disease was correctly segmented by the
method giving no suspicious regions. The extraction errors were
due to the small intensity difference between the normal liver
tissue and the lesion in some images. Pre-processing operations
like contrast enhancement could enhance this difference in
order to achieve better extraction rate.
IV. CONCLUSION
The proposed method is able to automatically extract the liver
region from a CT images based on anatomic knowledge of the
liver, using component labeling technique and morphological
processing. The SRs are extracted by the FCM clustering
method which can adaptively differentiate SRs from normal
liver tissue and be applied to various kinds of diseases. Using
component labeling technique in extracting liver region
achieved good extraction without losing almost any
important part of liver region. A very good extraction rate was
obtained in the experiments. The proposed method can be used
to present 2D representation of suspicious regions in the liver,
and could be a part of a complete liver diagnosis CAD system
aims to detect and diagnose liver tumors.
[1] Giger M., Computer-aided Diagnosis in Medical Imaging –A
New Era in Image Interpretation. University of Chicago, 2006.
[2] Wong D., Liu J., Fengshou Y., Tian Q., Xiong W., Zhou J., Qi
Y., Han T., Venkatesh S.K., and Wang S.C. A semi-automated
method for liver tumor segmentation based on 2D region
growing with knowledge-based constraints. The MIDAS
Journal - Grand Challenge Liver Tumor Segmentation,
MICCAI Workshop, 2008
[3] Mala K., Sadasivam V., and Alagappan S., Neural Network
based Texture Analysis of Liver Tumor from Computed
Tomography Images. International Journal of Biomedical
Sciences, Vol. 2 No. 1, 2007.
[4] Kobashi M. and Shapiro L.G., Knowledge-based organ
identification from CT images. Pattern Recognition, Vol. 28,
pp. 475–491, 1995.
[5] Koss J.E., Newman F.D., Johnson T.K., and Kirch D.L.,
Abdominal Organ Segmentation Using Texture Transforms
and a Hopfield Neural Network. IEEE Transactions on
Medical Imaging, Vol 18, No. 7, pp 640-648, July 1999.
[6] Miltiades G., Mougiakakou S., Matsopoulos G. K., Nikita K.,
Nikita A., and Kelekis D., A Computer-Aided Diagnostic
System to Characterize CT Focal Liver Lesions: Design and
Optimization of a Neural Network Classifier,”IEEE
Transactions on Information Technology in BioMedicine, Vol
7, Issue 3, pp 153 – 162, Sep. 2003.
[7] Hong J., Kaneko T., Sekiguchi R. and Houmpark K.,
Automatic Liver Tumor Detection from CT. IEICE Trans.Inf.
& Syst., Vol. E84-D, No. 6, pp 741-748, June 2001.
[8] Gonzalez R.C., Woods R.E., Digital Image Processing Using
MATLAB. Prentice Hall, 2004.