Journal of Computer and Communications, 2014, 2, 1-7
Published Online January 2014 (
Review on the Methods of Automatic Liver Segmentation
from Abdominal Images
Suhuai Luo1, Xuechen Li1, Jiaming Li2
1The University of Newcastle, Australia; 2The CSIRO Computational Informatics, Australia.
Received September 2013
Automatic liver segmentation from abdominal images is challenging on the aspects of segmentation accuracy,
automation and robustness. There exist many methods of liver segmentation and ways of categorisingthem. In
this paper, we present a new way of summarizing the latest achievements in automatic liver segmentation.We
categorise a segmentation method according to the image feature it works on, therefore better summarising the
performance of each category and leading to finding an optimal solution for a particular segmentation task. All
the methods of liver segmentation are categorized into three main classes including gray level based method,
structure based method and texture based method. In each class, the latest advance is reviewed with summary
comments on the advantages and drawbacks of each discussed approach. Performance comparisons among the
classes are given along with the remarks on the problems existed and possible solutions. In conclusion, we point
out that liver segmentation is still an open issue and the tendency is that multiple methods will be employed to-
gether to achieve better segmentation performance.
Liver Segmentation; Image Feature; Performance Comparison; Review; Su rvey
1. Introduction
The liver is one of the most important organs of human
body. Liver diseases result heavy death worldwide. In
helping doctors and surgeons treating liver diseases,
computer aided liver disease diagnosis and liver surgical
planning systems are playing important roles nowadays.
An accurate and automatic segmentation approach of
liver parenchyma, vessel and tumour is crucial to a com-
puter-aided liver disease diagnosis and liver surgical
planning system such as a system for liver transplanta-
tion. However, due to the highly varying shape of liver,
low contrast and intensity in homogeneity inside liver,
weak boundaries to its adjacent organs such as heart and
stomach, and intensity homogeneity to adjacent organs,
liver segmentation becomes a challenging task that has
attracted much research attention recently.
Liver segmentation from abdominal images is a pro-
cess of subdividing a medical image such as computed
tomography (CT) and magnetic resonance imaging (MRI)
into liver parenchyma area and non-parenchyma areas.
There exist many methods of liver segmentation includ-
ing region growing, active contour, level set, graph cuts,
clustering and threshold based methods, deformable
model, statistic shape model; support vector machine
(SVM) based, neural network (NN) based, etc. Some
comprehensive reviews have been done on liver seg-
mentation [1-4]. It is noticed that a particular categoris-
ing methodology is adopted to well address an emphasis
in these reviews. For example, [5] reviews the machine
learning techniques for automatic segmentation of liver
images, where the techniques are classified as NN based,
support vector machine-based, clustering based, and hy-
brid technique; [1] surveys segmentation of liver from
CT images, where the techniques are classified as region
based, threshold based, level set, model based, active
contour based, histogram based, gray level based, and
clustering based.
Based on thorough study of various liver segmentation
methods and systematic summary of the methods, we
categorise a segmentation method according to the image
features it works on, therefore better summarising the
performance of each category and leading to find an op-
timal solution for a particular segmentation task. All the
methods are categorized into three main classes including
gray level based method, structure based method and
texture based method. This paper is organized as below.
Review on the Methods of Automatic Liver Segmentation from Abdominal Images
Section 2 presents the literature review of liver segmen-
tation. It is structured in three categories including gray
level based method, structure based method and texture
based method. Section 3 discusses performance compar-
isons among the classes along with the remarks on the
problems existed and possible solutions. Finally, conclu-
sions are made in Section 4.
2. Review
The latest achievements in automatic liver segmentation
are reviewed in this section. All the methods are dis-
cussed in one of the three categories including gray level
based, structure based and texture based.
2.1. Gray Level Based Methods
Gray level is the most obvious feature of image. When
extracting objects from image, the most natural way is to
use the gray level to tell boundaries. The benefits of gray
level based methods are: the feature is easy to extract
without using special algorithm; they are stable and ro-
bust, can easily be used into similar cases; they often
achieve high accuracy result. Their drawbacks are: most
of them are semi-automatic methods and need user’s
operation; when the difference of gray level intensity
between target and background is small, the methods will
lose their effectiveness. This section reviews various
gray level based liver segmentation methods.
2.1.1. Region Growing
The seeded region growing algorithm was first proposed
by [6 ]. It puts the pixels with the similar gray value to-
gether as a same region. Comparing with the histogram-
based region selection, the region growing can use more
features rather than just gray level intensity. The average
intensity, variance, color or texture etc. all can be used to
measure the similarity of pixels or small regions. The
drawbacks are: the seed point should be chosen by user;
it will lose the effectiveness when the region is inhomo-
The efficiency of these methods depends on the choice
of the seed points strongly. The seed points are usually
selected by users to improve the quality of segmentation.
[7] presented a performance benchmarking study on liver
tumour segmentation for three semi-automatic algorithms:
2D region growing with knowledge-based constraints
(A1), 2D voxel classification with propagational learning
(A2) and Bayesian rule-based 3D region growing (A3).
Its conclusion is A1 and A2 is more effective than A3.
Some other methods are fully automatic. [8] presented an
adaptive region growing method that learns its homo-
geneity criterion from characteristics of the region to be
segmented automatically. However, the efficiency of this
method depends on the homogeneity of the tissue and
cannot avoid the under-segmentation when the target is
inhomogeneous. [9,10] based on [8]’s work and made
some improvement to deal with the inhomogeneity. They
determined the seed region based on the intensity of gray
level; and then separated the liver and heart based on the
anatomical feature; next, used an improved region grow-
ing to segment the image; finally, employed a post-
processing to deal with the under-segmentation near the
right lung lobe. Their method can deal with most cases
well and it is quick; but in some difficult cases (e.g. the
gray level intensity of live is inhomogeneous because of
large lesion), it will cause under-segmentation. In the
work of [11], the centroid of the largest connected region
of the eroded image is determined and the coordinates of
centroid point acts as the initial seed point for region
growing; then a gaussian model was used to determine
the threshold range of region growing; finally, a post-
processing was employed to fix the hole and the connec-
tion with other neighbourhood tissues. It can achieve
good results and less time-consuming. [12] employed ray
casting algorithm as a pre-treatment to transform the ini-
tial image into a projection plane, and then used region
growing to segment liver from CT scans.
2.1.2. Active Contour
Active contours are curves that deform within digital
images to recover object shapes. They are classified as
either parametric active contours or geometric active
contours according to their representation and imple-
mentation. In particular, parametric active contours are
represented explicitly as parameterized curves in La-
grangian formulation. Geometric active contours are
represented implicitly as level sets of two dimensional
distance function which evolve according to an Eulerian
formulation [13].
Parametric active contours (active contours, or snakes)
are curves defined within an image domain that can
move under the influence of internal forces coming from
within the curve itself and external forces which are de-
fined so that the snake will conform to an object boun-
dary or other desired features within an image. One suc-
cessful implementation is gradient vector flow (GVF)
[14]. The main advantage is that it can capture the tar-
geted object without a great definition of the initial
boundary, which can be iteratively calculated in- and
outwards. It can find simultaneously both concave and
convex features. [15] presented a new model (vascular
active contour model) to deal with vessel segmentation.
It can drive the active contour into the thinner and weak-
er vessel regions. And a dual curvature strategy is added
to smooth the surface of the vessel without changing its
shape. Active contour are also used to refine the seg-
mentation results and improve the accuracy [16,17].
Geometric active contour are based on the theory of
Review on the Methods of Automatic Liver Segmentation from Abdominal Images
curve evolution and the level set method. Level set me-
thods focus on the gradient of gray level, leads the
boundary to approach the position where gradient is
maximum. The basic idea of the level set method is to
represent a contour as the zero level set of a higher di-
mensional function, called level set function, and formu-
late the motion of the contour as the evolution of the lev-
el set function. It has been successfully used in the seg-
mentation of medical images. The main advantage of
level set is that it allows changes of surface topology
implicitly; however, it is time-consuming, and often
semi-automatic, and could lead to over-segmentation. To
increase the computation speed and make the method
fully automatic, [18] splatted the method into two steps:
first is a rough segmentation by employing fast-marching
level set and then a geodesic active contour level set al-
gorithm was used to refine the initial approximation.
There are also other ways to achieve the rough segmen-
tation. [19] presented a new fuzzy level set algorithm. It
begins with spatial fuzzy clustering. The result was uti-
lized to initialize level set segmentation and estimated
the parameters of level set evolution. Moreover, the
fuzzy level set algorithm was enhanced with locally re-
gularized evolution which can facilitate level set mani-
pulation and lead to more robust segmentation. It is effi-
cient when the background is simple and the boundary
between background and object is clear. In many cases,
the segmentation result of one slice could be used as an
initial segmentation of the adjacent slice [20,21]. To
achieve better segmentation results, the original level set
algorithm could be optimized. [22] proposed a level set
based variational approach that incorporates shape priors
into edge-based and region-based models. The energy
function took smoothness of the region and the length of
boundary into account, made the description of the
boundary shape more specifically. [23] presented an op-
timized level set algorithm. Instead of using the same
parameter values in all stages of the algorithm, they pro-
posed to change them depending on the resolution step
by means of a multi-curvature, multi-growth strategy and
a fine detail correction at the last multi-resolution level.
2.1.3. Graph Cuts
The main idea of graph cuts is to represent the image to
an undirected weighted graph. Every node represents
each pixel of image. Every edge connected a pair of ad-
jacent pixels. The weight of edge indicates the similarly
of gray level, colour or texture between each pair. The
segmentation is a cut of the graph. Each region repre-
sents a subgraph. The best cut is to make the similarity in
a subgraph maximum and the similarity between sub-
graphs minimum. It has become increasingly popular in
medical image segmentation within recent years because
it is not iterative, and achieves global minimization for
certain classes of energy functions. However, it is not a
fully automatic method because it needs users to select
seed points which label the “object” and “background”.
The applications on liver segmentation often focus on the
vessel and tumour. The reason is that when segmenting
the vessel or tumour, the “background”, liver, is homo-
geneous. It is easy to extract the object from a simple and
homogenous background by using graph cuts method.
When using it to the segmentation of liver parenchyma,
the seeds of background should include every other re-
gion, which is difficult.
In liver parenchyma segmentation, the seed points
should be selected carefully. To make the method fully
automatic, many algorithms were employed to select
seed points. Fast marching and mathematical morphol-
ogy were utilized in [24] to achieve a rough segmentation
of liver and background of CT image. Statistic adaptive
threshold initialization [25 ] and k-means clustering [26 ]
can also achieve the same purpose. However, compared
with other gray level based methods, graph cuts used in
liver parenchyma segmentation show more difficulties.
2.1.4. Threshold Based
The segmentation methods which base on threshold are
often used to make a rough segmentation to determine
ROI or seed points as pre-processing [10-12]. Firstly, the
intensity of initial image is enhanced; then the range of
liver or tumour is determined based on histogram analyse
and prior knowledge; finally, the rough segmentation is
completed based on the determined threshold. In liver
parenchyma segmentation, the threshold based methods
are too rough to achieve good result. However, it could
be employed to segment tumours from liver since the
contrast between liver and tumour is more significant.
[27,28] used threshold based method as the main seg-
mentation method of tumour. First, the contrast of gray
level intensity was enhanced; second, the slice was added
to itself, after that, the contrast between liver and tumour
is large enough to use the threshold to isolate tumour
directly; thirdly, the threshold based method was used to
segment the image; finally, the morphological filter was
employed as the post-processing. Since it is sensitive to
noise, [27] used roundness and information of neigh-
bouring slice to reduce false detection.
2.1.5. Clustering Based
The main idea of clustering based method is that in
n-dimensional feature space, the distance between sam-
ples is shorter if they belong to the same class and the
similarity of samples from same class is higher. There
are two main issues in clustering based methods: one is
how to estimate the similarity of samples, the other is
how to determine the threshold of similarity. The advan-
tages of clustering based methods are: they are fully
Review on the Methods of Automatic Liver Segmentation from Abdominal Images
automatic and they can handle multiple tasks of segmen-
tation. However, its results may contain many false posi-
tive region, needing post-processing.
The clustering based methods used on liver segmenta-
tion generally include two classes: Fuzzy c-means (FCM)
clustering and k-means clustering. In FCM, each point
has a degree of membership to classes rather than be-
longs to just one class completely. Points on the edge of
a class may have less membership degree than points in
the centre of class. When the number of classes is given
as n, all points will be classified into n classes based on
the membership degree and Euclidean distance between
each point and class centre. In [29-31] the initial image
was segmented by fuzzy c-means clustering and then
smoothed by morphological processing. Then the candi-
date regions were analysed based on computing proper-
ties [29] or classified by neural network [30,31]. Finally,
the regions which belong to liver or node were extracted.
Compared with k-means clustering, FCM can success-
fully segment the liver from CT images, despite the
similar gray level of adjacent organs and different gray
level of tumours in the liver. Also, the FCM can be used
to refine the rough segmentation [32]. K-means cluster-
ing does not often act as main segmentation method since
it is too rough. [26,33] employed k-means clustering to
select the object and background seeds for graph cuts.
[34] used the k-means clustering result as the initial
boundary of active contour.
There are also other clustering based methods. Hier-
archical agglomerative clustering combine with self-
organized map was utilized to achieve liver segmentation
of MR image [35]. The EM/MPM clustering method
which exploits both intensity of voxels and labels of the
neighbouring voxels was employed to cluster liver tissue
2.2. Structure Based Methods
Structure based methods have proven to be effective and
powerful in many medical applications. The central hy-
pothesis of it is that structures of interested objects have
a repetitive form of geometry. In the approach, a proba-
bilistic model is created to represent the variation of the
shapes of organs, and use this model as prior knowledge
to impose constraints in an image for segmentation.
[37] pre sen ted a semi-automatic segmentation method
by using deformable model. Statistical shape model
(SSM) is used for liver segmentation frequently because
of its ability to constrain the segmentation to approx-
imately match previously seen shapes of a training data
base. [38 ] proposed a hybrid liver segmentation method
using statistical pose model (SPM) and probabilistic atlas.
It achieved an average segmentation score of 72.4. [39]
presented an approach for automatic liver segmentation
based on SSM integrated with an optimal-surface-detec-
tion strategy. The method achieved higher accuracy com-
paring to previous model based methods. [40] presented
a novel statistical shape model approach for fully auto-
matic CT liver segmentation. The method combines
learned local shape priors with constraints that are di-
rectly derived from the current curvature of the model in
order to restrict adaptation to regions where large defor-
mations were expected and observed. That makes the
method more robust and reduces the leakage to other
organs. [41] used the model information to choose a
segment with the highest fidelity to the organ instead of
using the model information to direct the segmentation.
2.3. Texture Based Methods
Texture based methods are different from other segmen-
tation methods. They do not focus on the boundary of
object. Instead, they are interested in the texture features.
The main procedure of texture based methods is: firstly,
the texture features of target are extracted; then a classi-
fier is employed to classify the features; finally, the tar-
get region is refined and smoothed by post-processing.
Many different types of texture features are used in
liver segmentation. [42] extracted 4 features from liver
CT image including neighbourhood mean, neighbour-
hood variance, Law’s texture, and Unser’s sum-and-dif-
ference histograms. [43] used the appearance feature and
context feature to describe liver in 3D CT images. [44]
listed many tumour features such as tumour volume, tu-
mour diameter, tumour sizes region ratio, etc. It em-
ployed the minimum redundancy and maximum rele-
vance as feature selection method which selects useful
features according to the maximal statistical dependency
criterion based on mutual information and minimizes the
redundancy among features simultaneously. Wavelet
coefficients were used to extract texture characteristics of
liver and its surrounding tissues [45]. [46] presented an
accurate liver segmentation algorithm which used texture
features including high order statistical texture features
and anatomical structural features. A feature extraction
method of liver tumour based on watershed was em-
ployed [47].
Many classifiers are employed to determine liver tar-
get. As a classifier, support vector machine (SVM) does
not need much training data to achieve a good result. It is
appropriate for the liver segmentation. [44] presented a
SVM based liver segmentation method using wavelet
features and 3D median filtering as post-processing. [45]
used SVMto classify the data into pixel-wised liver area
or non-liver area, then integrated morphological opera-
tions were used to remove noise and finally delineated
the liver. Watershed transform was used in [47] to extract
features of liver tumour and SVM was used to make the
classification. The connected region detection and mor-
phological operations were employed as post- process-
Review on the Methods of Automatic Liver Segmentation from Abdominal Images
Neural networks (NNs) are non-linear statistical data
modelling or decision making tools. They can be used to
model complex relationships between inputs and outputs
or to find patterns in data, which makes it suitable for
liver segmentation. [ 31] employed NN to classify the
result of fuzzy c-mean clustering, identify which regions
belong to liver tissue. The same method was also used
[33] to identify the tumours from liver. Watershed algo-
rithm was used in [48] to over-segment the MRI into
many small regions, and NN was used to get the feature
value of liver image. By comparing the feature value of
each small region with the output of NN, the liver region
was selected and the segmentation parameter was ad-
justed, repeated the segmentation until the difference
between feature value of liver region and NN output did
not decrease. [49] used the colour information of liver
MR image in three colour spaces, then used Hopfield
neural network (HNN) to segment the tissue of liver.
Genetic algorithm [20,21] and extreme learning ma-
chine [42] can also be used in liver image segmentation
by combining with other methods.
With the development of machine learning technology,
more texture based methods will be employed in the im-
age segmentation area.
3. Discussion
All the three categories of liver segmentation methods
have their own merits and drawbacks and may be effec-
tive for a particular case. In this section, their characte-
ristics are summarized.
The gray level based methods directly utilize image’s
features. They are the main methods used in clinical
practice, especially in tumour segmentation. However,
these methods rely heavily on the evaluation of the gray
level of targets. Some methods use prior knowledge to
determine the gray level range of the liver. They are fast,
but may lose their effectiveness when the gray level of
the target changes. Some other methods utilize histo-
grams to estimate the gray level of the liver. They avoid
the problems of using prior knowledge, but may fail
when the image represents a low percentage of the liver.
There are also some methods that use manual work or
automatic rough segmentation to select a small region of
the liver and based on this region to compile information
about the gray level. They are more reliable, but need
manual work or more computation time. Some gray level
based methods use gradient information. The advantage
of these methods is that they are sensitive to boundaries,
resulting exactly approaches to image boundaries. The
disadvantage is that there are many boundaries in the
image, only a few of which are the real boundaries of the
liver. These methods will easily converge on the stronger
fake boundaries, causing over or under segmentation. To
refine the result, manual work or other methods are
The structure based methods can deal with the unclear
boundary of the liver by using prior knowledge, meaning
that they can handle some problems which gray level
based methods cannot handle. The difficulty of these
methods is that they need a large amount of training data
to cover all the conditions of the liver. It is even more
difficult with liver’s non-standard shape, which makes it
hard to describe liver using a unified model. This is the
main limitation for the approach in clinical practice.
Using texture based methods to segment is more like
using human eyesight to do segmentation. Just as humans
use texture information to determine boundaries rather
than gray level or shape, texture based methods often
rely on machine learning and pattern recognition. The
advantages are that more features are considered together,
and the result is closer to the results of manual segmenta-
tion. Similar to the structure based methods, texture
based methods can also achieve better results when the
boundaries are not clear. In addition to the need of train-
ing data, the description of texture feature is a challenge.
Although there have been many descriptors, these de-
scriptors are not like those described by human. In addi-
tion, there are so many kinds of descriptors that selection
among them is a problem. Machine learning and pattern
recognition are still developing technologies with much
weaker information processing abilities than human brain.
They cannot currently produce satisfactory segmentation
results, making it necessary to find more refined me-
In general, the gray level based methods are more
highly developed. They are often used together to handle
the problem of complex segmentation. In most cases,
they can achieve better segmentation results. Structure
based methods focus on the shape of the object, which
makes them more robust. Texture based methods try to
simulate the way our brains process information.
4. Conclusion
This paper reviews automatic liver segmentation from
abdominal images. The review is structured in a new way
of categorising a segmentation method according to the
image feature it works on, therefore better summarising
the performance of each category and leading to find an
optimal solution for a particular segmentation task. All
the methods of liver segmentation are categorized into
three main classes including gray level based method,
structure based method and texture based method. Per-
formance comparisons among the classes are given along
with the remarks on the problems existed and possible
solutions. In conclusion, we point out that liver segmen-
tation is still an open issue and the tendency is that mul-
tiple methods will be employed together to achieve better
Review on the Methods of Automatic Liver Segmentation from Abdominal Images
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