ArticlePDF Available

Abstract and Figures

Liver tumors segmentation is an important prerequisite for planning of surgical interventions. For clinical applicability, the segmentation approach must be able to cope with the high variation in shape and gray-value appearance of the liver. We present a fully automatic 3D segmentation method for the liver tumors from contrast-enhanced CT data. The method consists of two main stages. First an initial histogram and statistical distribution functions are created, and from them a new image is created where, in each voxel, a weighted function is attached in accordance with the probability of the voxel grey level. Next, we use the active contour method on the new image, where the active contour evolution is based upon the minimization of variances between the liver tumor and its closest neighborhood.
Content may be subject to copyright.
Liver Tumor segmentation in CT images using probabilistic
methods
Itay Ben-Dan Elior Shenhav
July 9, 2008
Abstract
Liver tumors segmentation is an important prerequisite for planning of surgical inter-
ventions. For clinical applicability, the segmentation approach must be able to cope with
the high variation in shape and gray-value appearance of the liver. We present a fully
automatic 3D segmentation method for the liver tumors from contrast-enhanced CT data.
The method consists of two main stages.
First an initial histogram and statistical distribution functions are created, and from
them a new image is created where, in each voxel, a weighted function is attached in accor-
dance with the probability of the voxel grey level. Next, we use the active contour method
on the new image, where the active contour evolution is based upon the minimization of
variances between the liver tumor and its closest neighborhood.
1 Introduction
Techniques of Image processing and data analysis are more and more used in medical practice.
Mathematical algorithms of features extraction and measurements can exploit data to detect
pathology in an individual, the evolution of the disease, or to compare a normal subject to an
abnormal one.
We are using the assumption that the liver can be segmented ([MC],[FPFW],[LLS],[1], previous
contest) and we will follow this assumption in the rest of the paper.
1.1 Description of the problem
Liver cancer is one of the most popular cancer diseases and causes a large amount of death
every year [2]. In order to make decisions such as liver resections, doctors will need to know
the tumor volume, and further, the functional liver volume. Thus, an important task in ra-
diology is the determination of tumor volume. Accurate segmentation of liver tumor from an
abdominal image is one of the most important steps in 3Drepresentation for liver volume
measurement, liver transplant, and treatment planning. Since manual segmentation is incon-
venient, time consuming and depends on the individual operator to a large extent, automatic
segmentation is much more preferred.
The main issue of automatic liver tumor segmentation from contrast-enhanced CT data
is that the intensity values of the liver tumors are often similar to those of healthy parts of
Mathematics Dept., Technion—Israel Institute of Technology, Haifa 32000, Israel. itaybd@gmail.com
Biomedical Dept., Technion—Israel Institute of Technology, Haifa 32000, Israel.
shenhave@technion.ac.il.
1
the liver. Approaches which are only based on local intensity or intensity gradient features
are usually not sufficient to differentiate between liver tissue and other anatomical structures
in those regions. In order to alleviate this problem prior knowledge about the typical shape
and the intensity of a liver tumors may be incorporated into the process to constrain the
segmentation process where the image information is not reliable.
1.2 Previous Work
A significant number of techniques has been proposed to deal with this and similar problems.
The whole set of approaches can be roughly divided into three groups, variational geometric
approach, texture analysis, machine learning.
Here we combine methods of prior analysis and energy based segmentation. Energy based
segmentation we use here based on [TCLV],[CV]. Efficient numerical methods were devel-
oped for blood vessels segmentation [HKPG], and for liver segmentation []. A combination of
Bayesian approaches and deformable surfaces for tumor segmentation was reported in [PHS],
[PSDF].
In this paper we adopt the Chan-Vese method and develop a new model using the intensity
likehood ratio test. Unlike the model in [LM], the energy based segmentation is preformed on
a probability image which yields a better and less noise sensitive results.
2 Methodology
Our approach for evaluating models for automatic liver segmentation consists of the following
stages: first, a probability image of the organ of interest is obtained by applying a binary
classification model (liver/non-liver) obtained using pixelbased priors. Since the classifier
model does not incorporate any spatial information, Chan-Vese segmentation algorithm is
applied on the organ probability image to overcome this drawback and remove the noise
introduced by misclassified pixels.
In this paper following([LM],[PHS]), we show how using the following two phases enables
us to extract the tumor up to relatively small mistakes.
2.1 Modeling tumor appearance in CT by weighted non-parametric density
estimate
Data obtained from manually segmented cases, was used as a reference to apply a learning
procedure method. The manually segmented data consists of the V OIin only. We then obtain
another volume of interest , V OIout, which is considered to contain only non-lesion tissue, by
first dilating generously the original mask V OIin using a 3Dstructuring element and then
excluding V OIin from the dilated mask. Morphological operations are restricted to respect
other pre-segmented structures, including body outline, bone and other detected hotspots. A
probabilistic model of tissue attenuation in CT in both the segmented tumor (i.e. lesion) as
well as in the background (i.e. non-lesion) can be obtained in terms of CT intensity likelihood
functions using weighted non-parametric density estimates. Let the CT value, I CT (x), at a
voxel, x, be I(x), then we can approximate the likelihood of this intensity,
in a lesion, or outside a lesion by:
2
f(α|in tumor) = 1
|V OIin|Zα+γ1
αγ1
dV OIin
f(α|out tumor) = 1
|V OIout|Zα+γ2
αγ2
dV OIout
Where αis the intensity value, V OIin is the measure of the region of the tumor and
V OIout is the measure of the region outside the tumor, and γ1, γ2are parameters determined
by |V OIin|,|V OIout|respectively.
A joint-likelihood ratio r(x) is calculated on a voxel-by-voxel basis in the CT domain to
provide a measure of voxel being contained in tumor tissue as opposed to being in background,
r(x) = f(x|in tumor)f(x|out tumor).
The choice of r(x) is based on tests we made.
By this we can overcome problems of small variance, and also enhancing difference between
the tumor and other parts (blood vessels) of the liver. This method prove its usefulness
especially when when a variety of tissues surrounding the tumor.
2.2 3DImage variational segmentation
Our method is based on geometric active surfaces that evolve according to geometric partial
differential equations until they stop at the boundaries of the objects. We use a minimal
variance term that measures the homogeneity inside and outside the object. The measure we
use is the minimal variance term proposed by Chan and Vese [TCLV]. It penalizes lack of
homogeneity inside and outside the evolving surface. In [TCLV], the image is divided into two
segments, the interior and exterior of a closed surface. This model minimizes the variance in
each segment. The model was generalized in [3][CV] to piecewise constant segmentation of
more than two segments and higher dimensions. Given a 2Dgray level image I(x, y) : Ω R2
, Chan and Vese proposed to use a minimal variance criterion given by the functional,
EM V (C, c1, c2) = Z ZC
(I(x, y)c1)2dxdy +Z Z\C
(I(x, y)c2)2dxdy +vZC
ds
(11) where Cis the contour separating the two regions, ΩCis the interior of the contour C, and
RCds measures the length of the separating contour, where vis a constant that determines
the regularization level. While minimizing this functional, c1and c2obtain the mean intensity
values of the image in the interior and the exterior of C, respectively. The optimal curve would
separate the interior and the exterior with respect to their relative expected values.
Our method integrates two ’methods’: a bayesian prior based tumor modelling, a homo-
geneity term based on the Chan-Vese functional. In the next section we discuss the experi-
mental results.
3 Experimental Results
Our primary results are based on the CT images from the contest. In order to obtain priori
knowledge we analyzed the intensity values of the liver tumors and of the healthy parts in the
given test data.
3
The intensity values of the liver tumors are often similar to those of healthy parts of the
liver, here we introduce some statistics (including the three relevant cases from the original
data set) which demonstrate the problem of using variance minimization methods on the orig-
inal picture: The intensity variance of ’tumor’ pixels is:
Case 1: 5.1955 105
Case2: 5.1477 105
Case4: 1.0655 104
The intensity variance of the ’non-tumor’ pixels is:
Case 1: 8.6659 104
Case2: 6.2103
Case4: 1.2103
One of the main problems is the small relative difference between the intensity of the
tumor and the healthy part this cause that any energy based segmentation of the normalized
intensity values will not work. We will demonstrate other problems which make methods as
multiplication of all pixels by some large constant inefficient.
Therefore, with our method, given the assumptions approved by the provided data and
figures, the result is that the distribution functions of the grey level of the tumors and the
healthy parts are different. Just as well, the distribution functions of the the tumors are similar
up to the expectation (by similar we mean that there is an isometry of the approximated
functions s.t they are 0<104close in the L1metric). This enables us segmenting the liver
tumor, the accuracy of the segmentation depends on the accuracy of estimated the distribution
function. In figure (3) we show graphically the advantage of our method.
4
Figure 1: Grey level distribution of tumors 1,2,4
Figure 2: Grey level distribution of tumors is marked by the blue curve and the distribution
of the healthy part is marked by the red curve
5
Figure 3: probability image of neighborhood of the liver tumor
An example for the segmentation is shown in figures ??,5
An example of one advantage of our method of using the probability image can be seen in
the following figures 6,?? where the tumor is near the boundary of the liver and the tumor is
surrounded by two different regions which both apply to the out tumor class.
The results comparison metrics and scores for all the ten test cases.
Overlap Volume Ave. Surf. RMS Surf. Max. Surf.
Error Diff. Dist. Dist. Dist.
Tumor (%) Score (%) Score (mm) Score (mm) Score (mm) Score Total Score
IMG05_L1 30.66 76 17.37 82 2.36 40 3.24 55 12.08 70 65
IMG05_L2 40.77 68 35.78 63 1.53 61 1.92 73 5.80 85 70
IMG05_L3 52.48 59 51.06 47 2.33 41 3.00 58 7.77 81 57
IMG06_L1 86.91 33 86.90 10 3.25 18 3.51 51 6.88 83 39
IMG06_L2 41.85 68 2.80 97 1.11 72 1.79 75 8.94 78 78
IMG07_L1 39.18 70 36.54 62 5.27 0 6.34 12 23.50 41 37
IMG07_L2 30.21 77 0.53 99 1.45 63 2.02 72 8.81 78 78
IMG08_L1 24.96 81 23.36 76 2.87 28 3.55 50 12.77 68 60
IMG09_L1 97.49 25 94.59 2 7.37 0 8.46 0 17.28 57 17
IMG10_L1 46.66 64 46.28 52 2.81 29 3.42 52 9.94 75 54
Average 49.12 62 39.52 59 3.04 35 3.73 50 11.38 72 56
4 Conclusion and discussion
The benefits of this algorithm can be summarized as follows: Automatic detection of interior
contours, robust with respect to noise, ability to detect and represent complex topologies
(boundaries, segments) and extraction of geometric measurements such as length, diameter,
area, volume intensity, of a detected tumor.
6
image.jpg
Figure 4: Tumor in the Coronal Cut
7
image segmented.jpg
Figure 5: Tumor Segmentation Coronal Cut
8
image edge.jpg
Figure 6: tumor is in the image edge
image segmented edge.jpg
Figure 7: segmented tumor in the image edge
9
Further possible improvements could be in Validation of vessels segmentation, Liver parti-
tioning to functional parts and Integration with pre-operative planning modules. And maybe
Using the algorithm to segment other organs.
5 Acknowledgements
We would like to thank Dr. Moshe Lapidot at the Rambam Hospital, Haifa, Israel.
References
[FEI] R. Kimmel,
Fast edge integration,
invited chapter in Geometric Level Set Methods in Imaging, Vision and Graphics, S. Osher
and N. Paragios (Eds.), Springer Verlag, ISBN 0387954880, 111 (2003)
[GS] R. kimmel, Geometric segmentation of 3D structures,
Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference
[BFPZ] R. Goldenberg, R. Kimmel,E. Rivlin, M. Rudzsky,
Cortex segmentation a fast variational geometric approach, Medical Imaging, IEEE Trans-
actions on Volume 21, Issue 12, Dec 2002 Page(s): 1544 - 1551.
[CV] F. Chan, L. Vese, Active contour and segmentation using geometric PDE dor medical
imaging.
[TCLV] F. Chan, L. Vese, Active contours without edges, IEEE transactions on image pro-
cessing, Vol 10, No. 2, February 2001
[LM] Rui Lu, Pina Marziliano, LIVER TUMOR VOLUME ESTIMATION BY SEMI-
AUTOMATIC SEGMENTATION METHOD, Engineering in Medicine and Biology Soci-
ety, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the Volume , Issue
, 2005 Page(s):3296 - 3299
[PHS] Vaclav Potesil, Xiaolei Huang , Xiang Sean Zhou,Automated Tumour Delineation Us-
ing Joint PET/CT Information Medical Imaging 2007: Computer-Aided Diagnosis. Edited
by Giger, Maryellen L.; Karssemeijer, Nico. Proceedings of the SPIE, Volume 6514, pp.
65142Y (2007).
[HKPG] Michal Holtzman-Gazit, Ron Kimmel, Nathan Peled, Dorith Goldsher, Seg-
mentation of Thin Structures in Volumetric Medical Images Digital Object Identifier
10.1109/TIP.2005.860624
[PSDF] Mailan Phama, Ruchaneewan Susomboonb, Tim Disneyc, Daniela Raicub, Jacob
Furstb, A Comparison of Texture Models for Automatic Liver Segmentation
[MC] Laurent Massoptier, Sergio Casciaro Fully Automatic Liver Segmentation through
Graph-Cut TechniqueProceedings of the 29th Annual International Conference of the IEEE
EMBS Internationale, Lyon, France
10
[FPFW] Charles Florin, Nikos Paragios, Gareth Funka-Lea, and James Williams Liver Seg-
mentation Using Sparse 3DPrior Models with Optimal Data Support Inf Process Med
Imaging. 2007;20:38-49.
[1] [LJH] Seong-Jae Lim, Yong-Yeon Jeong, and Yo-Sung Ho Segmentation of the Liver Using
the Deformable Contour Method on CT Images
[LLS] Hans Lamecker, Thomas Lange, Martin Seeba, Segmentation of the Liver using a 3D
Statistical Shape ModelZIB-Report 04-09
[2] [FS] Tse-Ling Fong, Leslie J. Schoenfield, Hepatocellular Carcinoma (Liver Can-
cer)www.medicinenet.com
[3] [CV2] Tony Chan,Luminete Vese A multiphase level set framework for image segmentation
using the Mumford and Shah model,Int. Journal of Comp. Vis., vol. 50, no. 3, pp. 271293,
2002
11
... The liver is the largest gland and vital organ in the body due to its functionality, and without its presence, survival is impossible [Ben-Dan, 2008]. It performs more than 500 tasks and plays a major role in carbohydrates, proteins, fats, steroids and medicines metabolism. ...
... The liver is the largest gland and vital organ in the body due to its functionality, and without its presence, survival is impossible [1]. CCl4 hepatotoxicity is characterized by hepatocellular necrosis with fat deposition. ...
Article
The liver is responsible for the metabolism and detoxification of the most of components that enter the body. Carbon tetrachloride (CCl4) is a highly toxic chemical agent, the most famous drug used to induce liver damage experimentally. Camel milk has been deeply studied for its special properties because of its higher hepatoprotective, insulin and antibacterial activities. The present study was designed to examine the preventive effects of camel milk (CM) and camel urine against the toxic effects of acute exposure to carbon tetrachloride (CCl4) on the liver tissue of mice. Administration of a single dose of CCl4 caused liver toxicity as monitored by an increase in liver enzymes, including ALT, AST and ALP. A total of 24 albino rats (200–250 g) were divided randomly into 4 groups comprising 6 rats in each group, G1 The first group is untreated control, G2 was the positive CCl4, (G3) Rats fed with Camel milk (100 ml/24 h/cage) injected with CCl4, (G4) Rats fed with Camel Urine (100 ml/24 h/cage) injected with CCl4. A significant (P < 0.05) increase in serum AST, ALT, and ALP activities was observed in the CCl4-treated rats compared with those of the control rats, respectively. Based on this study, Camel milk and camel urine have a protective effect against CCL4-Induced Hepatotoxicity.
... This technique demonstrated an average volume difference of 24.2% in tumor segmentation. Ben-Dan and Shenhav [13] developed an approach for liver tumor segmentation based on the active contour using a weighted function of the probability of each pixel. An approach for liver tumor segmentation based on Bayesian classification and the active contour 2 BioMed Research International method was proposed by Taieb et al. [14]. ...
Article
Full-text available
Objective . Our objective is to develop a computerized scheme for liver tumor segmentation in MR images. Materials and Methods . Our proposed scheme consists of four main stages. Firstly, the region of interest (ROI) image which contains the liver tumor region in the T1-weighted MR image series was extracted by using seed points. The noise in this ROI image was reduced and the boundaries were enhanced. A 3D fast marching algorithm was applied to generate the initial labeled regions which are considered as teacher regions. A single hidden layer feedforward neural network (SLFN), which was trained by a noniterative algorithm, was employed to classify the unlabeled voxels. Finally, the postprocessing stage was applied to extract and refine the liver tumor boundaries. The liver tumors determined by our scheme were compared with those manually traced by a radiologist, used as the “ground truth.” Results . The study was evaluated on two datasets of 25 tumors from 16 patients. The proposed scheme obtained the mean volumetric overlap error of 27.43% and the mean percentage volume error of 15.73%. The mean of the average surface distance, the root mean square surface distance, and the maximal surface distance were 0.58 mm, 1.20 mm, and 6.29 mm, respectively.
... To do this, the posterior probability of a pixel with color c belonging to foreground (F) or background (B) respectively is considered, assuming equal priors. As such, it is essentially functioning as a simple Bayesian classifier, the error in which can be estimated by (7) When there is no error (ε = 0), we would like to give the Color-based terms (M and G) full weight, and when the color models become indistinct (ε ≥0.5), we want to give them no weight: (8) The geodesic and boundary terms are further weighted based on the local confidence u(x) of the geodesic components: (9) where empirically γ= 2 to 2.5 works well. We redefine the region terms to weight the geodesic component by u(xi): (10) This maintains the weight of the geodesic distance term when relatively certain that the pixel xi is clearly in the objects interior or exterior (u(xi) close to 1) and decreases it near where geodesic segmentation would place boundaries (u(xi) close to 0). ...
Article
Full-text available
Medical imaging is an important technique for diagnosis and treatment planning today. A new proposed method of fully automatic processing frameworks is given based on graph-cut and Geodesic Graph cut algorithms. This paper addresses the problem of segmenting liver and tumor regions from the abdominal CT images. A predicate is defined for measuring the evidence for a boundary between two regions using Geodesic Graph-based representation of the image. The algorithm is applied to image segmentation using two different kinds of local neighborhoods in constructing the graph. Liver and hepatic tumor segmentation can be automatically processed by the Geodesic graph-cut based method. This system has concentrated on finding a fast and interactive segmentation method for liver and tumor segmentation. In the preprocessing stage, the CT image process is carried over with mean shift filter and statistical thresholding method for reducing processing area with improving detections rate. Second stage is liver segmentation; the liver region has been segmented using the algorithm of the proposed method. The next stage tumor segmentation also followed the same steps. Finally the liver and tumor regions are separately segmented from the computer tomography image.
... There have been numerous attempts to automate liver tumor segmentation. Broadly, these attempts include (i) machine learning [10,9,16,3] (ii) clustering [7] (iii) region based [1,14] (iv) edge detection [15] and (v) partial differential equation [13,12] based approaches. In these approaches, the image feature employed range from simple intensity features to a battery of texture features.Although there is some success with the existing tumor segmentation approaches, the major issue affecting performance is the high similarity between tumor and healthy tissue, both in terms of intensity as well as texture. ...
Conference Paper
Full-text available
Low contrast between tumor and healthy liver tissue is one of the significant and challenging features among others in the automated tumor delineation process. In this paper we propose kernel based clustering algorithms that incorporate Tsallis entropy to resolve long range interactions between tumor and healthy tissue intensities. This paper reports the algorithm and its encouraging results of evaluation with MICCAI liver Tumor Segmentation Challenge 08 (LTS08) dataset. Work in progress involves incorporating additional features and expert knowledge into clustering algorithm to improve the accuracy.
Article
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via www.lits-challenge.com.
Article
Liver cancer is one of the diseases with the highest mortality in the world. Accurate segmentation of liver regions from liver CT data is a necessary preprocessing stage of computer-aided detection and diagnosis algorithms. In view of the small number of existing medical image data sets and difficulty in acquisition, this paper expanded the effective data set based on an improved conditional generation adversarial network pix2pix. We added random noise to the input of the generator, and the last layer of the output of the discriminator. Moreover, the original fully connected layer was replaced with a probability matrix with a size of 16 × 16. This is to make the discriminator more delicate. We merged the block module in MobileNet-v2 and U-Net (M2-Unet) to achieve liver segmentation. In addition to the training and validation using the public competition data set LiTs, we also constructed a new liver data set. The Dice similarity coefficient of the algorithm in this paper is 88.7%. The proposed method has improved to the ordinary U-Net algorithm. In the test phase, each image only needs 0.072 s to complete the segmentation on average, which exceeds the segmentation speed of specialists. Experimental results show that algorithm in this paper can accurately segment the liver and meet the requirements of realtime segmentation.
Chapter
Liver is a vital organ to detoxify the blood and break down the fat molecules from the food that we consume. Most of the liver diseases are inherited from their parents. Liver can get damaged due to various factors, the liver can be damaged due to many reasons. Among this, cancer is one cause of liver damage. Considering the Indian population, the rate of mortality due to liver cancer is 6.8 per one lakh in men and 5.1 per one lakh in women. The computed tomography is the common method to screen, diagnose, staging, and prognosis assessment; thereby, the liver cancer patient can be given proper treatment. While using image processing techniques for liver tumor segmentation, we adopted a binary segmentation task where a network has been designed to segment liver and liver tumor segments. The proposed system focuses on medical image sequences of the liver tumor segmentation based on region. The abnormality structure of the liver has been altered based on the position and by setting a threshold for segmenting. A deep neural network was used to compare the dataset images, and the input dataset has been trained and verified. Finally, the status of liver tumor would be inferred whether it is malignant or benign.
Research
Medical Image Segmentation is an activity with huge handiness. Biomedical and anatomical data are made simple to acquire because of progress accomplished in computerizing picture division. More research and work on it has improved more viability to the extent the subject is concerned. A few techniques are utilized for therapeutic picture division, for example, Clustering strategies, Thresholding technique, Classifier, Region Growing, Deformable Model, Markov Random Model and so forth. This work has for the most part centered consideration around Clustering techniques, particularly k-implies what's more, fluffy c-implies grouping calculations. These calculations were joined together to concoct another technique called fluffy k-c-implies bunching calculation, which has a superior outcome as far as time usage. The calculations have been actualized and tried with Magnetic Resonance Image (MRI) pictures of Human cerebrum. The proposed strategy has expanded effectiveness and lessened emphasis when contrasted with different techniques. The nature of picture is assessed by figuring the proficiency as far as number of rounds and the time which the picture takes to make one emphasis. Results have been dissected and recorded. Some different strategies were surveyed and favorable circumstances and hindrances have been expressed as special to each. Terms which need to do with picture division have been characterized nearby with other grouping strategies.
Article
Full-text available
Medical Image Segmentation is an activity with huge handiness. Biomedical and anatomical data are made simple to acquire because of progress accomplished in computerizing picture division. More research and work on it has improved more viability to the extent the subject is concerned. A few techniques are utilized for therapeutic picture division, for example, Clustering strategies, Thresholding technique, Classifier, Region Growing, Deformable Model, Markov Random Model and so forth. This work has for the most part centered consideration around Clustering techniques, particularly k-implies what's more, fluffy c-implies grouping calculations. These calculations were joined together to concoct another technique called fluffy k-c-implies bunching calculation, which has a superior outcome as far as time usage. The calculations have been actualized and tried with Magnetic Resonance Image (MRI) pictures of Human cerebrum. The proposed strategy has expanded effectiveness and lessened emphasis when contrasted with different techniques. The nature of picture is assessed by figuring the proficiency as far as number of rounds and the time which the picture takes to make one emphasis. Results have been dissected and recorded. Some different strategies were surveyed and favorable circumstances and hindrances have been expressed as special to each. Terms which need to do with picture division have been characterized nearby with other grouping strategies.
Article
Full-text available
Computed tomography (CT) imaging remains the most utilized modality for liver-related cancer screening and treatment monitoring purposes. Liver, liver tumor and liver vasculature segmentation from CT data is a prerequisite for treatment planning and computer assisted detection/diagnosis systems. In this paper, we present a survey on liver, liver tumor and liver vasculature segmentation methods that are using CT images, recent methods presented in the literature are viewed and discussed along with positives, negatives and statistical performance of these methods. Liver computer assisted detection/diagnosis systems will also be discussed along with their limitations and possible ways of improvement. In this paper, we concluded that although there is still room for improvement, automatic liver segmentation methods have become comparable to human segmentation. However, the performance of liver tumor segmentation methods can be considered lower than expected in both automatic and semi-automatic methods. Furthermore, it can be seen that most computer assisted detection/diagnosis systems require manual segmentation of liver and liver tumors, limiting clinical applicability of these systems. Liver, liver tumor and liver vasculature segmentation is still an open problem since various weaknesses and drawbacks of these methods can still be addressed and improved especially in tumor and vasculature segmentation along with computer assisted detection/diagnosis systems.
Conference Paper
Full-text available
Segmentation in volumetric images deals with separating 'objects' from their 'background' in a given 3D data. Usually, one starts with 'edge detectors' that give binary clues on the locations of the objects boundaries. Classical edge detectors that can be adopted from 2D are the Marr-Hildreth, and Haralick or Canny edge detectors. Next, usually one integrates these clues into meaningful contours or surfaces that indicate the boundaries of the objects. We use our recent variational explanation for the Marr-Hildreth and the Haralick-Canny like edge detectors to extend these classical operators. We combine these operators with a minimal deviation measure that can be tuned to the problem at hand. Finally, an improved 'geometric active surface model' is defined.
Conference Paper
Full-text available
Automatic liver segmentation from abdominal computed tomography (CT) images is one of the most important steps for computer-aided diagnosis (CAD) for liver CT. However, the liver must be separated manually or semi-automatically since surface features of the liver and partial-volume effects make automatic discrimination from other adjacent organs or tissues very difficult. In this paper, we present an unsupervised liver segmentation algorithm with three steps. In the preprocessing, we simplify the input CT image by estimating the liver position using a prior knowledge about the location of the liver and by performing multilevel threshold on the estimated liver position. The proposed scheme utilizes the multiscale morphological filter recursively with region-labeling and clustering to detect the search range for deformable contouring. Most of the liver contours are positioned within the search range. In order to perform an accurate segmentation, we produce the gradient-label map, which represents the gradient magnitude in the search range. The proposed algorithm performed deformable contouring on the gradient-label map by using regular patterns of the liver boundary. Experimental results are comparable to those of manual tracing by radiological doctors and shown to be efficient.
Article
Full-text available
An automatic cortical gray matter segmentation from a three-dimensional (3-D) brain images [magnetic resonance (MR) or computed tomography] is a well known problem in medical image processing. In this paper, we first formulate it as a geometric variational problem for propagation of two coupled bounding surfaces. An efficient numerical scheme is then used to implement the geodesic active surface model. Experimental results of cortex segmentation on real 3-D MR data are provided.
Article
Full-text available
We present a new segmentation method for extracting thin structures embedded in three-dimensional medical images based on modern variational principles. We demonstrate the importance of the edge alignment and homogeneity terms in the segmentation of blood vessels and vascular trees. For that goal, the Chan-Vese minimal variance method is combined with the boundary alignment, and the geodesic active surface models. An efficient numerical scheme is proposed. In order to simultaneously detect a number of different objects in the image, a hierarchal approach is applied.
Conference Paper
Full-text available
Volume segmentation is a relatively slow process and, in certain circumstances, the enormous amount of prior knowledge available is underused. Model-based liver segmentation suffers from the large shape variability of this organ, and from structures of similar appearance that juxtapose the liver. The technique presented in this paper is devoted to combine a statistical analysis of the data with a reconstruction model from sparse information: only the most reliable information in the image is used, and the rest of the liver's shape is inferred from the model and the sparse observation. The resulting process is more efficient than standard segmentation since most of the workload is concentrated on the critical points, but also more robust, since the interpolated volume is consistent with the prior knowledge statistics. The experimental results on liver datasets prove the sparse information model has the same potential as PCA, if not better, to represent the shape of the liver. Furthermore, the performance assessment from measurement statistics on the liver's volume, distance between reconstructed surfaces and ground truth, and inter-observer variability demonstrates the liver is efficiently segmented using sparse information.
Article
We propose a new multiphase level set framework for image segmentation using the Mumford and Shah model, for piecewise constant and piecewise smooth optimal approximations. The proposed method is also a generalization of an active contour model without edges based 2-phase segmentation, developed by the authors earlier in T. Chan and L. Vese (1999. In Scale-Space'99, M. Nilsen et al. (Eds.), LNCS, vol. 1682, pp. 141–151) and T. Chan and L. Vese (2001. IEEE-IP, 10(2):266–277). The multiphase level set formulation is new and of interest on its own: by construction, it automatically avoids the problems of vacuum and overlap; it needs only log n level set functions for n phases in the piecewise constant case; it can represent boundaries with complex topologies, including triple junctions; in the piecewise smooth case, only two level set functions formally suffice to represent any partition, based on The Four-Color Theorem. Finally, we validate the proposed models by numerical results for signal and image denoising and segmentation, implemented using the Osher and Sethian level set method.
Article
Automatic liver segmentation from abdominal computed tomography (CT) images based on gray levels or shape alone is difficult because of the overlap in gray-level ranges and the variation in position and shape of the soft tissues. To address these issues, we propose an automatic liver segmentation method that utilizes low-level features based on texture information; this texture information is expected to be homogenous and consistent across multiple slices for the same organ. Our proposed approach consists of the following steps: first, we perform pixel-level texture extraction; second, we generate liver probability images using a binary classification approach; third, we apply a split-and-merge algorithm to detect the seed set with the highest probability area; and fourth, we apply to the seed set a region growing algorithm iteratively to refine the liver's boundary and get the final segmentation results. Furthermore, we compare the segmentation results from three different texture extraction methods (Co-occurrence Matrices, Gabor filters, and Markov Random Fields (MRF)) to find the texture method that generates the best liver segmentation. From our experimental results, we found that the co-occurrence model led to the best segmentation, while the Gabor model led to the worst liver segmentation. Moreover, co-occurrence texture features alone produced approximately the same segmentation results as those produced when all the texture features from the combined co-occurrence, Gabor, and MRF models were used. Therefore, in addition to providing an automatic model for liver segmentation, we also conclude that Haralick cooccurrence texture features are the most significant texture characteristics in distinguishing the liver tissue in CT scans.
Article
This paper is devoted to the analysis and the extraction of information from bio-medical images. The proposed technique is based on object and contour detection, curve evolution and segmentation. We present a particular active contour model for 2D and 3D images, formulated using the level set method, and based on a 2-phase piecewise-constant segmentation. We then show how this model can be generalized to segmentation of images with more than two segments. The techniques used are based on the Mumford-Shah [21] model. By the proposed models, we can extract in addition measurements of the detected objects, such as average intensity, perimeter, area, or volume. Such informations are useful when in particular a time evolution of the subject is known, or when we need to make comparisons between different subjects, for instance between a normal subject and an abnormal one. Finally, all these will give more informations about the dynamic of a disease, or about how the human body growths. We illustrate the efficiency of the proposed models by calculations on two-dimensional and three-dimensional bio-medical images.
Article
In this paper, we propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford--Shah functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by gradient. We minimize an energy which can be seen as a particular case of the minimal partition problem. In the level set formulation, the problem becomes a "mean-curvature flow"-like evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. We will give a numerical algorithm using finite differences. Finally, we will present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable. Also, the initial curve can be anywhere in the image, and interior contours are automatically detected.
Article
We propose a new multiphase level set framework for image segmentation using the Mumford and Shah model, for piecewise constant and piecewise smooth optimal approximations. The proposed method is also a generalization of an active contour model without edges based 2-phase segmentation, developed by the authors earlier in (Chan and Vese 1999), (Chan and Vese 2001). The multiphase level set formulation is new and of interest on its own: by construction, it automatically avoids the problems of vacuum and overlap; it needs only log n level set functions for n phases in the piecewise constant case; it can represent boundaries with complex topologies, including triple junctions; in the piecewise smooth case, only two level set functions formally suce to represent any partition, based on The Four-Color Theorem. Finally, we validate the proposed models by numerical results for signal and image denoising and segmentation, implemented using the Osher and Sethian level set method.