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Proceedings ArticleDOI

3D Volumetric CT Liver Segmentation Using Hybrid Segmentation Techniques

04 Dec 2009-pp 404-408
TL;DR: A liver segmentation algorithm is proposed using hybrid techniques by combining morphological-based, region-based and histogram-based techniques to segment volumetric CT data.
Abstract: The first step for computer-aided diagnosis for liver of CT scans is the identification of liver region. To deal with multislice CT scans, automatic liver segmentation is required. In this paper, we propose a liver segmentation algorithm using hybrid techniques by combining morphological-based, region-based and histogram-based techniques to segment volumetric CT data. A morphological-based technique is used to find the initial liver tissue from the first slice which is set as a starting slice and region-based is used for further processing for the rest slices, which incorporates seed point generation from Euclidean distance transform (EDT) image on the previous slice for region growing on the current slice. In order to remove neighboring abdominal organs of the liver which connect to the liver organ, the histogram-based technique is used by finding the left and right histogram tail threshold (HTT) and we repeat the use of morphology filtering and large contour detecting for liver smoothing.
Citations
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Journal ArticleDOI
TL;DR: A novel real time integrated method to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score.
Abstract: Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score. Although, it had comparable recall values with that gained by the human segmented images. It was noted that as the image under test had a dark background with the brighter object, thus the proposed method provided the superior recall value compared to the other methods.

102 citations


Cites methods from "3D Volumetric CT Liver Segmentation..."

  • ...Image segmentation techniques can also be classified into five approaches, such as Thresholdingbased method [8], Boundary-based method [9], Region-based segmentation method [10], Active contour model (Snakes) image segmentation [11], hybrid segmentation technique [12]....

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Proceedings ArticleDOI
11 Nov 2010
TL;DR: A new planning system for liver surgery is introduced, which is meant for computer tomography (CT) data analysis and graphical representation, and was developed with the intention to help surgeons by planning an operation and increasing the efficiency in open liver surgery.
Abstract: In clinical routine of liver surgery there are a multitude of risks such as vessel injuries, blood loss, incomplete tumor resection, etc. In order to avoid these risks the surgeons perform a planning of a surgical intervention. A good graphical representation of the liver and its inner structures is of great importance for a good planning. In this work we introduce a new planning system for liver surgery, which is meant for computer tomography (CT) data analysis and graphical representation. The system is based on automatic and semiautomatic segmentation techniques as well as on a simple and intuitive user interface and was developed with the intention to help surgeons by planning an operation and increasing the efficiency in open liver surgery.

23 citations

01 Jan 2013
TL;DR: In this article, the liver tissue was segmented in CT images and the comparison with several literature methods was made. But, the liver segmentation was performed using only a single image.
Abstract: The purpose of this paper is the segmentation of the liver tissue in computed tomography (CT) images and the comparison with several literature methods. Several expert radiologist restrictions such as automation, an easy user-interaction, and a low time-cost were taken into account for selecting the nal algorithm. Thirty public dataset have been used to estimate the accuracy of the algorithm, twenty for training and ten for testing our method. A Jaccard index of 0:89, an average distance of 2:06 mm, and a runtime of 0.54 seconds per image state a promising eciency but a poor ecacy. For this reason, user-assisted tools were used in a nal step to demonstrate that this fast computational method with minimal user corrections (in some required dataset) remains runtime ecient and enough accurate for the liver segmentation purpose.

6 citations


Cites background from "3D Volumetric CT Liver Segmentation..."

  • ...86 [6], or the VOE is greater than 14% [15]....

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Journal ArticleDOI
TL;DR: A new approach to the liver segmentation from 3D images is presented and compared to the existing methods in terms of quality and speed of segmentation, which is significantly faster.
Abstract: A new approach to the liver segmentation from 3D images is presented and compared to the existing methods in terms of quality and speed of segmentation. The proposed technique is based on 3D deformable model (active surface) combining boundary and region information. The segmentation quality is comparable to the existing methods but the proposed technique is significantly faster. The experimental evaluation was performed on clinical datasets (both MRI and CT), representing typical as well as more challenging to segment liver shapes.

6 citations


Cites methods from "3D Volumetric CT Liver Segmentation..."

  • ...freeform (where the method does not need the training stage [15]): the need of training data is sometimes a problem, moreover - only cases similar to the training data sets are well segmented; d) 2D (where each 2D slice is segmented separately, sometimes using an initialization from the previous slice [21], and the volume is reconstructed from the independent 2D segmentations [5]) vs....

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Journal ArticleDOI
TL;DR: A computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors using support vector machines classifier is introduced.
Abstract: One of the leading causes of mortality worldwide is liver cancer. The earlier the detection of hepatic tumors, the lower the mortality rate. This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors. Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range, intensity values overlap between the liver and neighboring organs, high noise from computed tomography scanner, and large variance in tumors shapes. The proposed method consists of three main stages; liver segmentation using Fast Generalized Fuzzy C-Means, tumor segmentation using dynamic thresholding, and the tumor’s classification into malignant/benign using support vector machines classifier. The performance of the proposed system was evaluated using three liver benchmark datasets, which are MICCAI-Sliver07, LiTS17, and 3Dircadb. The proposed computer adided diagnosis system achieved an average accuracy of 96.75%, sensetivity of 96.38%, specificity of 95.20% and Dice similarity coefficient of 95.13%.

5 citations

References
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Journal ArticleDOI
TL;DR: Six different distance transformations, both old and new, are used for a few different applications, which show both that the choice of distance transformation is important, and that any of the six transformations may be the right choice.
Abstract: A distance transformation converts a binary digital image, consisting of feature and non-feature pixels, into an image where all non-feature pixels have a value corresponding to the distance to the nearest feature pixel. Computing these distances is in principle a global operation. However, global operations are prohibitively costly. Therefore algorithms that consider only small neighborhoods, but still give a reasonable approximation of the Euclidean distance, are necessary. In the first part of this paper optimal distance transformations are developed. Local neighborhoods of sizes up to 7×7 pixels are used. First real-valued distance transformations are considered, and then the best integer approximations of them are computed. A new distance transformation is presented, that is easily computed and has a maximal error of about 2%. In the second part of the paper six different distance transformations, both old and new, are used for a few different applications. These applications show both that the choice of distance transformation is important, and that any of the six transformations may be the right choice.

2,019 citations


"3D Volumetric CT Liver Segmentation..." refers methods in this paper

  • ...The cost values are assigned according to the following scheme which were suggested in [3] with a = 1, b = 1....

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Journal ArticleDOI
TL;DR: A comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.
Abstract: This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

979 citations


Additional excerpts

  • ...The metric measures the percentage of mismatching voxels between the automatic and manual segmentation [8]....

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Journal ArticleDOI
TL;DR: A CT liver image diagnostic classification system which will automatically find, extract the CT liver boundary and further classify liver diseases is presented and shown to be efficient and very effective.
Abstract: Computed tomography (CT) images have been widely used for liver disease diagnosis. Designing and developing computer-assisted image processing techniques to help doctors improve their diagnosis has received considerable interests over the past years. In this paper, a CT liver image diagnostic classification system is presented which will automatically find, extract the CT liver boundary and further classify liver diseases. The system comprises a detect-before-extract (DBE) system which automatically finds the liver boundary and a neural network liver classifier which uses specially designed feature descriptors to distinguish normal liver, two types of liver tumors, hepatoma and hemageoma. The DBE system applies the concept of the normalized fractional Brownian motion model to find an initial liver boundary and then uses a deformable contour model to precisely delineate the liver boundary. The neural network is included to classify liver tumors into hepatoma and hemageoma. It is implemented by a modified probabilistic neural network (PNN) [MPNN] in conjunction with feature descriptors which are generated by fractal feature information and the gray-level co-occurrence matrix. The proposed system was evaluated by 30 liver cases and shown to be efficient and very effective.

280 citations

Journal ArticleDOI
TL;DR: A new approach to automatic segmentation of the liver for volume measurement in sequential CT images is proposed and the experimental measurement of area and volume is compared with those using manual tracing method as a gold standard by the radiological doctors, and demonstrates that this algorithm is effective.

134 citations


"3D Volumetric CT Liver Segmentation..." refers methods in this paper

  • ...Using multiscale morphology as used in [5] we believe it can process objects based on their shape as well as their size....

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01 Jan 2007
TL;DR: A fully automated method based on an evolu- tionary algorithm, a statistical shape model (SSM), and a deformable mesh to tackle the liver segmentation task of the MICCAI Grand Chal- lenge workshop.
Abstract: We present a fully automated method based on an evolu- tionary algorithm, a statistical shape model (SSM), and a deformable mesh to tackle the liver segmentation task of the MICCAI Grand Chal- lenge workshop. To model the expected shape and appearance, the SSM is trained on the 20 provided training datasets. Segmentation is started by a global search with the evolutionary algorithm, which provides the initial parameters for the SSM. Subsequently, a local search similar to the Active Shape method is used to refine the detected parameters. The resulting model is used to initialize the main component of our approach: a deformable mesh that strives for an equilibrium between internal and external forces. The internal forces describe the deviation of the mesh from the underlying SSM, while the external forces model the fit to the image data. To constrain the allowed deformation, we employ a graph- based optimal surface detection during calculation of the external forces. Applied to the ten test datasets of the workshop, our method delivers comparable results to the human second rater in six cases and scores an average of 59 points.

84 citations


"3D Volumetric CT Liver Segmentation..." refers background in this paper

  • ...Therefore, several published papers employ higher processing levels such as deformable models [2], [7] and probabilistic atlas [1]....

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