Liver and Tumor Segmentation and Analysis from CT of Diseased Patients via a Generic Affine Invariant Shape Parameterization and Graph Cuts

Linguraru, Marius George; Richbourg, William J.; Watt, Jeremy; Pamulapati, Vivek; Summers, Ronald M.

doi:10.1007/978-3-642-28557-8_25

Cited by 24 publications

(22 citation statements)

References 20 publications

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“…Results for each measure represent as the mean and standard deviations of the overall datasets. Table II shows the quantitative comparative results of the liver segmentation with previous methods in [17], [18], [26], [42], [43] and the proposed liver shape initialization and segmentation based on the Sliver07 database. As can be seen in the 6th row of Table II, model initialization was far from the accurate segmentation of the liver.…”

Section: ) Quantitative Results and Comparisonsmentioning

confidence: 99%

Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images

Chen

Shi

et al. 2015

IEEE Trans. on Image Process.

167

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Liver segmentation is still a challenging task in medical image processing area due to the complexity of the liver's anatomy, low contrast with adjacent organs, and presence of pathologies. This investigation was used to develop and validate an automated method to segment livers in CT images. The proposed framework consists of three steps: 1) preprocessing; 2) initialization; and 3) segmentation. In the first step, a statistical shape model is constructed based on the principal component analysis and the input image is smoothed using curvature anisotropic diffusion filtering. In the second step, the mean shape model is moved using thresholding and Euclidean distance transformation to obtain a coarse position in a test image, and then the initial mesh is locally and iteratively deformed to the coarse boundary, which is constrained to stay close to a subspace of shapes describing the anatomical variability. Finally, in order to accurately detect the liver surface, deformable graph cut was proposed, which effectively integrates the properties and inter-relationship of the input images and initialized surface. The proposed method was evaluated on 50 CT scan images, which are publicly available in two databases Sliver07 and 3Dircadb. The experimental results showed that the proposed method was effective and accurate for detection of the liver surface.

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Section: ) Quantitative Results and Comparisonsmentioning

confidence: 99%

Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images

Chen

Shi

et al. 2015

IEEE Trans. on Image Process.

167

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“…Linguraru et al [26] presented an automated segmentation of livers from abdominal CT images, in which an affine invariant shape parameterization is combined with a geodesic active contour and graph cuts. A geodesic active contour locally corrects the segmentations of organs in abnormal images of abnormal liver, while the optimized graph cuts segment the vasculature and hepatic tumors using shape and enhancement constraints.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Method for Fully Automatic Liver Segmentation in Medical MRI-Images

Mohamed

Seada

Hamdy

et al. 2017

IJCA

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Despite the importance of the liver segmentation in the medical images for efficient noninvasive diagnosis, few studies found in the literatures for fully automated methods for liver segmentation in Magnetic Resonance Imaging (MRI) compared to that in Computed Tomography (CT) scans. Motivated by this, we propose an adaptive fully automatic liver segmentation method for MRI images based on thresholding and Bayesian classification. Bayesian classifications have proved to be highly robust to various image degradations. It only requires a small amount of training data to estimate the parameters necessary for classification, which is a huge advantage in medical applications. Furthermore, the Bayesian model is robust when large uncertainties are involved in medical image analysis problems. The proposed method is successfully tested on many MRI cases acquired from different patients, in various sizes. Experiments proved the robustness of the proposed liver automatic segmentation process even on data from different scanner types. The segmentation accuracy of the model has a mean Dice Similarity Coefficient (DSC) of 95.5% for MRI datasets.

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“…Liver masks were obtained using an automated method previously developed by our group [13]. Once the liver was segmented, a graph-cut approach was applied to find the hepatic tumors.…”

Section: Graph Cutsmentioning

confidence: 99%

Robust automated detection, segmentation, and classification of hepatic tumors from CT data

et al. 2012

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The manuscript presents the automated detection and segmentation of hepatic tumors from abdominal CT images with variable acquisition parameters. After obtaining an initial segmentation of the liver, optimized graph cuts segment the liver tumor candidates using shape and enhancement constraints. One hundred and fifty-seven features are computed for the tumor candidates and support vector machines are used to select features and separate true and false detections. Training and testing are performed using leave-one-patientout on 14 patients with a total of 79 tumors. After selection, the feature space is reduced to eight. The resulting sensitivity for tumor detection was 100% at 2.3 false positives/case. For the true tumors, 74.1% overlap and 1.6mm average surface distance were recorded between the ground truth and the results of the automated method. Results from test data demonstrate the method's robustness to analyze livers from difficult clinical cases to allow the diagnoses and temporal monitoring of patients with hepatic cancer.

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Liver and Tumor Segmentation and Analysis from CT of Diseased Patients via a Generic Affine Invariant Shape Parameterization and Graph Cuts

Cited by 24 publications

References 20 publications

Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images

Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images

An Adaptive Method for Fully Automatic Liver Segmentation in Medical MRI-Images

Robust automated detection, segmentation, and classification of hepatic tumors from CT data

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