Accelerated liver tumor segmentation in four-phase computed tomography images

Chaieb, Faten; Said, Tarek; Mabrouk, Sabra; Ghorbel, Faouzi

doi:10.1007/s11554-016-0578-y

Cited by 16 publications

(9 citation statements)

References 17 publications

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“…The MIDAS dataset provides manual segmentation from 5 radiologists, so it allows us to evaluate the robustness of our method according to different manual delineations. Note that we were only interested in metastasis tumors in this validation experiment, so the tumor from patient 4 was not used for method validation because it is a Hepatocellular carcinoma (HCC) [ 54 ]. For method validation, we compared each segmentation result with its five corresponding manual delineations to get five sets of metric values and took the average of the values for the five sets as the final metric values for each segmentation result.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

“…It took about 162 s to segment a tumor, most of which was spent by the HMRF-EM algorithm and the unified LSM. The HMRF-EM algorithm is known to be a time-consuming algorithm, especially for handling large-size images [ 54 ]. So for the future work, in addition to accelerating the unified LSM evolution, we will consider proposing a scheme to boost the HMRF-EM process.…”

Section: Discussionmentioning

confidence: 99%

“…In our study, we use a rectangle to define a ROI. In addition, it is important to notice that if we consider the case of tumors located on the liver borders and adjacent to other structures in the abdomen, then the choice of a ROI exceeding the limits of the tumor may affect the segmentation result [ 54 ]. Thus, a ROI must be selected as close as possible to the tumor to avoid such problems.…”

Section: Methodsmentioning

confidence: 99%

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A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images

Zheng

Zhang

et al. 2018

BioMed Research International

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Accurate and reliable segmentation of liver tissue and liver tumor is essential for the follow-up of hepatic diagnosis. In this paper, we present a method for liver segmentation and a method for liver tumor segmentation. The two methods are grounded on a novel unified level set method (LSM), which incorporates both region information and edge information to evolve the contour. This level set framework is more resistant to edge leakage than the single-information driven LSMs for liver segmentation and surpasses many other models for liver tumor segmentation. Specifically, for liver segmentation, a hybrid image preprocessing scheme is used first to convert an input CT image into a binary image. Then with manual setting of a few seed points on the obtained binary image, the following region-growing is performed to extract a rough liver region with no leakage. The unified LSM is proposed at last to refine the segmentation result. For liver tumor segmentation, a local intensity clustering based LSM coupled with hidden Markov random field and expectation-maximization (HMRF-EM) algorithm is applied to construct an enhanced edge indicator for the unified LSM. With this development, expected segmentation results can be obtained via the unified LSM, even for complex tumors. The two methods were evaluated with various datasets containing a local hospital dataset, the public datasets SLIVER07, 3Dircadb, and MIDAS via five measures. The proposed liver segmentation method outperformed other previous semiautomatic methods on the SLIVER07 dataset and required less interaction. The proposed liver tumor segmentation method was also competitive with other state-of-the-art methods in both accuracy and efficiency on the 3Dircadb database. Our methods are evaluated to be accurate and efficient, which allows their adoptions in clinical practice.

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Section: Evaluation and Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images

Zheng

Zhang

et al. 2018

BioMed Research International

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“…Kuo et al 14 applied an SVM classifier using a liver tumor texture feature vector. Chaieb et al 17 implemented a bootstrap sampling technique for effective liver tumor isolation. Smeets et al 18 implemented a semiautomatic level‐set technique that merges a fuzzy supervised pixel classification technique.…”

Section: Related Workmentioning

confidence: 99%

Automatic segmentation system for liver tumors based on the multilevel thresholding and electromagnetism optimization algorithm

Mahdy

Ezzat

Torad

et al. 2020

Int J Imaging Syst Tech

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In this article, we propose an automated segmentation system for liver tumors using magnetic resonance imaging and computed tomography. The proposed system is based on the algorithm of multilevel thresholding with electromagnetism optimization (EMO). The system starts with visualizing a patient's digital communication in medicine (DICOM) abdominal data set in three views. Two‐stage active contour segmentation methods that integrate region‐based local and global techniques using the active geodesic contour technique are proposed to segment the liver. To increase the accuracy and speed of segmentation for liver images, we identify the optimal threshold of the image segmentation method based on EMO with Otsu and Kapur algorithms. EMO offers interesting search capabilities while keeping a low computational cost. The proposed system was tested using a set of five DICOM data sets. All images were of the same size and stored in JPEG format (512 × 512 pixels). Experimental results illustrate that the proposed system outperforms state‐of‐the‐art methods such as the watershed algorithm. The average sensitivity, specificity, and accuracy of the segmented liver using the active contour model were 97.05%, 99.88%, and 98.47%, respectively. Moreover, the average sensitivity, specificity, and accuracy of the segmented liver tumor results were 94.15%, 99.57%, and 96.86%, respectively.

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“…Texture of an image can be categorized by coarseness, roughness, granulation in surface, randomness, and irregularity. All these features were described by intensity and a spatial array of pixels in the textural US images [15,16]. As shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Detection of cirrhosis through ultrasound imaging by intensity difference technique

Aggarwal

Bhamrah

Ryait³

2019

J Image Video Proc.

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Cirrhosis is a liver disease that is considered to be among the most common diseases in healthcare. Due to its noninvasive nature, ultrasound (US) imaging is a widely accepted technology for the diagnosis of this disease. This research work proposed a method for discriminating the cirrhotic liver from normal liver through US images. The liver US images were obtained from the radiologist. The radiologist also specified the region of interest (ROI) from these images, and then the proposed method was applied to it. Two parameters were extracted from the US images through differences in intensity of neighboring pixels. Then, these parameters can be used to train a classifier by which cirrhotic region of test patient can be detected. A 2-D array was created by the difference in intensity of the neighboring pixels. From this array, two parameters were calculated. The decision was taken by checking these parameters. The validation of the proposed tool was done on 80 images of cirrhotic and 30 images of normal liver, and classification accuracy of 98.18% was achieved. The result was also verified by the radiologist. The results verified its possibility and applicability for high-performance cirrhotic liver discrimination.

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Accelerated liver tumor segmentation in four-phase computed tomography images

Cited by 16 publications

References 17 publications

A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images

A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images

Automatic segmentation system for liver tumors based on the multilevel thresholding and electromagnetism optimization algorithm

Detection of cirrhosis through ultrasound imaging by intensity difference technique

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