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Semi-automatic Liver Segmentation in CT Images Through Intensity Separation and Region Growing
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Available Available online online at at www.sciencedirect.com www.sciencedirect.com Procedia Computer Science00 (2018) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia
Procedia Computer Science00 (2018) 000–000
www.elsevier.com/locate/procedia
Procedia Computer Science 131 (2018) 220–225
8th International Congress of Information and Communication Technology (ICICT-2018)
Semi-automatic Liver Segmentation in CT Images Through Intensity Separation and Region Growing Semi-automatic Liver Segmentation Through b, c CT Images Zheng Zhoua,b, Zhang Xue-chang , Zheng Si-mingin , Xu Hua-feia,b, Shi Yue-dinga,b Intensity Separation andUniversity, Region Growing Institute of Mechanical Engineering, Zhejiang Hangzhou310027, China
8th International Congress of Information and Communication Technology (ICICT-2018) 一
a
School of Mechanical and Energy Engineering, Ningbo Institute of Technology, Zhejiang University, Ningbo315000, China c Department Hepatobiliary Hernia, Ningbo China a,bof Minimally Invasive Surgery for b,一 c Li Hui-li hospital, Ningbo315040, a,b a,b
b
Zheng Zhou , Zhang Xue-chang
, Zheng Si-ming , Xu Hua-fei , Shi Yue-ding
a Institute of Mechanical Engineering, Zhejiang University, Hangzhou310027, China School of Mechanical and Energy Engineering, Ningbo Institute of Technology, Zhejiang University, Ningbo315000, China Abstract cDepartment of Minimally Invasive Surgery for Hepatobiliary Hernia, Ningbo Li Hui-li hospital, Ningbo315040, China b
Liver segmentation is considered as a challenge task, and accurate and reliable segmentation of liver is essential of the follow-up of liver treatment. In this paper, a novel liver segmentation method including intensity separation, region growing and Abstract morphological hole-filling is presented. Firstly, intensity separation is employed to increase the difference between the intensities of liver and its adjacent tissues. Then the following region growing algorithm is applied to segment the liver. And the Liver segmentation is considered challenge task, accurate andresults. reliableThe segmentation of liverwas is essential of with the follow-up morphological hole-filling is usedasata last to refine theand segmentation proposed method evaluated a patient of liver coming treatment. this paper, a novel liverThe segmentation methodandincluding intensity show separation, region growing anda dataset fromInNingbo Li Hui-li hospital. validation results surface rendering that the method provides morphological hole-filling presented. Firstly,This intensity separation is employed to increase the difference reliable and robust way forisliver segmentation. method could provide a reference for clinical practice. between the intensities of liver and its adjacent tissues. Then the following region growing algorithm is applied to segment the liver. And the © 2018 The Authors. Published by at Elsevier Keywords: liver segmentation, separation, regionthe growing, surface rendering morphological hole-filling isintensity used last to Ltd. refine segmentation results. The proposed method was evaluated with a patient This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) dataset coming from Ningbo Li Hui-li hospital. The validation results and surface rendering show that the method provides a Selection and peer-review under responsibility of the scientific committee of the 8th International Congress of Information and reliable and robust way for liver segmentation. This method could provide a reference for clinical practice. Communication Technology.
1. Introduction
Keywords: liver segmentation, intensity separation, region growing, surface rendering
Liver is an important organ of human beings, where lesions like tumor developing is sometimes fatal. And surgery is the only way to treat these serious diseases yet in most cases. However, for the complex anatomy of liver 1. Introduction ducts, liver surgery is still a challenging task these days. Thus, safe and easier liver surgeries are essential for patients and experts. Computer-aided surgery providing many efficient features, such as preoperative simulation, Liver is an support important of human beings, where lesions like tumor is sometimesliver fatal. And intraoperative andorgan postoperative evaluation, plays an increasing key roledeveloping in aiding conventional surgery. surgery is the only way to treat these serious diseases yet in most cases. However, for the complex anatomy of liver Besides, to ensure the reliability, computer-aided surgery requires precise liver model reconstructions of patients. At ducts, is still a challenging task these Thus, safecomputerized and easier liver surgeries are essential for present,liver datasurgery for liver model reconstructions mainlydays. comes from tomographic(CT) images and patients and experts. Computer-aided surgery providing many efficient features, such as preoperative simulation, magnetic resonance(MR) images. Therefore, it is vital to extract the liver regions from abdominal medical images intraoperative support and postoperative evaluation, plays an increasing key role in aiding conventional liver surgery. Besides, to ensure the reliability, computer-aided surgery requires precise liver model reconstructions of patients. At 一 Corresponding author. Tel.: +8613757412050 present, data for liver model reconstructions mainly comes from computerized tomographic(CT) images and E-mail address: [email protected] magnetic resonance(MR) images. Therefore, it is vital to extract the liver regions from abdominal medical images © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license https://creativecommons.org/licenses/by-nc-nd/4.0/) 一 Corresponding author. Tel.: +8613757412050 Selection and peer-review under responsibility of the scientific committee of the 8th International Congress of Information and Communication E-mail address: [email protected] Technology © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license https://creativecommons.org/licenses/by-nc-nd/4.0/) 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is anand open access article the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection peer-review underunder responsibility of the scientific committee of the 8th International Congress of Information and Communication Selection and peer-review under responsibility of the scientific committee of the 8th International Congress of Information and Technology Communication Technology 10.1016/j.procs.2018.04.206
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Zheng Zhou et al. / Procedia Computer Science 131 (2018) 220–225 Author name / Procedia Computer Science00 (2018) 000–000
221
(usually CT images) precisely. However, due to the blurry edges and low level of contrast which characterize the CT images, liver segmentation is also a challenging task. Manual segmentation is the current “gold-standard” technique for extracting liver from each CT image. Although manual segmentation could get accurate results, it is very time consuming and really a mental work. According to the statistics, it takes more than 30 minutes to finish this manual work for one patient on average. In order to segment liver in CT with efficiency and accuracy, many remarkable segmentation methods consisting of different techniques have been proposed. For instance, Heimann et al. [1] employed a deformable mesh with internal forces based on a statistic shape model (SSM) and external forces based on image data for automatic liver segmentation. And Beichel et al. [2] presented an approach with high interaction, which is based on a graph-cut segmentation and two refinement steps. Some other related works includes methods combining neural network [3, 4] and level set [5, 6]. Compared to automatic segmentation method, semi-automatic method that allows an accurate segmentation under full user control, is a key requirement for clinical practice [7]. In this paper, we presented a novel semi-automatic method including intensity separation, region growing and morphological hole filling. Intensity separation was employed to increase the difference between the intensities of liver and its adjacent tissues. Then the following region growing algorithm was applied to segment liver. In some cases, there would be holes in the segmentation results, so the morphological hole filling was used to refine the result. We evaluated the proposed method with a patient dataset coming from Ningbo Li Hui-li hospital. Besides, the final segmentation was used for surface rendering to test the practicability of our method. The proposed method provides a reliable and robust way for liver segmentation in CT. 2. The proposed method An overview of the proposed method is given in Fig. 1. For the method, firstly, since the original CT image is in DICOM format, the users are required to set a suitable window level and window width for the following segmentation. Secondly, gray scale histogram analysis is applied to find an approximate gray scale range of the liver region. Thirdly, the intensity separation is used to increase the difference between the intensities of liver and its adjacent tissues. Then the following region growing would be employed to segment the liver. And morphological post-processing is used at last for result refinement.
Fig. 1. Block diagram giving an overview of the segmentation algorithm.
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Zheng Zhou et al. / Procedia Computer Science 131 (2018) 220–225 Author name / Procedia Computer Science00 (2018) 000–000
3
2.1. Setting of window level and window width Because the original CT image is in the DICOM format, the liver area could not be clearly displayed, see Fig. 2a for an example. Thus the users are required to set a suitable window level and window width to highlight the liver area. As illustrated in Fig. 2b, we set window level and window width to 50 and 300, respectively. The liver area could be displayed more clearly for the following segmentation process.
(a)
(b)
Fig. 2. Window level and window width setting.
2.2. Gray histogram analysis Gray histogram analysis was employed to analysis the intensity distribution. As illustrated in Fig. 3, we made a gray histogram analysis of Fig. 2b. We could find that the approximate gray scale range of the liver region is in [95,115], and the intensities of kidney and heart are close to that of liver. In such a low-contrast situation, it is difficult to segment liver accurately.
Fig. 3. An example of gray histogram analysis.
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Zheng Zhou et al. / Procedia Computer Science 131 (2018) 220–225 Author name / Procedia Computer Science00 (2018) 000–000
223
2.3. Intensity separation Since intensities of liver are similar to that of adjacent tissues in some cases (such as Fig. 3), resulting low contrast. Intensity separation was proposed in our study to increase the difference between the intensities of liver and its adjacent tissues, and intensity separation is discussed below. Given an image I, whose intensities are in [0,255]. Assume a set α= {α1, α2, …, αN}, where αi is the intensity of each pixel in I, and N is the total number of pixels. Besides, assume the intensities interval of liver is [βL, βH]. In our study, we make an assumption that the intensity αi in I would be assigned into five sets. Specifically, these sets are defined as [0, βL-x], [βL-x, βL], [βL, βH], [βH, βH+y] and [βH+y,255], where x and y are constants, and their values are determined by the range of intensity difference between the liver and adjacent tissues. The smaller range corresponds to the smaller values. To increase the difference between the intensities of liver and its adjacent tissues, the intensity separation was defined as: 0, L x 0 L x, L L x , L L , H L , H H , H y H , H y y, 255 0 H
(1)
where λ is a constant that controls the degree of separation. The larger value of λ corresponds to the larger degree of separation. In this way, the difference between the intensities of liver and its adjacent tissues would be increased from 0 to λ. An example of intensity separation is shown in Fig. 4. Through gray histogram analysis, we could decide the intensity of liver is in [95,115]. Then we set x and y to 20 and 10, and intensities of this image was assigned into five sets [0,75], [75,95], [95,115], [115,125] and [125,255]. For intensity separation, we set λ to 20, these five sets would become 0, [25,45], [95,115], [165.175] and 0. The difference between the intensities of liver and its adjacent tissues would be increased from 0 to 20, and the contrast would be enhanced.
Fig. 4. An example of intensity separation.
2.4. Region growing In this process, region growing was employed to segment the liver from CT. After intensity separation, the difference between the intensities of liver and its adjacent tissues was increased, so region growing could be more adaptable to the growth threshold. Specifically, the growth threshold for region growing could be set relatively
224
Zheng Zhou et al. / Procedia Computer Science 131 (2018) 220–225 Author name / Procedia Computer Science00 (2018) 000–000
5
larger. After setting up threshold and then by manually selecting seed points to start region growing. Some examples of region growing segmentation are displayed in Fig. 5.
Fig 5. Some examples of region growing.
2.5. Morphologic post-processing Because of the presence of lesion or noise in the liver, there would be holes in the result of region growing in some cases. To solve this problem, morphological hole filling was used for result refinement. An example of post-processing is described in Fig. 6.
Fig 6. An example of morphological hole filling.
3. Surface rendering In this section, to test the practicability of our results, we reconstructed a liver model based on liver segmented results with Mimics® 17.0 (Materialise, Belgium) and Geomagic Studio 12 (Geomagic, America). We first imported the resulting liver sequences into Mimics to obtain a rough 3D model. Due to the error in the segmentation process, the rough model was not very consistent with the expectation. Thus, we used Geomagic Studio 12 to reprocess the model via surface smoothing, surface removal and surface reconstruction to obtain the final 3D model. As illustrated in Fig. 7, these four images are standard four views (top view, model view, side view and front view, respectively) of this 3D model.
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Zheng Zhou et al. / Procedia Computer Science 131 (2018) 220–225 Author name / Procedia Computer Science00 (2018) 000–000
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Fig 7. Surface rendering of the patient dataset.
4. Discussion and conclusion We proposed a novel semi-automatic method for liver segmentation in CT. This method mainly includes intensity separation, region growing and morphologic post-processing. Intensity separation was proposed to increase the difference between the intensities of liver and its adjacent tissues. This technique could handle well with lowcontrast CT images. Region growing is the only step that requires interaction to select seed to grow. Morphological hole filling was applied to reduce the segmentation error. The capabilities of the developed method were evaluated with a patient dataset. Moreover, surface rendering provided a high reductive 3D liver model. A limitation of our method is that the database of our experimental materials was small. Therefore, our experiment was short of sample diversity to some extent, we will increase the number of cases for evaluation. In summary, we proposed a promising semi-automatic method including three different algorithms. This hybrid framework integrates the advantages of each algorithm, and the results of validation show that this framework provides a reliable and robust way for liver segmentation. Acknowledgements This work was supported by Natural Science Foundation of Zhejiang Province, China under Grant Nos. LY17E050011, National Natural Science Foundation of China under Grant Nos. 51075362 and Ningbo funded project of research on key technologies of complex surgery for liver resection based on 3D printing under Grant Nos. 2015C50025. References 1. Heimann, T., Meinzer, H. and Wolf, I. A statistical deformable model for the segmentation of liver CT volumes. Proceedings of 3D
Segmentation in the clinic: A grand challenge (2007), 161-166. 2. Beichel, R., Bauer, C., Bornik, A., Sorantin, E. and Bischof, H. Liver segmentation in CT data: A segmentation refinement approach. Proceedings of 3D Segmentation in The Clinic: A Grand Challenge (2007), 235-245. 3. Dou, Q., Chen, H., Jin, Y., Yu, L., Qin, J. and Heng, P. 3d deeply supervised network for automatic liver segmentation from CT volumes. Proceeding of International conference on Medical Image Computing and Computer-Assisted Intervention, (2016), Springer, Greece, 149157. 4. Ben-Cohen, A., Diamant, I., Klang, E., Amitai, M. and Greenspan, H. Fully convolutional network for liver segmentation and lesions detection. Proceeding of International Workshop on Deep Learning in Medical Image Analysis, (2016), Springer, Quebec City, 77-85. 5. Yang, X., Yu, H. C., Choi, Y., Lee, W., Wang, B., Yang, J., Hwang, H., Kim, J. H., Song, J. and Cho, B. H. A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points. COMPUT METH PROG BIO, 113, 1 (2014), 69-79. 6. Suzuki, K., Huynh, H. T., Liu, Y., Calabrese, D., Zhou, K., Oto, A. and Hori, M. Computerized segmentation of liver in hepatic CT and MRI by means of level-set geodesic active contouring. Proceeding of Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, (2013), IEEE, 0saka, 2984-2987. 7. Heimann, T., Ginneken, B. V., Styner, M. A., Arzhaeva, Y. and Aurich, V. Comparison and Evaluation of Methods for Liver Segmentation from CT Datasets. IEEE Transactions on Medical Imaging 28, 8 (2009), 1251-1265.
ScienceDirect
Available Available online online at at www.sciencedirect.com www.sciencedirect.com Procedia Computer Science00 (2018) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia
Procedia Computer Science00 (2018) 000–000
www.elsevier.com/locate/procedia
Procedia Computer Science 131 (2018) 220–225
8th International Congress of Information and Communication Technology (ICICT-2018)
Semi-automatic Liver Segmentation in CT Images Through Intensity Separation and Region Growing Semi-automatic Liver Segmentation Through b, c CT Images Zheng Zhoua,b, Zhang Xue-chang , Zheng Si-mingin , Xu Hua-feia,b, Shi Yue-dinga,b Intensity Separation andUniversity, Region Growing Institute of Mechanical Engineering, Zhejiang Hangzhou310027, China
8th International Congress of Information and Communication Technology (ICICT-2018) 一
a
School of Mechanical and Energy Engineering, Ningbo Institute of Technology, Zhejiang University, Ningbo315000, China c Department Hepatobiliary Hernia, Ningbo China a,bof Minimally Invasive Surgery for b,一 c Li Hui-li hospital, Ningbo315040, a,b a,b
b
Zheng Zhou , Zhang Xue-chang
, Zheng Si-ming , Xu Hua-fei , Shi Yue-ding
a Institute of Mechanical Engineering, Zhejiang University, Hangzhou310027, China School of Mechanical and Energy Engineering, Ningbo Institute of Technology, Zhejiang University, Ningbo315000, China Abstract cDepartment of Minimally Invasive Surgery for Hepatobiliary Hernia, Ningbo Li Hui-li hospital, Ningbo315040, China b
Liver segmentation is considered as a challenge task, and accurate and reliable segmentation of liver is essential of the follow-up of liver treatment. In this paper, a novel liver segmentation method including intensity separation, region growing and Abstract morphological hole-filling is presented. Firstly, intensity separation is employed to increase the difference between the intensities of liver and its adjacent tissues. Then the following region growing algorithm is applied to segment the liver. And the Liver segmentation is considered challenge task, accurate andresults. reliableThe segmentation of liverwas is essential of with the follow-up morphological hole-filling is usedasata last to refine theand segmentation proposed method evaluated a patient of liver coming treatment. this paper, a novel liverThe segmentation methodandincluding intensity show separation, region growing anda dataset fromInNingbo Li Hui-li hospital. validation results surface rendering that the method provides morphological hole-filling presented. Firstly,This intensity separation is employed to increase the difference reliable and robust way forisliver segmentation. method could provide a reference for clinical practice. between the intensities of liver and its adjacent tissues. Then the following region growing algorithm is applied to segment the liver. And the © 2018 The Authors. Published by at Elsevier Keywords: liver segmentation, separation, regionthe growing, surface rendering morphological hole-filling isintensity used last to Ltd. refine segmentation results. The proposed method was evaluated with a patient This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) dataset coming from Ningbo Li Hui-li hospital. The validation results and surface rendering show that the method provides a Selection and peer-review under responsibility of the scientific committee of the 8th International Congress of Information and reliable and robust way for liver segmentation. This method could provide a reference for clinical practice. Communication Technology.
1. Introduction
Keywords: liver segmentation, intensity separation, region growing, surface rendering
Liver is an important organ of human beings, where lesions like tumor developing is sometimes fatal. And surgery is the only way to treat these serious diseases yet in most cases. However, for the complex anatomy of liver 1. Introduction ducts, liver surgery is still a challenging task these days. Thus, safe and easier liver surgeries are essential for patients and experts. Computer-aided surgery providing many efficient features, such as preoperative simulation, Liver is an support important of human beings, where lesions like tumor is sometimesliver fatal. And intraoperative andorgan postoperative evaluation, plays an increasing key roledeveloping in aiding conventional surgery. surgery is the only way to treat these serious diseases yet in most cases. However, for the complex anatomy of liver Besides, to ensure the reliability, computer-aided surgery requires precise liver model reconstructions of patients. At ducts, is still a challenging task these Thus, safecomputerized and easier liver surgeries are essential for present,liver datasurgery for liver model reconstructions mainlydays. comes from tomographic(CT) images and patients and experts. Computer-aided surgery providing many efficient features, such as preoperative simulation, magnetic resonance(MR) images. Therefore, it is vital to extract the liver regions from abdominal medical images intraoperative support and postoperative evaluation, plays an increasing key role in aiding conventional liver surgery. Besides, to ensure the reliability, computer-aided surgery requires precise liver model reconstructions of patients. At 一 Corresponding author. Tel.: +8613757412050 present, data for liver model reconstructions mainly comes from computerized tomographic(CT) images and E-mail address: [email protected] magnetic resonance(MR) images. Therefore, it is vital to extract the liver regions from abdominal medical images © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license https://creativecommons.org/licenses/by-nc-nd/4.0/) 一 Corresponding author. Tel.: +8613757412050 Selection and peer-review under responsibility of the scientific committee of the 8th International Congress of Information and Communication E-mail address: [email protected] Technology © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license https://creativecommons.org/licenses/by-nc-nd/4.0/) 1877-0509 © 2018 The Authors. Published by Elsevier Ltd. This is anand open access article the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection peer-review underunder responsibility of the scientific committee of the 8th International Congress of Information and Communication Selection and peer-review under responsibility of the scientific committee of the 8th International Congress of Information and Technology Communication Technology 10.1016/j.procs.2018.04.206
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Zheng Zhou et al. / Procedia Computer Science 131 (2018) 220–225 Author name / Procedia Computer Science00 (2018) 000–000
221
(usually CT images) precisely. However, due to the blurry edges and low level of contrast which characterize the CT images, liver segmentation is also a challenging task. Manual segmentation is the current “gold-standard” technique for extracting liver from each CT image. Although manual segmentation could get accurate results, it is very time consuming and really a mental work. According to the statistics, it takes more than 30 minutes to finish this manual work for one patient on average. In order to segment liver in CT with efficiency and accuracy, many remarkable segmentation methods consisting of different techniques have been proposed. For instance, Heimann et al. [1] employed a deformable mesh with internal forces based on a statistic shape model (SSM) and external forces based on image data for automatic liver segmentation. And Beichel et al. [2] presented an approach with high interaction, which is based on a graph-cut segmentation and two refinement steps. Some other related works includes methods combining neural network [3, 4] and level set [5, 6]. Compared to automatic segmentation method, semi-automatic method that allows an accurate segmentation under full user control, is a key requirement for clinical practice [7]. In this paper, we presented a novel semi-automatic method including intensity separation, region growing and morphological hole filling. Intensity separation was employed to increase the difference between the intensities of liver and its adjacent tissues. Then the following region growing algorithm was applied to segment liver. In some cases, there would be holes in the segmentation results, so the morphological hole filling was used to refine the result. We evaluated the proposed method with a patient dataset coming from Ningbo Li Hui-li hospital. Besides, the final segmentation was used for surface rendering to test the practicability of our method. The proposed method provides a reliable and robust way for liver segmentation in CT. 2. The proposed method An overview of the proposed method is given in Fig. 1. For the method, firstly, since the original CT image is in DICOM format, the users are required to set a suitable window level and window width for the following segmentation. Secondly, gray scale histogram analysis is applied to find an approximate gray scale range of the liver region. Thirdly, the intensity separation is used to increase the difference between the intensities of liver and its adjacent tissues. Then the following region growing would be employed to segment the liver. And morphological post-processing is used at last for result refinement.
Fig. 1. Block diagram giving an overview of the segmentation algorithm.
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Zheng Zhou et al. / Procedia Computer Science 131 (2018) 220–225 Author name / Procedia Computer Science00 (2018) 000–000
3
2.1. Setting of window level and window width Because the original CT image is in the DICOM format, the liver area could not be clearly displayed, see Fig. 2a for an example. Thus the users are required to set a suitable window level and window width to highlight the liver area. As illustrated in Fig. 2b, we set window level and window width to 50 and 300, respectively. The liver area could be displayed more clearly for the following segmentation process.
(a)
(b)
Fig. 2. Window level and window width setting.
2.2. Gray histogram analysis Gray histogram analysis was employed to analysis the intensity distribution. As illustrated in Fig. 3, we made a gray histogram analysis of Fig. 2b. We could find that the approximate gray scale range of the liver region is in [95,115], and the intensities of kidney and heart are close to that of liver. In such a low-contrast situation, it is difficult to segment liver accurately.
Fig. 3. An example of gray histogram analysis.
4
Zheng Zhou et al. / Procedia Computer Science 131 (2018) 220–225 Author name / Procedia Computer Science00 (2018) 000–000
223
2.3. Intensity separation Since intensities of liver are similar to that of adjacent tissues in some cases (such as Fig. 3), resulting low contrast. Intensity separation was proposed in our study to increase the difference between the intensities of liver and its adjacent tissues, and intensity separation is discussed below. Given an image I, whose intensities are in [0,255]. Assume a set α= {α1, α2, …, αN}, where αi is the intensity of each pixel in I, and N is the total number of pixels. Besides, assume the intensities interval of liver is [βL, βH]. In our study, we make an assumption that the intensity αi in I would be assigned into five sets. Specifically, these sets are defined as [0, βL-x], [βL-x, βL], [βL, βH], [βH, βH+y] and [βH+y,255], where x and y are constants, and their values are determined by the range of intensity difference between the liver and adjacent tissues. The smaller range corresponds to the smaller values. To increase the difference between the intensities of liver and its adjacent tissues, the intensity separation was defined as: 0, L x 0 L x, L L x , L L , H L , H H , H y H , H y y, 255 0 H
(1)
where λ is a constant that controls the degree of separation. The larger value of λ corresponds to the larger degree of separation. In this way, the difference between the intensities of liver and its adjacent tissues would be increased from 0 to λ. An example of intensity separation is shown in Fig. 4. Through gray histogram analysis, we could decide the intensity of liver is in [95,115]. Then we set x and y to 20 and 10, and intensities of this image was assigned into five sets [0,75], [75,95], [95,115], [115,125] and [125,255]. For intensity separation, we set λ to 20, these five sets would become 0, [25,45], [95,115], [165.175] and 0. The difference between the intensities of liver and its adjacent tissues would be increased from 0 to 20, and the contrast would be enhanced.
Fig. 4. An example of intensity separation.
2.4. Region growing In this process, region growing was employed to segment the liver from CT. After intensity separation, the difference between the intensities of liver and its adjacent tissues was increased, so region growing could be more adaptable to the growth threshold. Specifically, the growth threshold for region growing could be set relatively
224
Zheng Zhou et al. / Procedia Computer Science 131 (2018) 220–225 Author name / Procedia Computer Science00 (2018) 000–000
5
larger. After setting up threshold and then by manually selecting seed points to start region growing. Some examples of region growing segmentation are displayed in Fig. 5.
Fig 5. Some examples of region growing.
2.5. Morphologic post-processing Because of the presence of lesion or noise in the liver, there would be holes in the result of region growing in some cases. To solve this problem, morphological hole filling was used for result refinement. An example of post-processing is described in Fig. 6.
Fig 6. An example of morphological hole filling.
3. Surface rendering In this section, to test the practicability of our results, we reconstructed a liver model based on liver segmented results with Mimics® 17.0 (Materialise, Belgium) and Geomagic Studio 12 (Geomagic, America). We first imported the resulting liver sequences into Mimics to obtain a rough 3D model. Due to the error in the segmentation process, the rough model was not very consistent with the expectation. Thus, we used Geomagic Studio 12 to reprocess the model via surface smoothing, surface removal and surface reconstruction to obtain the final 3D model. As illustrated in Fig. 7, these four images are standard four views (top view, model view, side view and front view, respectively) of this 3D model.
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Zheng Zhou et al. / Procedia Computer Science 131 (2018) 220–225 Author name / Procedia Computer Science00 (2018) 000–000
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Fig 7. Surface rendering of the patient dataset.
4. Discussion and conclusion We proposed a novel semi-automatic method for liver segmentation in CT. This method mainly includes intensity separation, region growing and morphologic post-processing. Intensity separation was proposed to increase the difference between the intensities of liver and its adjacent tissues. This technique could handle well with lowcontrast CT images. Region growing is the only step that requires interaction to select seed to grow. Morphological hole filling was applied to reduce the segmentation error. The capabilities of the developed method were evaluated with a patient dataset. Moreover, surface rendering provided a high reductive 3D liver model. A limitation of our method is that the database of our experimental materials was small. Therefore, our experiment was short of sample diversity to some extent, we will increase the number of cases for evaluation. In summary, we proposed a promising semi-automatic method including three different algorithms. This hybrid framework integrates the advantages of each algorithm, and the results of validation show that this framework provides a reliable and robust way for liver segmentation. Acknowledgements This work was supported by Natural Science Foundation of Zhejiang Province, China under Grant Nos. LY17E050011, National Natural Science Foundation of China under Grant Nos. 51075362 and Ningbo funded project of research on key technologies of complex surgery for liver resection based on 3D printing under Grant Nos. 2015C50025. References 1. Heimann, T., Meinzer, H. and Wolf, I. A statistical deformable model for the segmentation of liver CT volumes. Proceedings of 3D
Segmentation in the clinic: A grand challenge (2007), 161-166. 2. Beichel, R., Bauer, C., Bornik, A., Sorantin, E. and Bischof, H. Liver segmentation in CT data: A segmentation refinement approach. Proceedings of 3D Segmentation in The Clinic: A Grand Challenge (2007), 235-245. 3. Dou, Q., Chen, H., Jin, Y., Yu, L., Qin, J. and Heng, P. 3d deeply supervised network for automatic liver segmentation from CT volumes. Proceeding of International conference on Medical Image Computing and Computer-Assisted Intervention, (2016), Springer, Greece, 149157. 4. Ben-Cohen, A., Diamant, I., Klang, E., Amitai, M. and Greenspan, H. Fully convolutional network for liver segmentation and lesions detection. Proceeding of International Workshop on Deep Learning in Medical Image Analysis, (2016), Springer, Quebec City, 77-85. 5. Yang, X., Yu, H. C., Choi, Y., Lee, W., Wang, B., Yang, J., Hwang, H., Kim, J. H., Song, J. and Cho, B. H. A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points. COMPUT METH PROG BIO, 113, 1 (2014), 69-79. 6. Suzuki, K., Huynh, H. T., Liu, Y., Calabrese, D., Zhou, K., Oto, A. and Hori, M. Computerized segmentation of liver in hepatic CT and MRI by means of level-set geodesic active contouring. Proceeding of Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, (2013), IEEE, 0saka, 2984-2987. 7. Heimann, T., Ginneken, B. V., Styner, M. A., Arzhaeva, Y. and Aurich, V. Comparison and Evaluation of Methods for Liver Segmentation from CT Datasets. IEEE Transactions on Medical Imaging 28, 8 (2009), 1251-1265.