3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy<i> C</i>-Means and Graph Cuts

Wu, Weiwei; Wu, Shuicai; Zhou, Zhuhuang; Zhang, Rui; Zhang, Yanhua

doi:10.1155/2017/5207685

Cited by 54 publications

(34 citation statements)

References 28 publications

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“…Moghbel et al[53] utilized additional the MIDAS dataset while Li et al[54] used the SLIVER07 dataset in the training, respectively. In addition, Foruzan and Chen[50] and Wu et al[51] achieved good results on tumor segmentation by semi-automatic methods.…”

mentioning

confidence: 97%

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

Chen

et al. 2018

IEEE Trans. Med. Imaging

1,852

974

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Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2-D and 3-D FCNs, serve as the backbone in many volumetric image segmentation. However, 2-D convolutions cannot fully leverage the spatial information along the third dimension while 3-D convolutions suffer from high computational cost and GPU memory consumption. To address these issues, we propose a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2-D DenseUNet for efficiently extracting intra-slice features and a 3-D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation. We formulate the learning process of the H-DenseUNet in an end-to-end manner, where the intra-slice representations and inter-slice features can be jointly optimized through a hybrid feature fusion layer. We extensively evaluated our method on the data set of the MICCAI 2017 Liver Tumor Segmentation Challenge and 3DIRCADb data set. Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.

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mentioning

confidence: 97%

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

Chen

et al. 2018

IEEE Trans. Med. Imaging

1,852

974

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“…The FCM algorithm assigns data to each category through the use of fuzzy memberships [38][39][40][41]. Let = ( 1 , 2 , .…”

Section: Fuzzy Means Algorithmmentioning

confidence: 99%

Stroke Subtype Clustering by Multifractal Bayesian Denoising with Fuzzy C Means and K‐Means Algorithms

et al. 2018

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Multifractal denoising techniques capture interest in biomedicine, economy, and signal and image processing. Regarding stroke data there are subtle details not easily detectable by eye physicians. For the stroke subtypes diagnosis, details are important due to including hidden information concerning the possible existence of medical history, laboratory results, and treatment details. Recently, K-means and fuzzy C means (FCM) algorithms have been applied in literature with many datasets. We present efficient clustering algorithms to eliminate irregularities for a given set of stroke dataset using 2D multifractal denoising techniques (Bayesian (mBd), Nonlinear (mNold), and Pumping (mPumpD)). Contrary to previous methods, our method embraces the following assets: (a) not applying the reduction of the stroke datasets’ attributes, leading to an efficient clustering comparison of stroke subtypes with the resulting attributes; (b) detecting attributes that eliminate “insignificant” irregularities while keeping “meaningful” singularities; (c) yielding successful clustering accuracy performance for enhancing stroke data qualities. Therefore, our study is a comprehensive comparative study with stroke datasets obtained from 2D multifractal denoised techniques applied for K-means and FCM clustering algorithms. Having been done for the first time in literature, 2D mBd technique, as revealed by results, is the most successful feature descriptor in each stroke subtype dataset regarding the mentioned algorithms’ accuracy rates.

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“…The use of FCDH in background subtraction reduced the number of false errors due to the illumination variation and indicated an efficient improvement in moving object detection [23]. Other recent studies have employed Fuzzy C-means algorithms for automated classification and segmentation techniques [24,25]. Ouma and Hahn developed a vision-based detection method using morphological reconstruction and Fuzzy C-means clustering.…”

Section: Introductionmentioning

confidence: 99%

Development of Machine Vision System for Pen Parts Identification under Various Illumination Conditions in an Industry 4.0 Environment

Bagheri¹,

Venn²,

Mosaddegh³

2020

Preprint

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The fourth Industrial Revolution, well-known as "Industry 4.0", based on the integration of information and communication technologies, has introduced significant improvements in manufacturing. However, vision systems still experience various impracticalities in dealing with the effect of complex lighting on the systems platform. Therefore, a machine vision system for automatic identification of pen parts under varying lighting conditions at a digital learning factory is proposed. The developed vision system presented a straightforward approach by effectively minimizing the environmental lighting effect on the identification process. First, the obtained information of the designed vision framework is exported to a program, where a reduction of non-uniform illumination is achieved through the implementation of Retinex image enhancement techniques. Then, the color-based Fuzzy C-means (FCM) algorithm, including improved mark watershed segmentation, is employed for pen parts object classification. Finally, the position features of the selected pen part are reported. The process applied to a total number of 210 upper pen parts (caps) and 241 lower pen parts (tubes) images under different lighting scenarios. Results indicate that average parts identification precision for cap and tube parts is different and equals to 98.64% and 95.26%, respectively. The present methodology provides a promising scheme that can be feasibly adapted for other industrial Color-based object recognition applications.

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3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts

Cited by 54 publications

References 28 publications

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

Stroke Subtype Clustering by Multifractal Bayesian Denoising with Fuzzy C Means and K‐Means Algorithms

Development of Machine Vision System for Pen Parts Identification under Various Illumination Conditions in an Industry 4.0 Environment

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