Modified Otsu thresholding based level set and local directional ternary pattern technique for liver tumor segmentation

2022 ◽  
Author(s):  
Deepak S. Uplaonkar ◽  
Virupakshappa ◽  
Nagabhushan Patil
Keyword(s):  
Level Set ◽  
Liver Tumor ◽  
Tumor Segmentation ◽  
Otsu Thresholding ◽  
Liver Tumor Segmentation

Liver tumor segmentation based on level set

2018 ◽  
Author(s):  
Qianqian Pan ◽  
Hai Li ◽  
Liwei Zhang ◽  
Li Xia ◽  
Hongzhi Wang
Keyword(s):  
Level Set ◽  
Liver Tumor ◽  
Tumor Segmentation ◽  
Liver Tumor Segmentation

Ultrasound liver tumor segmentation using adaptively regularized kernel-based fuzzy C means with enhanced level set algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Deepak S. Uplaonkar ◽  
Virupakshappa ◽  
Nagabhushan Patil
Keyword(s):  
Level Set ◽  
Liver Tumor ◽  
Experimental Analysis ◽  
Tumor Segmentation ◽  
Ultrasound Images ◽  
Dice Coefficient ◽  
Content Type ◽  
Fuzzy C Means ◽  
Local Ternary Pattern ◽  
Liver Tumor Segmentation

PurposeThe purpose of this study is to develop a hybrid algorithm for segmenting tumor from ultrasound images of the liver.Design/methodology/approachAfter collecting the ultrasound images, contrast-limited adaptive histogram equalization approach (CLAHE) is applied as preprocessing, in order to enhance the visual quality of the images that helps in better segmentation. Then, adaptively regularized kernel-based fuzzy C means (ARKFCM) is used to segment tumor from the enhanced image along with local ternary pattern combined with selective level set approaches.FindingsThe proposed segmentation algorithm precisely segments the tumor portions from the enhanced images with lower computation cost. The proposed segmentation algorithm is compared with the existing algorithms and ground truth values in terms of Jaccard coefficient, dice coefficient, precision, Matthews correlation coefficient, f-score and accuracy. The experimental analysis shows that the proposed algorithm achieved 99.18% of accuracy and 92.17% of f-score value, which is better than the existing algorithms.Practical implicationsFrom the experimental analysis, the proposed ARKFCM with enhanced level set algorithm obtained better performance in ultrasound liver tumor segmentation related to graph-based algorithm. However, the proposed algorithm showed 3.11% improvement in dice coefficient compared to graph-based algorithm.Originality/valueThe image preprocessing is carried out using CLAHE algorithm. The preprocessed image is segmented by employing selective level set model and Local Ternary Pattern in ARKFCM algorithm. In this research, the proposed algorithm has advantages such as independence of clustering parameters, robustness in preserving the image details and optimal in finding the threshold value that effectively reduces the computational cost.

A Likelihood and Local Constraint Level Set Model for Liver Tumor Segmentation from CT Volumes

2013 ◽  
Vol 60 (10) ◽  
pp. 2967-2977 ◽  
Author(s):  
Changyang Li ◽  
Xiuying Wang ◽  
Stefan Eberl ◽  
Michael Fulham ◽  
Yong Yin ◽  
...  
Keyword(s):  
Level Set ◽  
Liver Tumor ◽  
Tumor Segmentation ◽  
Local Constraint ◽  
Liver Tumor Segmentation ◽  
Constraint Level ◽  
Level Set Model

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

2018 ◽  
Vol 2018 ◽  
pp. 1-26 ◽  
Author(s):  
Zhou Zheng ◽  
Xuechang Zhang ◽  
Huafei Xu ◽  
Wang Liang ◽  
Siming Zheng ◽  
...  
Keyword(s):  
Level Set ◽  
Liver Tumor ◽  
Region Growing ◽  
Binary Image ◽  
Tumor Segmentation ◽  
Liver Segmentation ◽  
Segmentation Method ◽  
Liver Tumor Segmentation ◽  
Public Datasets ◽  
Level Set Framework

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.

Multi-stage Threshold Method for Liver Tumor Segmentation in CT Scan Images and Its Implementation for FPGA

2020 ◽  
Author(s):  
Y. Rakesh Kumar ◽  
Dr. Chandra Sekhar V ◽  
Sravanthi A ◽  
B. Sreekanth Reddy
Keyword(s):  
Ct Scan ◽  
Liver Tumor ◽  
Tumor Segmentation ◽  
Threshold Method ◽  
Multi Stage ◽  
Liver Tumor Segmentation

Liver tumor segmentation using 2.5D UV-Net with multi-scale convolution

2021 ◽  
pp. 104424
Author(s):  
C. Zhang ◽  
Q.-Q. Hua ◽  
Y.-Y. Chu ◽  
P.-W. Wang
Keyword(s):  
Liver Tumor ◽  
Tumor Segmentation ◽  
Multi Scale ◽  
Liver Tumor Segmentation
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