A 3D Segmentation and Visualization Scheme for Solid and Non-solid Lung Lesions Based on Gaussian Filtering Regularized Level Set

2014 ◽  
Author(s):  
Liansheng Wang ◽  
Huangjing Lin ◽  
Xiaoyang Huang ◽  
Boliang Wang ◽  
Yiping Chen
Keyword(s):  
Level Set ◽  
3D Segmentation ◽  
Lung Lesions ◽  
Gaussian Filtering

Edge Based Level Set with Gaussian Filtering Regularized and its Application in Liver Segmentation

2010 ◽  
Vol 121-122 ◽  
pp. 222-227 ◽  
Author(s):  
Rui Jie Feng ◽  
Hui Yan Jiang
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Active Contours ◽  
Active Contour Model ◽  
Synthetic Image ◽  
Gaussian Filter ◽  
Gaussian Filtering ◽  
Level Set Function ◽  
Contour Model ◽  
Edge Based

A novel edge-based active contour model (ACM) is proposed in this paper. Our edge-based active contour model has many advantages over the conventional active contour models. Firstly, the proposed model can get much smoother contour and needs much less iterations to evolution by being implemented with a special processing named Selectively Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) method. Secondly, we introduce Bilateral Gaussian Filter which can preserve edges to smooth images. So we make weak edges more clear than traditional Gaussian Filter. Thirdly, the level set function can be easily initialized with binary function, which is more efficient to construct than the widely used signed distance function (SDF) because of the special processing. Experiments on synthetic image and segmenting liver from abdominal CT images demonstrate the advantages of the proposed method over geodesic active contours (GAC) in term of both efficiency and accuracy.

scholarly journals Streaming level set algorithm for 3D segmentation of confocal microscopy images

2009 ◽  
Author(s):  
A. Gouaillard ◽  
K. Mosaliganti ◽  
A. Gelas ◽  
L. Souhait ◽  
N. Obholzer ◽  
...  
Keyword(s):  
Confocal Microscopy ◽  
Level Set ◽  
3D Segmentation ◽  
Microscopy Images

scholarly journals 3D Shape-Weighted Level Set Method for Breast MRI 3D Tumor Segmentation

2018 ◽  
Vol 2018 ◽  
pp. 1-15
Author(s):  
Chuin-Mu Wang ◽  
Chieh-Ling Huang ◽  
Sheng-Chih Yang
Keyword(s):  
Image Segmentation ◽  
Level Set Method ◽  
Level Set ◽  
Three Dimensional ◽  
Breast Mri ◽  
Medical Image Segmentation ◽  
Tumor Segmentation ◽  
3D Segmentation ◽  
3D Shape ◽  
Segmentation Methods

Three-dimensional (3D) medical image segmentation is used to segment the target (a lesion or an organ) in 3D medical images. Through this process, 3D target information is obtained; hence, this technology is an important auxiliary tool for medical diagnosis. Although some methods have proved to be successful for two-dimensional (2D) image segmentation, their direct use in the 3D case has been unsatisfactory. To obtain more precise tumor segmentation results from 3D MR images, in this paper, we propose a method known as the 3D shape-weighted level set method (3D-SLSM). The proposed method first converts the LSM, which is superior with respect to 2D image segmentation, into a 3D algorithm that is suitable for overall calculations in 3D image models, and which improves the efficiency and accuracy of calculations. A 3D shape-weighted value is then added for each 3D-SLSM iterative process according to the changes in volume. Besides increasing the convergence rate and eliminating background noise, this shape-weighted value also brings the segmented contour closer to the actual tumor margins. To perform a quantitative analysis of 3D-SLSM and to examine its feasibility in clinical applications, we have divided our experiments into computer-simulated sequence images and actual breast MRI cases. Subsequently, we simultaneously compared various existing 3D segmentation methods. The experimental results demonstrated that 3D-SLSM exhibited precise segmentation results for both types of experimental images. In addition, 3D-SLSM showed better results for quantitative data compared with existing 3D segmentation methods.

3D segmentation of medical volume image using hybrid level set method

2011 ◽  
Author(s):  
Myungeun Lee ◽  
Wanhyun Cho ◽  
Sunworl Kim ◽  
Yanjuan Chen ◽  
Soohyung Kim
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
3D Segmentation

scholarly journals PENGGUNAAN METODE ACTIVE CONTOUR UNTUK SEGMENTASI PARASIT MALARIA PLASMODIUM FALCIPARUM

2015 ◽  
Vol 6 (1) ◽  
pp. 163 ◽  
Author(s):  
Endi Permata
Keyword(s):  
Plasmodium Falciparum ◽  
Level Set ◽  
Active Contour ◽  
Gaussian Filtering

ABSTRAK Plasmodium falciparum merupakan penyebab malaria tropika atau malaria falciparum. Spesies ini paling berbahaya dibandingkan keempat spesies lainnya karena dapat menyebabkan komplikasi malaria serebral. Parasit ini menyerang setiap eritrosit tanpa memandang umur, sehingga angka infeksi eritrosit (derajat parasitemia) sangat tinggi dan sering menyebabkan komplikasi berat antara lain syok, malaria serebral, gagal ginjal akut, hemolisis intravaskular, dan edema paru. Penulis tertarik untuk melakukan penelitian segmentasi pada citra parasit malaria plasmodium falciparum dengan metode active contour yang mengimplementasikan sebuah proses khusus yaitu Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS). Pada proses SBGFRLS, pertama kali dilakukan pengubahan fungsi level set kedalam bentuk biner, kemudian digunakan filter gaussian untuk meregularisasinya. Metode active contour Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) untuk segmentasi obyek sel darah terjangkit parasit plasmodium falciparum dalam mendeteksi tepi objek dapat melakukan segmentasi dengan baik pada suatu obyek sel darah yang memiliki intensitas interior tidak homogen. Kata kunci: plasmodium falciparum, active contour, selective binary and gaussian filtering regularized level set (SBGFRLS), filter gaussian.

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