scholarly journals A Hybrid Method for Pancreas Extraction from CT Image Based on Level Set Methods

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Huiyan Jiang ◽  
Hanqing Tan ◽  
Hiroshi Fujita
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Negative Impact ◽  
Level Set Methods ◽  
Region Growing ◽  
Ct Images ◽  
Medical Image Segmentation ◽  
Initial Contour ◽  
Ct Image ◽  
Fast Marching

This paper proposes a novel semiautomatic method to extract the pancreas from abdominal CT images. Traditional level set and region growing methods that request locating initial contour near the final boundary of object have problem of leakage to nearby tissues of pancreas region. The proposed method consists of a customized fast-marching level set method which generates an optimal initial pancreas region to solve the problem that the level set method is sensitive to the initial contour location and a modified distance regularized level set method which extracts accurate pancreas. The novelty in our method is the proper selection and combination of level set methods, furthermore an energy-decrement algorithm and an energy-tune algorithm are proposed to reduce the negative impact of bonding force caused by connected tissue whose intensity is similar with pancreas. As a result, our method overcomes the shortages of oversegmentation at weak boundary and can accurately extract pancreas from CT images. The proposed method is compared to other five state-of-the-art medical image segmentation methods based on a CT image dataset which contains abdominal images from 10 patients. The evaluated results demonstrate that our method outperforms other methods by achieving higher accuracy and making less false segmentation in pancreas extraction.

scholarly journals Segmentation of Image Using Watershed and Fast Level set methods

2012 ◽  
pp. 38-43
Author(s):  
Minal M. Purani ◽  
Shobha Krishnan
Keyword(s):  
Medical Imaging ◽  
Level Set Method ◽  
Level Set ◽  
Medical Images ◽  
Level Set Methods ◽  
Initial Boundary ◽  
Fast Marching Method ◽  
Fast Marching ◽  
Watershed Transform ◽  
Marching Method

Technology is proliferating. Many methods are used for medical imaging .The important methods used here are fast marching and level set in comparison with the watershed transform .Since watershed algorithm was applied to an image has over clusters in segmentation . Both methods are applied to segment the medical images. First, fast marching method is used to extract the rough contours. Then level set method is utilized to finely tune the initial boundary. Moreover, Traditional fast marching method was modified by the use of watershed transform. The method is feasible in medical imaging and deserves further research. It could be used to segment the white matter, brain tumor and other small and simple structured organs in CT and MR images. In the future, we will integrate level set method with statistical shape analysis to make it applicable to more kinds of medical images and have better robustness to noise.

scholarly journals A Priori Knowledge and Probability Density Based Segmentation Method for Medical CT Image Sequences

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Huiyan Jiang ◽  
Hanqing Tan ◽  
Benqiang Yang
Keyword(s):  
Probability Density ◽  
Level Set Method ◽  
Level Set ◽  
A Priori ◽  
Image Sequences ◽  
Medical Image Segmentation ◽  
A Priori Knowledge ◽  
Ct Image ◽  
Distance Regularization ◽  
Priori Knowledge

This paper briefly introduces a novel segmentation strategy for CT images sequences. As first step of our strategy, we extract a priori intensity statistical information from object region which is manually segmented by radiologists. Then we define a search scope for object and calculate probability density for each pixel in the scope using a voting mechanism. Moreover, we generate an optimal initial level set contour based on a priori shape of object of previous slice. Finally the modified distance regularity level set method utilizes boundaries feature and probability density to conform final object. The main contributions of this paper are as follows: a priori knowledge is effectively used to guide the determination of objects and a modified distance regularization level set method can accurately extract actual contour of object in a short time. The proposed method is compared to other seven state-of-the-art medical image segmentation methods on abdominal CT image sequences datasets. The evaluated results demonstrate our method performs better and has the potential for segmentation in CT image sequences.

scholarly journals ResBCDU-Net: A Deep Learning Framework for Lung CT Image Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 268
Author(s):  
Yeganeh Jalali ◽  
Mansoor Fateh ◽  
Mohsen Rezvani ◽  
Vahid Abolghasemi ◽  
Mohammad Hossein Anisi
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Ct Images ◽  
Medical Image Segmentation ◽  
Great Success ◽  
Morphological Operations ◽  
Ct Image ◽  
Feature Maps ◽  
Learning Framework ◽  
Lung Ct

Lung CT image segmentation is a key process in many applications such as lung cancer detection. It is considered a challenging problem due to existing similar image densities in the pulmonary structures, different types of scanners, and scanning protocols. Most of the current semi-automatic segmentation methods rely on human factors therefore it might suffer from lack of accuracy. Another shortcoming of these methods is their high false-positive rate. In recent years, several approaches, based on a deep learning framework, have been effectively applied in medical image segmentation. Among existing deep neural networks, the U-Net has provided great success in this field. In this paper, we propose a deep neural network architecture to perform an automatic lung CT image segmentation process. In the proposed method, several extensive preprocessing techniques are applied to raw CT images. Then, ground truths corresponding to these images are extracted via some morphological operations and manual reforms. Finally, all the prepared images with the corresponding ground truth are fed into a modified U-Net in which the encoder is replaced with a pre-trained ResNet-34 network (referred to as Res BCDU-Net). In the architecture, we employ BConvLSTM (Bidirectional Convolutional Long Short-term Memory)as an advanced integrator module instead of simple traditional concatenators. This is to merge the extracted feature maps of the corresponding contracting path into the previous expansion of the up-convolutional layer. Finally, a densely connected convolutional layer is utilized for the contracting path. The results of our extensive experiments on lung CT images (LIDC-IDRI database) confirm the effectiveness of the proposed method where a dice coefficient index of 97.31% is achieved.

scholarly journals Combining interpolation and 3D level set method (I+3DLSM) for medical image segmentation

2016 ◽  
Vol 52 (8) ◽  
pp. 592-594 ◽  
Author(s):  
T. Doshi ◽  
G. Di Caterina ◽  
J. Soraghan ◽  
L. Petropoulakis ◽  
D. Grose ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Level Set Method ◽  
Level Set ◽  
Medical Image ◽  
Medical Image Segmentation

Correntropy-based level set method for medical image segmentation and bias correction

2017 ◽  
Vol 234 ◽  
pp. 216-229 ◽  
Author(s):  
Sanping Zhou ◽  
Jinjun Wang ◽  
Mengmeng Zhang ◽  
Qing Cai ◽  
Yihong Gong
Keyword(s):  
Image Segmentation ◽  
Level Set Method ◽  
Level Set ◽  
Bias Correction ◽  
Medical Image ◽  
Medical Image Segmentation

CT Images Segmentation Based on Dynamic Relative Fuzzy Region Growing Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 4233-4236
Author(s):  
Zhen You Zhang ◽  
Guo Huan Lou
Keyword(s):  
Confidence Interval ◽  
Region Growing ◽  
Ct Images ◽  
Segmentation Algorithm ◽  
Experimental Results ◽  
Segmentation Result ◽  
Ct Image ◽  
Initial Segmentation ◽  
Fuzzy Region

Segmentation algorithm of CT Image is discussed in this paper. Dynamic relative fuzzy region growing algorithm is used for CT. At the beginning of the segmentation, the confidence interval region growing algorithm is used. The overlapping parts in the initial segmentation result is segmented again with the improved fuzzy connected, and then determine which region the overlapping parts belong to. Thus, the final segmentation result can be obtained. Since the algorithm contains the advantages of region growing algorithm, fuzzy connected algorithm and the region competition, the runtime of segmentation is greatly reduced and better experimental results are obtained.

Sign in / Sign up

Export Citation Format

Share Document