Touching tooth segmentation from CT image sequences using coupled level set method

2008 ◽  
Author(s):  
Hui Gao ◽  
Oksam Chae
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Image Sequences ◽  
Ct Image ◽  
Tooth Segmentation

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 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 SEPARATION OF OVERLAPPING OBJECT SEGMENTATION USING LEVEL SET WITH AUTOMATIC INITALIZATION ON DENTAL PANORAMIC RADIOGRAPH

2020 ◽  
Vol 13 (1) ◽  
pp. 25
Author(s):  
Safri Adam ◽  
Agus Zainal Arifin
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Hierarchical Cluster ◽  
Panoramic Radiograph ◽  
Forensic Odontology ◽  
Automatic Initialization ◽  
Dental Panoramic Radiograph ◽  
Human Age Estimation ◽  
Tooth Segmentation

To extract features on dental objects, it is necessary to segment the teeth. Segmentation is separating between the teeth (objects) with another part than teeth (background). The process of segmenting individual teeth has done a lot of the recently research and obtained good results. However, when faced with overlapping teeth, this is quite challenging. Overlapping tooth segmentation using the latest algorithm produces an object that should be segmented into two objects, instantly becoming one object. This is due to the overlapping between two teeth. To separate overlapping teeth, it is necessary to extract the overlapping object first. Level set method is widely used to segment overlap objects, but it has a limitation that needs to define the initial level set method manually by the user. In this study, an automatic initialization strategy is proposed for the level set method to segment overlapping teeth using hierarchical cluster analysis on dental panoramic radiographs images. The proposed strategy was able to initialize overlapping objects properly with accuracy of 73%.  Evaluation to measure quality of segmentation result are using misscassification error (ME) and relative foreground area error (RAE). ME and RAE were calculated based on the average results of individual tooth segmentation and obtain 16.41% and 52.14%, respectively. This proposed strategy are expected to be able to help separate the overlapping teeth for human age estimation through dental images in forensic odontology.

scholarly journals An improved level set method for vertebra CT image segmentation

2013 ◽  
Vol 12 (1) ◽  
pp. 48 ◽  
Author(s):  
Juying Huang ◽  
Fengzeng Jian ◽  
Hao Wu ◽  
Haiyun Li
Keyword(s):  
Image Segmentation ◽  
Level Set Method ◽  
Level Set ◽  
Ct Image
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