A versatile algorithm for the automatic segmentation of hippocampus based on level set

2011 ◽  
Vol 7 (3) ◽  
pp. 213 ◽  
Author(s):  
J. Rajeesh ◽  
R.S. Moni ◽  
S. Palanikumar ◽  
T. Gopalakrishnan
Keyword(s):  
Level Set ◽  
Automatic Segmentation

Medical Imaging Importance in the Real World

2020 ◽  
pp. 136-162
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Level Set ◽  
Real World ◽  
Medical Image ◽  
Automatic Segmentation ◽  
Medical Image Segmentation ◽  
Image Resolution ◽  
Variational Model ◽  
Medical Applications

In the medical image resolution, automatic segmentation is a challenging task, and it's still an unsolved problem for most medical applications due to the wide variety connected with image modalities, encoding parameters, and organic variability. In this chapter, a review and critique of medical image segmentation using clustering, compression, histogram, edge detection, parametric, variational model. and level set-based methods is presented. Modes of segmentation like manual, semi-automatic, interactive, and automatic are also discussed. To present current challenges, aim and motivation for doing fast, interactive and correct segmentation, the medical image modalities X-ray, CT, MRI, and PET are discussed in this chapter.

Automatic Correction of Gaps in Cerebrovascular Segmentations Extracted from 3D Time-of-Flight MRA Datasets

2012 ◽  
Vol 51 (05) ◽  
pp. 415-422 ◽  
Author(s):  
A. Schmidt-Richberg ◽  
J. Fiehler ◽  
T. Illies ◽  
D. Möller ◽  
H. Handels ◽  
...  
Keyword(s):  
Level Set ◽  
Automatic Segmentation ◽  
Time Of Flight ◽  
Medical Expert ◽  
Consistency Check ◽  
Major Drawback ◽  
Segmentation Methods ◽  
Initial Segmentation ◽  
Level Set Approach ◽  
Manual Correction

Summary Objectives: Exact cerebrovascular segmentations are required for several applications in today’s clinical routine. A major drawback of typical automatic segmentation methods is the occurrence of gaps within the segmentation. These gaps are typically located at small vessel structures exhibiting low intensities. Manual correction is very time-consuming and not suitable in clinical practice. This work presents a post-processing method for the automatic detection and closing of gaps in cerebrovascular segmentations. Methods: In this approach, the 3D centerline is calculated from an available vessel segmentation, which enables the detection of corresponding vessel endpoints. These endpoints are then used to detect possible connections to other 3D centerline voxels with a graph-based approach. After consistency check, reasonable detected paths are expanded to the vessel boundaries using a level set approach and combined with the initial segmentation. Results: For evaluation purposes, 100 gaps were artificially inserted at non-branching vessels and bifurcations in manual cerebrovascular segmentations derived from ten Time-of-Flight magnetic resonance angiography datasets. The results show that the presented method is capable of detecting 82% of the non-branching vessel gaps and 84% of the bifurcation gaps. The level set segmentation expands the detected connections with 0.42 mm accuracy compared to the initial segmentations. A further evaluation based on 10 real automatic segmentations from the same datasets shows that the proposed method detects 35 additional connections in average per dataset, whereas 92.7% were rated as correct by a medical expert. Conclusion: The presented approach can considerably improve the accuracy of cerebrovascular segmentations and of following analysis outcomes.

Automatic Segmentation of the Skull in MRI Sequences Using Level Set Method

2011 ◽  
Vol 58-60 ◽  
pp. 2370-2375
Author(s):  
Wei Li Ding ◽  
Feng Jiang ◽  
Jia Qing Yan
Keyword(s):  
Level Set ◽  
Medical Image ◽  
Automatic Segmentation ◽  
Image Sequences ◽  
Initial Contour ◽  
Single Image ◽  
Mri Sequences ◽  
Magnetic Resonance Imaging Mri ◽  
Mri Image

Magnetic Resonance Imaging (MRI) has been widely used in clinical diagnose. Segmentation of these images obtained by MRI is a necessary procedure in medical image processing. In this paper, an improved level set algorithm was proposed to optimize the segmentation of MRI image sequences based on article [1]. Firstly, we add an area term and the edge indicator function to the total energy function for single image segmentation. Secondly, we presented a new method which uses the circumscribed polygon of the previous segmentation result as the initial contour of the next image to achieve automatic segmentation of image sequences. The algorithm was tested on MRI image sequences provided by Chuiyanliu Hospital, Chaoyang District of Beijing; the results have indicated that the proposed algorithm can effectively enhance the segmentation speed and quality of MRI sequences.

scholarly journals Segmentation of Meristem Cells by an Automated Optimization Algorithm

2020 ◽  
Vol 10 (23) ◽  
pp. 8523
Author(s):  
Oswaldo Rojas ◽  
Manuel G. Forero ◽  
José M. Menéndez ◽  
Angharad Jones ◽  
Walter Dewitte ◽  
...  
Keyword(s):  
Level Set ◽  
Automatic Segmentation ◽  
Optimization Method ◽  
Intensity Level ◽  
Low Contrast ◽  
Segmentation Methods ◽  
Segmented Images ◽  
Optimization Function ◽  
Confocal Images ◽  
Automated Optimization

Meristem cells are irregularly shaped and appear in confocal images as dark areas surrounded by bright ones. Images are characterized by regions of very low contrast and absolute loss of edges deeper into the meristem. Edges are blurred, discontinuous, sometimes indistinguishable, and the intensity level inside the cells is similar to the background of the image. Recently, a technique called Parametric Segmentation Tuning was introduced for the optimization of segmentation parameters in diatom images. This paper presents a PST-tuned automatic segmentation method of meristem cells in microscopy images based on mathematical morphology. The optimal parameters of the algorithm are found by means of an iterative process that compares the segmented images obtained by successive variations of the parameters. Then, an optimization function is used to determine which pair of successive images allows for the best segmentation. The technique was validated by comparing its results with those obtained by a level set algorithm and a balloon segmentation technique. The outcomes show that our methodology offers better results than two free available state-of-the-art alternatives, being superior in all cases studied, losing 9.09% of the cells in the worst situation, against 75.81 and 25.45 obtained in the level set and the balloon segmentation techniques, respectively. The optimization method can be employed to tune the parameters of other meristem segmentation methods.

scholarly journals Automatic Localization Algorithm for Ultrasound Breast Tumors Based on Human Visual Mechanism

2017 ◽  
Author(s):  
Yuting Xie ◽  
Ke Chen ◽  
Jiangli Lin
Keyword(s):  
Level Set ◽  
Automatic Segmentation ◽  
Breast Tumors ◽  
Input Image ◽  
Object Localization ◽  
Initial Contour ◽  
Natural Image ◽  
Localization Algorithm ◽  
Localization Method ◽  
Automatic Localization

Human visual system (HVM) can quickly localize the most salient object in scenes, which has been widely applied on natural image segmentation [15]-[19]. In ultrasound (US) breast images, compared with background areas, tumor is more salient because of its higher contrast. In this paper, we develop a novel automatic localization method based on HVM for automatic segmentation of ultrasound (US) breast tumors. First, the input image is smoothed by convolution with a linearly separable Gaussian filter and then subsampled into a 9-layer Gaussian pyramid. Then intensity, blackness ratio, and superpixel contrast features are combined to compute saliency map, in which Winner Take All algorithm is used to localize the most salient region, presenting with a circle on the localized target. Finally the circle is taken as the initial contour of CV level set to finish the extraction of breast tumor. The localization method has been tested on 400 US beast images, among which 378 images have higher saliency than background areas and succeed in localization, with high accuracy 92.00%. The HVM localization method can be used to localize the tumors, combined with this method, CV level set can achieve the fully automatic segmentation of US breast tumors. By combing intensity, blackness ratio and superpixel contrast features, the proposed localization method can successfully avoid the interference caused by background areas with low echo and high intensity. Moreover, multi-object localization of US breast images can be considered in future employment.

scholarly journals AN AUTOMATIC SEGMENTATION OF LIVER COLUME THAT CONTAINED DISCONNECTED REGION WITH DYNAMIC LOCAL NEIGHBOURHOOD REGION SIZES

2016 ◽  
Vol 3 (1) ◽  
pp. 129-148
Author(s):  
Puteri Suhaiza Sulaiman ◽  
Rahmita Wirza Rahmat ◽  
Ramlan Mahmod ◽  
Abdul Hamid Abdul Rashid
Keyword(s):  
Level Set ◽  
Automatic Segmentation ◽  
Dice Similarity Coefficient ◽  
Dynamic Localization ◽  
Hybrid Energy ◽  
Segmentation Accuracy ◽  
Average Factor ◽  
Liver Region ◽  
Level Set Framework ◽  
Localization Region

Segmentation of liver images containing disconnected regions has always been an overlooked problem. Previous works on liver segmentation either ignore this problem or use manual initialization when facing these disconnected regions. Therefore, in this paper we propose a liver level set (LLS) algorithm which is able to segment disconnected regions automatically. The LLS algorithm is based on level set framework together with hybrid energy minimization as the stopping function. By using the LLS algorithm in a looping manner, we allow the current liver boundary to inherit the topological changes from previous images in a 2.5D environment. We also conduct an experiment to obtain an average factor for dynamic localization region sizes based on liver anatomy to improve the segmentation accuracy. These dynamic localization region sizes ensure a more accurate segmentation when compared with manual segmentation. Our experiment gives a respective segmentation result with dice similarity coefficient (DSC) percentage of 87.5%. Plus, our LLS algorithm is able to segment all connected and disconnected liver region automatically and accurately.

A Genetic Algorithm-Based Level Set Curve Evolution for Prostate Segmentation on Pelvic CT and MRI Images

2010 ◽  
pp. 127-149 ◽  
Author(s):  
Payel Ghosh ◽  
Melanie Mitchell ◽  
James A. Tanyi ◽  
Arthur Hung
Keyword(s):  
Genetic Algorithm ◽  
Level Set ◽  
Energy Function ◽  
Automatic Segmentation ◽  
Prostate Gland ◽  
Descent Method ◽  
Curve Evolution ◽  
Gradient Descent Method ◽  
Level Set Function ◽  
Set Function

A novel genetic algorithm (GA) is presented here that performs level set curve evolution using texture and shape information to automatically segment the prostate on pelvic images in computed tomography and magnetic resonance imaging modalities. Here, the segmenting contour is represented as a level set function. The contours in a typical level set evolution are deformed by minimizing an energy function using the gradient descent method. In these methods, the computational complexity of computing derivatives increases as the number of terms (needed for curve evolution) in the energy function increase. In contrast, a genetic algorithm optimizes the level-set function without the need to compute derivatives, thereby making the introduction of new curve evolution terms straightforward. The GA developed here uses the texture of the prostate gland and its shape derived from manual segmentations to perform curve evolution. Using these high-level features makes automatic segmentation possible.

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