Optimized Automatic Seeded Region Growing Algorithm with Application to ROI Extraction

2017 ◽  
Vol 17 (04) ◽  
pp. 1750024 ◽  
Author(s):  
Qianwen Li ◽  
Zhihua Wei ◽  
Cairong Zhao
Keyword(s):  
Image Analysis ◽  
Computational Complexity ◽  
Clustering Algorithm ◽  
Region Of Interest ◽  
Region Growing ◽  
Regions Of Interest ◽  
Automatic Extraction ◽  
Seeded Region Growing ◽  
Roi Extraction ◽  
Ap Clustering

Region of interest (ROI) is the most important part of an image that expresses the effective content of the image. Extracting regions of interest from images accurately and efficiently can reduce computational complexity and is essential for image analysis and understanding. In order to achieve the automatic extraction of regions of interest and obtain more accurate regions of interest, this paper proposes Optimized Automatic Seeded Region Growing (OASRG) algorithm. The algorithm uses the affinity propagation (AP) clustering algorithm to extract the seeds automatically, and optimizes the traditional region growing algorithm by regrowing strategy to obtain the regions of interest where target objects are contained. Experimental results show that our algorithm can automatically locate seeds and produce results as good as traditional region growing with seeds selected manually. Furthermore, the precision is improved and the extraction effect is better after the optimization with regrowing strategy.

scholarly journals Model-based Automatic Segmentation of Ascending Aorta from Multimodality Medical Data

2016 ◽  
Vol 6 (6) ◽  
pp. 3161
Author(s):  
Noha Seada ◽  
Safwat Hamad ◽  
Mostafa G. M. Mostafa
Keyword(s):  
Automatic Segmentation ◽  
Model Fitting ◽  
Region Of Interest ◽  
Region Growing ◽  
Magnetic Resonance Images ◽  
Ascending Aorta ◽  
Imaging Modalities ◽  
Dice Similarity Coefficient ◽  
Seeded Region Growing ◽  
Novel Approach

<p>Automatic Ascending Aorta Segmentation is one of the important steps towards automatic segmentation of the whole cardiac tree. This paper presents a novel approach for the automatic segmentation of the ascending aorta from two imaging modalities: CTA (Computed Tomography Angiography) and PC-MRI (Phase-Contrast Magnetic Resonance Images). The novel approach is an algorithm that works without the need for setting manual seed points or applying preprocessing steps or setting a region of interest. Instead, the proposed algorithm automatically detects and segments the ascending aorta using an ascending aorta model built from its anatomical features. The proposed segmentation algorithm begins with aorta detection through features model fitting augmented with Hough transform, where the ascending aorta is identified from the descending aorta and any other circular structures based on the proposed model. After detection, the whole ascending aorta is segmented up from the aortic arch down to the ostia points using a novel automatic seeded region growing algorithm. The proposed algorithm is fully automatic, works in real-time and robust as parameters used are the same for all the tested datasets. The detection and segmentation of the ascending aorta succeeded in all test cases acquired from the two imaging modalities; proving the robustness of the proposed ascending aorta model and algorithm for the automatic segmentation process even on data from different modalities and different scanner types. The accuracy of the segmentation has a mean Dice Similarity Coefficient (DSC) of 94.72% for CTA datasets and 97.13% for PC-MRI datasets.</p>

Model-based Automatic Segmentation of Ascending Aorta from Multimodality Medical Data

2016 ◽  
Vol 6 (6) ◽  
pp. 3161
Author(s):  
Noha Seada ◽  
Safwat Hamad ◽  
Mostafa G. M. Mostafa
Keyword(s):  
Automatic Segmentation ◽  
Model Fitting ◽  
Region Of Interest ◽  
Region Growing ◽  
Magnetic Resonance Images ◽  
Ascending Aorta ◽  
Imaging Modalities ◽  
Dice Similarity Coefficient ◽  
Seeded Region Growing ◽  
Novel Approach

<p>Automatic Ascending Aorta Segmentation is one of the important steps towards automatic segmentation of the whole cardiac tree. This paper presents a novel approach for the automatic segmentation of the ascending aorta from two imaging modalities: CTA (Computed Tomography Angiography) and PC-MRI (Phase-Contrast Magnetic Resonance Images). The novel approach is an algorithm that works without the need for setting manual seed points or applying preprocessing steps or setting a region of interest. Instead, the proposed algorithm automatically detects and segments the ascending aorta using an ascending aorta model built from its anatomical features. The proposed segmentation algorithm begins with aorta detection through features model fitting augmented with Hough transform, where the ascending aorta is identified from the descending aorta and any other circular structures based on the proposed model. After detection, the whole ascending aorta is segmented up from the aortic arch down to the ostia points using a novel automatic seeded region growing algorithm. The proposed algorithm is fully automatic, works in real-time and robust as parameters used are the same for all the tested datasets. The detection and segmentation of the ascending aorta succeeded in all test cases acquired from the two imaging modalities; proving the robustness of the proposed ascending aorta model and algorithm for the automatic segmentation process even on data from different modalities and different scanner types. The accuracy of the segmentation has a mean Dice Similarity Coefficient (DSC) of 94.72% for CTA datasets and 97.13% for PC-MRI datasets.</p>

scholarly journals Protocol for Quantification of Defects in Natural Fibres for Composites

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Ulrich Andreas Mortensen ◽  
Bo Madsen
Keyword(s):  
Image Analysis ◽  
Region Growing ◽  
Defect Size ◽  
Natural Fibres ◽  
Variation Method ◽  
Flax Fibres ◽  
Seeded Region Growing ◽  
Otsu’S Method ◽  
Definition Of ◽  
Significant Figures

Natural bast-type plant fibres are attracting increasing interest for being used for structural composite applications where high quality fibres with good mechanical properties are required. A protocol for the quantification of defects in natural fibres is presented. The protocol is based on the experimental method of optical microscopy and the image analysis algorithms of the seeded region growing method and Otsu’s method. The use of the protocol is demonstrated by examining two types of differently processed flax fibres to give mean defect contents of 6.9 and 3.9%, a difference which is tested to be statistically significant. The protocol is evaluated with respect to the selection of image analysis algorithms, and Otsu’s method is found to be a more appropriate method than the alternative coefficient of variation method. The traditional way of defining defect size by area is compared to the definition of defect size by width, and it is shown that both definitions can be used to give unbiased findings for the comparison between fibre types. Finally, considerations are given with respect to true measures of defect content, number of determinations, and number of significant figures used for the descriptive statistics.

scholarly journals Segmentation of Thermogram Based on Region Based Technique using Split and Merge Method

2019 ◽  
Vol 8 (4) ◽  
pp. 9574-9578
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Object Representation ◽  
Region Of Interest ◽  
Region Growing ◽  
Destructive Testing ◽  
Segmentation Methods ◽  
Split And Merge ◽  
Thermographic Image ◽  
Non Destructive

The main aim of segmentation is to identify the Region of Interest for image analysis. The segregation of an image into meaningful structures is often an important phase in image analysis, object representation, visualization and also in various other image processing tasks. Image segmentation is mostly useful in applications like detection where it is difficult to process whole image at a time. In this paper Region based image segmentation is used to identify the delaminations in Thermographic image of Infrared Non-Destructive Testing. There are two basic techniques in Region based segmentation viz. Region growing method, splitting and merging method. New method based Split and Merge segmentation technique is employed to identify the defective regions in thermogram. Results obtained after segmentation as compared with state of art segmentation methods

An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding

2016 ◽  
Vol 6 (6) ◽  
pp. 2773 ◽  
Author(s):  
Sirshendu Hore ◽  
Souvik Chakraborty ◽  
Sankhadeep Chatterjee ◽  
Nilanjan Dey ◽  
Amira S. Ashour ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Region Of Interest ◽  
Region Growing ◽  
Threshold Value ◽  
Pixel Intensity ◽  
Integrated Method ◽  
Seeded Region Growing ◽  
Segmented Images ◽  
Otsu’S Method ◽  
Region Growing Segmentation

<p>Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score. Although, it had comparable recall values with that gained by the human segmented images. It was noted that as the image under test had a dark background with the brighter object, thus the proposed method provided the superior recall value compared to the other methods.</p>

scholarly journals An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding

2016 ◽  
Vol 6 (6) ◽  
pp. 2773 ◽  
Author(s):  
Sirshendu Hore ◽  
Souvik Chakraborty ◽  
Sankhadeep Chatterjee ◽  
Nilanjan Dey ◽  
Amira S. Ashour ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Region Of Interest ◽  
Region Growing ◽  
Threshold Value ◽  
Pixel Intensity ◽  
Integrated Method ◽  
Seeded Region Growing ◽  
Segmented Images ◽  
Otsu’S Method ◽  
Region Growing Segmentation

<p>Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score. Although, it had comparable recall values with that gained by the human segmented images. It was noted that as the image under test had a dark background with the brighter object, thus the proposed method provided the superior recall value compared to the other methods.</p>

An improved seeded region growing algorithm

1997 ◽  
Vol 18 (10) ◽  
pp. 1065-1071 ◽  
Author(s):  
Andrew Mehnert ◽  
Paul Jackway
Keyword(s):  
Region Growing ◽  
Seeded Region Growing

Segmentation of corpus callosum based on tensor fuzzy clustering algorithm

2021 ◽  
pp. 1-14
Author(s):  
Yujia Qu ◽  
Yuanjun Wang
Keyword(s):  
Corpus Callosum ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Diffusion Tensor ◽  
Region Of Interest ◽  
Midsagittal Plane ◽  
Fuzzy Clustering Algorithm ◽  
Human Connectome Project ◽  
Clustering Center ◽  
Fixed Region

BACKGROUND: The corpus callosum in the midsagittal plane plays a crucial role in the early diagnosis of diseases. When the anisotropy of the diffusion tensor in the midsagittal plane is calculated, the anisotropy of corpus callosum is close to that of the fornix, which leads to blurred boundary of the segmentation region. OBJECTIVE: To apply a fuzzy clustering algorithm combined with new spatial information to achieve accurate segmentation of the corpus callosum in the midsagittal plane in diffusion tensor images. METHODS: In this algorithm, a fixed region of interest is selected from the midsagittal plane, and the anisotropic filtering algorithm based on tensor is implemented by replacing the gradient direction of the structural tensor with an eigenvector, thus filtering the diffusion tensor of region of interest. Then, the iterative clustering center based on K-means clustering is used as the initial clustering center of tensor fuzzy clustering algorithm. Taking filtered diffusion tensor as input data and different metrics as similarity measures, the neighborhood diffusion tensor pixel calculation method of Log Euclidean framework is introduced in the membership function calculation, and tensor fuzzy clustering algorithm is proposed. In this study, MGH35 data from the Human Connectome Project (HCP) are tested and the variance, accuracy and specificity of the experimental results are discussed. RESULTS: Segmentation results of three groups of subjects in MGH35 data are reported. The average segmentation accuracy is 97.34%, and the average specificity is 98.43%. CONCLUSIONS: When segmenting the corpus callosum of diffusion tensor imaging, our method cannot only effective denoise images, but also achieve high accuracy and specificity.

scholarly journals Automatic Detection of Pulmonary Nodules using Three-dimensional Chain Coding and Optimized Random Forest

2020 ◽  
Vol 10 (7) ◽  
pp. 2346 ◽  
Author(s):  
May Phu Paing ◽  
Kazuhiko Hamamoto ◽  
Supan Tungjitkusolmun ◽  
Sarinporn Visitsattapongse ◽  
Chuchart Pintavirooj
Keyword(s):  
Random Forest ◽  
Three Dimensional ◽  
Pulmonary Nodules ◽  
Region Growing ◽  
Favorable Result ◽  
Dimensional Chain ◽  
Chain Code ◽  
Seeded Region Growing ◽  
Chain Coding ◽  
Diagnosis Of Lung Cancer

The detection of pulmonary nodules on computed tomography scans provides a clue for the early diagnosis of lung cancer. Manual detection mandates a heavy radiological workload as it identifies nodules slice-by-slice. This paper presents a fully automated nodule detection with three significant contributions. First, an automated seeded region growing is designed to segment the lung regions from the tomography scans. Second, a three-dimensional chain code algorithm is implemented to refine the border of the segmented lungs. Lastly, nodules inside the lungs are detected using an optimized random forest classifier. The experiments for our proposed detection are conducted using 888 scans from a public dataset, and achieves a favorable result of 93.11% accuracy, 94.86% sensitivity, and 91.37% specificity, with only 0.0863 false positives per exam.

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