scholarly journals A Region Growing Vessel Segmentation Algorithm Based on Spectrum Information

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Huiyan Jiang ◽  
Baochun He ◽  
Di Fang ◽  
Zhiyuan Ma ◽  
Benqiang Yang ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Region Growing ◽  
Vessel Segmentation ◽  
Growth Strategy ◽  
Segmentation Algorithm ◽  
Edge Information ◽  
Manual Intervention ◽  
Vascular Structures ◽  
Region Growing Segmentation ◽  
Seed Points

We propose a region growing vessel segmentation algorithm based on spectrum information. First, the algorithm does Fourier transform on the region of interest containing vascular structures to obtain its spectrum information, according to which its primary feature direction will be extracted. Then combined edge information with primary feature direction computes the vascular structure’s center points as the seed points of region growing segmentation. At last, the improved region growing method with branch-based growth strategy is used to segment the vessels. To prove the effectiveness of our algorithm, we use the retinal and abdomen liver vascular CT images to do experiments. The results show that the proposed vessel segmentation algorithm can not only extract the high quality target vessel region, but also can effectively reduce the manual intervention.

Point Cloud Based Bin Picking: Object Recognition and Pose Estimation Using Region Growing Segmentation Algorithm

2015 ◽  
Vol 791 ◽  
pp. 189-194
Author(s):  
Frantisek Durovsky
Keyword(s):  
Object Recognition ◽  
Pose Estimation ◽  
Point Cloud ◽  
Region Growing ◽  
Segmentation Algorithm ◽  
Suction Cup ◽  
Bin Picking ◽  
Region Growing Segmentation ◽  
Dual Arm ◽  
Sensor Region

Presented paper describes experimental bin picking using Kinect sensor, region-growing algorithm, latest ROS-Industrial drivers and dual arm manipulator Motoman SDA10f.As well known if manipulation with objects of regular shapes by suction cup is required, it is sufficient to estimate only 5DoF for successful pick. In such a case simpler region growing algorithm may be used instead of complicated 3D object recognition and pose estimation techniques, resulting in shorter processing time and decrease of computational load. Experimental setup for such a scenario, manipulated objects and results using region growing segmentation algorithm are explained in detail. Video and link to open-source code of described application are provided as well.

scholarly journals Selective Feature Fusion Based Adaptive Image Segmentation Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Qianwen Li ◽  
Zhihua Wei ◽  
Wen Shen
Keyword(s):  
Image Segmentation ◽  
Feature Fusion ◽  
Region Growing ◽  
Adaptive Threshold ◽  
Segmentation Algorithm ◽  
Image Feature ◽  
Fusion Algorithm ◽  
Region Growing Segmentation ◽  
Image Segmentation Algorithm ◽  
Change Threshold

Image segmentation is an essential task in computer vision and pattern recognition. There are two key challenges for image segmentation. One is to find the most discriminative image feature set to get high-quality segments. The other is to achieve good performance among various images. In this paper, we firstly propose a selective feature fusion algorithm to choose the best feature set by evaluating the results of presegmentation. Specifically, the proposed method fuses selected features and applies the fused features to region growing segmentation algorithm. To get better segments on different images, we further develop an algorithm to change threshold adaptively for each image by measuring the size of the region. The adaptive threshold can achieve better performance on each image than fixed threshold. Experimental results demonstrate that our method improves the performance of traditional region growing by selective feature fusion and adaptive threshold. Moreover, our proposed algorithm obtains promising results and outperforms some popular approaches.

A Region-Growing Segmentation Algorithm for GPUs

2013 ◽  
Vol 10 (6) ◽  
pp. 1612-1616 ◽  
Author(s):  
Patrick Nigri Happ ◽  
Raul Queiroz Feitosa ◽  
Cristiana Bentes ◽  
Ricardo Farias
Keyword(s):  
Region Growing ◽  
Segmentation Algorithm ◽  
Region Growing Segmentation

scholarly journals Automatic Pectoral Muscle Removal and Microcalcification Localization in Digital Mammograms

2021 ◽  
Vol 27 (3) ◽  
pp. 222-230
Author(s):  
Kevin Alejandro Hernández Gómez ◽  
Julian D. Echeverry-Correa ◽  
Álvaro Ángel Orozco Gutiérrez
Keyword(s):  
Region Of Interest ◽  
Region Growing ◽  
Pectoral Muscle ◽  
Test Accuracy ◽  
Morphological Operations ◽  
Sensitivity Score ◽  
Preprocessing Method ◽  
Breast Segmentation ◽  
Contour Fitting ◽  
Region Growing Segmentation

Objectives: Breast cancer is the most common cancer diagnosed in women, and microcalcification (MCC) clusters act as an early indicator. Thus, the detection of MCCs plays an important role in diagnosing breast cancer.Methods: This paper presents a methodology for mammogram preprocessing and MCC detection. The preprocessing method employs automatic artefact deletion and pectoral muscle removal based on region-growing segmentation and polynomial contour fitting. The MCC detection method uses a convolutional neural network for region-of-interest (ROI) classification, along with morphological operations and wavelet reconstruction to reduce false positives (FPs).Results: The methodology was evaluated using the mini-MIAS and UTP datasets in terms of segmentation accuracy in the preprocessing phase, as well as sensitivity and the mean FP rate per image in the MCC detection phase. With the mini-MIAS dataset, the proposed methods achieved accuracy scores of 99% for breast segmentation and 95% for pectoral segmentation, a sensitivity score of 82% for MCC detection, and an FP rate per image of 3.27. With the UTP dataset, the methods achieved accuracy scores of 97% for breast segmentation and 91% for pectoral segmentation, a sensitivity score of 78% for MCC detection, and an FP rate per image of 0.74.Conclusions: The proposed preprocessing method outperformed the state-of-the-art methods for breast segmentation and achieved relatively good results for pectoral muscle removal. Furthermore, the MCC detection module achieved the highest test accuracy in identifying potential ROIs with MCCs compared to other 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>

scholarly journals Nearly automatic vessels segmentation using graph-based energy minimization

2009 ◽  
Author(s):  
Moti Freiman ◽  
Judith Frank ◽  
Lior Weizman ◽  
Einav Nammer ◽  
Ofek Shilon ◽  
...  
Keyword(s):  
Shortest Path ◽  
Energy Minimization ◽  
Region Of Interest ◽  
Ground Truth ◽  
Minimization Method ◽  
Anatomical Structures ◽  
Vascular Structures ◽  
Automatic Tool ◽  
Symmetric Surface ◽  
Seed Points

We present a nearly automatic tool for the accurate segmentation of vascular structures in volumetric CTA images. Its inputs are a start and an end seed points inside the vessel. The two-step graph-based energy minimization method starts by computing the weighted shortest path between the vessel seed endpoints based on local image and seed intensities and vessel path geometric characteristics. It then automatically defines a Vessel Region Of Interest (VROI) from the shortest path and the estimated vessel radius, and extracts the vessels boundaries by minimize the energy on a corresponding graph cut.We evaluate our method within the 2009 MICCAI 3D Segmentation Challenge for Clinical Applications Workshop. Experimental results on the 46 carotid bifurcations from clinical CTAs, compared to ground-truth genrated by averaging three manual annotations, yield an average symmetric surface distance of 0.83mm and a Dice similarity of 81.8%, with only three input seeds. These results indicates that our method is easy to use, produces accurate segmentations of vessels lumen, and is robust to intensity variations inside the vessels, radius changes, bifurcations, and nearby anatomical structures with similar intensity values.

Sign in / Sign up

Export Citation Format

Share Document