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>

A Survey on Segmentation Techniques for Image Processing

2021 ◽  
Vol 17 (2) ◽  
pp. 73-93
Author(s):  
Wala’a Jasim ◽  
Rana Mohammed
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Image Segmentation ◽  
Region Growing ◽  
Support Vector ◽  
Pixel Intensity ◽  
Iterative Thresholding ◽  
Hybrid Techniques ◽  
Seeded Region Growing ◽  
Fuzzy C Means Clustering

The segmentation methods for image processing are studied in the presented work. Image segmentation can be defined as a vital step in digital image processing. Also, it is used in various applications including object co-segmentation, recognition tasks, medical imaging, content based image retrieval, object detection, machine vision and video surveillance. A lot of approaches were created for image segmentation. In addition, the main goal of segmentation is to facilitate and alter the image representation into something which is more important and simply to be analyzed. The approaches of image segmentation are splitting the images into a few parts on the basis of image’s features including texture, color, pixel intensity value and so on. With regard to the presented study, many approaches of image segmentation are reviewed and discussed. The techniques of segmentation might be categorized into six classes: First, thresholding segmentation techniques such as global thresholding (iterative thresholding, minimum error thresholding, otsu's, optimal thresholding, histogram concave analysis and entropy based thresholding), local thresholding (Sauvola’s approach, T.R Singh’s approach, Niblack’s approaches, Bernsen’s approach Bruckstein’s and Yanowitz method and Local Adaptive Automatic Binarization) and dynamic thresholding. Second, edge-based segmentation techniques such as gray-histogram technique, gradient based approach (laplacian of gaussian, differential coefficient approach, canny approach, prewitt approach, Roberts approach and sobel approach). Thirdly, region based segmentation approaches including Region growing techniques (seeded region growing (SRG), statistical region growing, unseeded region growing (UsRG)), also merging and region splitting approaches. Fourthly, clustering approaches, including soft clustering (fuzzy C-means clustering (FCM)) and hard clustering (K-means clustering). Fifth, deep neural network techniques such as convolution neural network, recurrent neural networks (RNNs), encoder-decoder and Auto encoder models and support vector machine. Finally, hybrid techniques such as evolutionary approaches, fuzzy logic and swarm intelligent (PSO and ABC techniques) and discusses the pros and cons of each method.

Automatic Seeded Region Growing Image Segmentation for Medical Image Segmentation: A Brief Review

2020 ◽  
Vol 20 (03) ◽  
pp. 2050018
Author(s):  
Neeraj Shrivastava ◽  
Jyoti Bharti
Keyword(s):  
Image Segmentation ◽  
Region Growing ◽  
Magnetic Resonance Images ◽  
Medical Image Segmentation ◽  
Seed Selection ◽  
Seeded Region Growing ◽  
Processing Strategies ◽  
Edge Based ◽  
Seed Points ◽  
Selection Of

In the domain of computer technology, image processing strategies have become a part of various applications. A few broadly used image segmentation methods have been characterized as seeded region growing (SRG), edge-based image segmentation, fuzzy [Formula: see text]-means image segmentation, etc. SRG is a quick, strongly formed and impressive image segmentation algorithm. In this paper, we delve into different applications of SRG and their analysis. SRG delivers better results in analysis of magnetic resonance images, brain image, breast images, etc. On the other hand, it has some limitations as well. For example, the seed points have to be selected manually and this manual selection of seed points at the time of segmentation brings about wrong selection of regions. So, a review of some automatic seed selection methods with their advantages, disadvantages and applications in different fields has been presented.

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 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.

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