Automated Mammogram Segmentation Using Seed Point Identification and Modified Region Growing Algorithm

2015 ◽  
Vol 6 (4) ◽  
pp. 378-385 ◽  
Author(s):  
K. Rajkumar ◽  
G. Raju
Keyword(s):  
Region Growing ◽  
Seed Point

scholarly journals A smoke image segmentation algorithm based on rough set and region growing

2019 ◽  
Vol 65 (No. 8) ◽  
pp. 321-329
Author(s):  
Haitao Wang ◽  
Yanli Chen
Keyword(s):  
Image Segmentation ◽  
Rough Set ◽  
Region Growing ◽  
Recall Rate ◽  
Segmentation Algorithm ◽  
Colour Space ◽  
Seed Point ◽  
Black Smoke ◽  
Growth Method ◽  
Image Segmentation Algorithm

Because the image fire smoke segmentation algorithm can not extract white, gray and black smoke at the same time, a smoke image segmentation algorithm is proposed by combining rough set and region growth method. The R component of the image is extracted in the RGB colour space, the roughness histogram is constructed according to the statistical histogram of the R component, and the appropriate valley value in the roughness histogram is selected as the segmentation threshold, the image is roughly segmented. Relative to the background image, the smoke belongs to the motion information, and the motion region is extracted by the interframe difference method to eliminate static interference. Smoke has a unique colour feature, a smoke colour model is created in the RGB colour space, the motion disturbances of similar colour are removed and the suspected smoke areas are obtained. The seed point is selected in the region, and the region is grown on the result of rough segmentation, the smoke region is extracted. The experimental results show that the algorithm can segment white, gray and black smoke at the same time, and the irregular information of smoke edges is relatively complete. Compared with the existing algorithms, the average segmentation accuracy, recall rate and F-value are increased by 19%, 21.5% and 20%, respectively.<br /><br />

Automatic Segmentation and Diagnosis Based on Multi-Scale Two-Stage Region Growing and Skeleton Extraction for Vessel Stenosis in Coronary Angiography

2020 ◽  
Vol 10 (2) ◽  
pp. 446-451
Author(s):  
Wu Deng ◽  
Kai Luo ◽  
Qinke Shi ◽  
Yi Yang ◽  
Ning Ning
Keyword(s):  
Coronary Angiography ◽  
Hessian Matrix ◽  
Region Growing ◽  
Vessel Segmentation ◽  
Good Effect ◽  
Skeleton Extraction ◽  
Seed Point ◽  
Automatic Diagnosis ◽  
Small Vessels ◽  
Vessel Stenosis

Although great progress has been made in vessel segmentation, the existing methods still can not accurately segment small vessels. A novel vessel segmentation and automatic diagnosis in coronary angiography image was proposed. During vessel segmentation, a new vessel function based on Hessian matrix was put forward. Then the vessel contour was extracted by the dual-stage region growing with automatic selection of seed point. Next, the automatic diagnosis was realized by vessel skeleton extraction, skeleton point search and diameter measurement. The experimental results demonstrate that our proposed vessel segmentation can extract the main branch contour accurately and have a good effect on the enhancement and segmentation of small vessels. The automatic diagnosis of vessel stenosis is fast. With a relatively accurate diagnosis result, it can provide a good reference and quantitative basis for the final judgment of the doctor.

scholarly journals Reliability of Segmenting Brain Tumor and Finding Optimal Volume Estimator for MR Images of Patients with Glioma’s

2019 ◽  
Vol 8 (9) ◽  
pp. 1647-1652
Keyword(s):  
Brain Tumor ◽  
Tumor Volume ◽  
Region Growing ◽  
Threshold Value ◽  
Volume Estimation ◽  
Clinical Aspects ◽  
Tumor Segmentation ◽  
Low Grade ◽  
Seed Point ◽  
Glioma Tumor

Tumor volume estimation is a significant prognostic part of the Glioma tumor detection. Reliable assessment of Glioma tumor segmentation and volume estimation is a common problem in clinical aspects. We aim to propose a tumor segmentation method by suggesting suitable estimator for MR brain tumor volume construction. Run length algorithm is used to automatic initialize the seed point to the region growing algorithm. Region growing algorithm works with a threshold value using 8 × 8 patches. In this experiment includes thirty BraTS2013 high-grade and low-grade Glioma datasets. Proposed method yield 80.12% of Dice similarity with 6.8% of deviation and 84% of accuracy with 10% of deviation. The proposed work uses six state-of-the-art volume detectors to estimate the size of tumor volume. From the results, Cavalieri’s estimator gives more accurate results with less deviation

Building Segmentation of Satellite Image based on Area and Perimeter using Region Growing

2016 ◽  
Vol 3 (3) ◽  
pp. 579 ◽  
Author(s):  
Ervin Yohannes ◽  
Fitri Utaminingrum
Keyword(s):  
Satellite Image ◽  
Region Growing ◽  
Important Variable ◽  
Segmentation Method ◽  
Seed Point ◽  
Otsu Method ◽  
Area And Perimeter

<em><span>A </span><span lang="IN">building can be known by look shape, color, and texture. Building can be detected by using many method. Region growing is one simple segmentation method because only use seed point. Before segmentation, the image must be preprocessing include sharpening, binerization by otsu method. Sharpening for clarify image and otsu method changed image valued 0 and 1. Next step is post-preprocessing include segmentation using region growing and opening closing operation. And the last process is detection building where building of detection will be signed. In this research, we present region growing for building segmentation by using both area and perimeter as a important variable in the region growing. Value of area more than 10 and perimeter is more than 50 are produced most of building.</span></em>

Automatic segmentation and automatic seed point selection of nasopharyngeal carcinoma from microscopy images using region growing based approach

2017 ◽  
Vol 20 ◽  
pp. 61-69 ◽  
Author(s):  
Mazin Abed Mohammed ◽  
Mohd Khanapi Abd Ghani ◽  
Raed Ibraheem Hamed ◽  
Mohamad Khir Abdullah ◽  
Dheyaa Ahmed Ibrahim
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Automatic Segmentation ◽  
Region Growing ◽  
Point Selection ◽  
Seed Point ◽  
Microscopy Images ◽  
Selection Of

Microscopic Image Segmentation of Chinese Herbal Medicine Based on Region Growing Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 4110-4115 ◽  
Author(s):  
Qing Liu ◽  
Li Jun Zhang ◽  
Xi Ping Liu
Keyword(s):  
Image Segmentation ◽  
Herbal Medicine ◽  
Chinese Herbal Medicine ◽  
Cell Structure ◽  
Region Growing ◽  
Microscopic Image ◽  
Seed Selection ◽  
Seed Point ◽  
Microscopic Images ◽  
Chinese Herbal

In order to effectively separate the target region of the microscopic image of Chinese Herbal Medicine (CHM), and lay the foundation for the subsequent image recognition processing, a microscopic image segmentation method of CHM by using region growing (RG) algorithm is put forward based on the characteristics of the plant microscopic images. Firstly, the CHM microscopic images with different cell structure are regarded as a multi-dimensional matrix to process and established seed label matrix. Secondly, in a given region threshold conditions, the different seed growth points are selected to segmented the different images. Finally, given a fixed growth points, the microscopic images are processed by choosing a different threshold. The experimental results show that CHM image segmentation threshold and seed selection decide the image target extraction. For different CHM images, according to a certain method, the better image segmentation results can be achieved in the case to obtain a suitable threshold value using image information and the seed point adjustment.

scholarly journals Enhanced Region Growing for Brain Tumor MR Image Segmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 22
Author(s):  
Erena Siyoum Biratu ◽  
Friedhelm Schwenker ◽  
Taye Girma Debelee ◽  
Samuel Rahimeto Kebede ◽  
Worku Gachena Negera ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Region Growing ◽  
Developed Countries ◽  
Brain Images ◽  
Seed Point ◽  
Brain Image ◽  
The Brain

A brain tumor is one of the foremost reasons for the rise in mortality among children and adults. A brain tumor is a mass of tissue that propagates out of control of the normal forces that regulate growth inside the brain. A brain tumor appears when one type of cell changes from its normal characteristics and grows and multiplies abnormally. The unusual growth of cells within the brain or inside the skull, which can be cancerous or non-cancerous has been the reason for the death of adults in developed countries and children in under developing countries like Ethiopia. The studies have shown that the region growing algorithm initializes the seed point either manually or semi-manually which as a result affects the segmentation result. However, in this paper, we proposed an enhanced region-growing algorithm for the automatic seed point initialization. The proposed approach’s performance was compared with the state-of-the-art deep learning algorithms using the common dataset, BRATS2015. In the proposed approach, we applied a thresholding technique to strip the skull from each input brain image. After the skull is stripped the brain image is divided into 8 blocks. Then, for each block, we computed the mean intensities and from which the five blocks with maximum mean intensities were selected out of the eight blocks. Next, the five maximum mean intensities were used as a seed point for the region growing algorithm separately and obtained five different regions of interest (ROIs) for each skull stripped input brain image. The five ROIs generated using the proposed approach were evaluated using dice similarity score (DSS), intersection over union (IoU), and accuracy (Acc) against the ground truth (GT), and the best region of interest is selected as a final ROI. Finally, the final ROI was compared with different state-of-the-art deep learning algorithms and region-based segmentation algorithms in terms of DSS. Our proposed approach was validated in three different experimental setups. In the first experimental setup where 15 randomly selected brain images were used for testing and achieved a DSS value of 0.89. In the second and third experimental setups, the proposed approach scored a DSS value of 0.90 and 0.80 for 12 randomly selected and 800 brain images respectively. The average DSS value for the three experimental setups was 0.86.

scholarly journals An effective approach for CT lung segmentation using region growing

2021 ◽  
Vol 2082 (1) ◽  
pp. 012001
Author(s):  
Xi Yang ◽  
Guanyu Xu ◽  
Teng Zhou
Keyword(s):  
Medical Image ◽  
Lung Diseases ◽  
Region Growing ◽  
Computer Aided Diagnosis ◽  
Medical Image Segmentation ◽  
Point Selection ◽  
Seed Point ◽  
Lung Segmentation ◽  
Computer Aided ◽  
Aided Diagnosis

Abstract X-ray is an important means of detecting lung diseases. With the increasing incidence of lung diseases, computer-aided diagnosis technology is of great significance in clinical treatment. It has become a hot research direction to use computer-aided diagnosis to recognize chest radiography images, which can alleviate the uneven status of regional medical level. For clinical diagnosis, medical image segmentation can enable users to timely obtain the target region they are interested in and analyze it, which is significant to be used as an important basis for auxiliary research and judgment. In this case, a region growing algorithm based on threshold presegmentation is selected for lung segmentation, which integrates image enhancement, threshold segmentation, seed point selection and morphological post-processing, etc., to improve the segmentation effect, which also has certain reference value for other medical image processing.

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