scholarly journals Automatic Vector Seeded Region Growing for Parenchyma Classification in Brain MRI

10.5772/28801 ◽  
2012 ◽  
Author(s):  
Chuin-Mu Wang ◽  
Ruey-Maw Che
Keyword(s):  
Brain Mri ◽  
Region Growing ◽  
Seeded Region Growing

scholarly journals A Study on the Application of Fuzzy Information Seeded Region Growing in Brain MRI Tissue Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Chuin-Mu Wang ◽  
Geng-Cheng Lin
Keyword(s):  
Detection Method ◽  
Brain Mri ◽  
Region Growing ◽  
Generation Process ◽  
Tissue Segmentation ◽  
Seeded Region Growing ◽  
Highly Sensitive ◽  
Merging Process ◽  
Edge Detection Method ◽  
Initial Seed

After long-term clinical trials, MRI has been proven to be used in humans harmlessly, and it is popularly used in medical diagnosis. Although MR is highly sensitive, it provides abundant organization information. Therefore, how to transform the multi-spectral images which is easier to be used for doctor’s clinical diagnosis. In this thesis, the fuzzy bidirectional edge detection method is used to solve conventional SRG problem of growing order in the initial seed stages. In order to overcome the problems of the different regions, although it is the same Euclidean distance for region growing and merging process stages, we present the peak detection method to improve them. The standard deviation target generation process (SDTGP) is applied to guarantee the regions merging process does not cause over- or undersegmentation. Experimental results reveal that FISRG segments a multispectral MR image much more effectively than FAST andK-means.

Seeded Region Growing Based on Extension for Multispectral MR Images Classification

2014 ◽  
Vol 1079-1080 ◽  
pp. 872-877
Author(s):  
Yen Che Chang ◽  
Kuei Ting Kuo ◽  
Zih Yi Wang ◽  
Chuin Mu Wang
Keyword(s):  
Cerebral Spinal Fluid ◽  
Brain Mri ◽  
Medical Knowledge ◽  
Region Growing ◽  
Mr Images ◽  
Brain Images ◽  
New Approach ◽  
The Past ◽  
Seeded Region Growing ◽  
Effective Diagnosis

In the past, doctors judged images based on their own medical knowledge. Nowadays, the digital image processing technology can alleviate the burden of judging a large amount of multispectral information and lead to more effective diagnosis of the pathological tissues. In this paper, we propose a new approach of seeded region growing based on extension (SRGBE) to classify tissues from brain MRI. Based on extension, we tried to strengthen the regional definition. First, we use seeded region growing (SRG) to segment brain images. Second, the SRGBE result is further classified by K-means. Finally, we compare the images of gray matter, white matter and cerebral spinal fluid produced by both approaches to demonstrate the performance of SRGBE.

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

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.

scholarly journals Texture feature based automated seeded region growing in abdominal MRI segmentation

2009 ◽  
Vol 02 (01) ◽  
pp. 1-8 ◽  
Author(s):  
Jie Wu ◽  
Skip Poehlman ◽  
Michael D. Noseworthy ◽  
Markad V. Kamath
Keyword(s):  
Texture Feature ◽  
Region Growing ◽  
Mri Segmentation ◽  
Seeded Region Growing ◽  
Abdominal Mri ◽  
Feature Based

scholarly journals La transformada de Radon aplicada a la segmentación de imágenes digitales en escala de grises

2018 ◽  
Vol 17 (32) ◽  
pp. 213-227
Author(s):  
Ricardo Joaquín de Armas Costa ◽  
Shirley Viviana Quintero Torres ◽  
Cristina Acosta Muñoz ◽  
Carlos Camilo Guillermo Rey Torres
Keyword(s):  
Region Growing ◽  
Seeded Region Growing

En este artículo de investigación científica se da a conocer a la comunidad interesada en el procesamiento digital de imágenes, una aplicación inédita de la transformada de Radon para segmentar imágenes en escala de grises, lo que permite la identificación y clasificación de regiones u objetos, misma que puede extenderse a imágenes en color. Los resultados obtenidos se compararon con los resultados de dos algoritmos clásicos de segmentación: el algoritmo de umbralización Otsu optimizado, y el algoritmo de crecimiento de regiones Seeded Region Growing.

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