A hybrid method of seeded region growing and region hue-area information fusion for object segmentation under patterned background

2007 ◽  
Author(s):  
Yuxi Chen ◽  
Chongzhao Han
Keyword(s):  
Information Fusion ◽  
Hybrid Method ◽  
Object Segmentation ◽  
Region Growing ◽  
Seeded Region Growing

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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