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.

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

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.

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