A New Connected Component Analysis Algorithm Based on Max-Tree

2009 ◽  
Author(s):  
Yan Fu ◽  
Xu Chen ◽  
Hui Gao
Keyword(s):  
Component Analysis ◽  
Connected Component ◽  
Analysis Algorithm ◽  
Connected Component Analysis

scholarly journals Dental Images’ Segmentation Using Threshold Connected Component Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Vincent Majanga ◽  
Serestina Viriri
Keyword(s):  
Deep Learning ◽  
Region Of Interest ◽  
Component Analysis ◽  
Connected Components ◽  
Connected Component ◽  
Low Contrast ◽  
Connected Component Analysis ◽  
Feature Representations ◽  
Learning Techniques ◽  
Performance Results

Recent advances in medical imaging analysis, especially the use of deep learning, are helping to identify, detect, classify, and quantify patterns in radiographs. At the center of these advances is the ability to explore hierarchical feature representations learned from data. Deep learning is invaluably becoming the most sought out technique, leading to enhanced performance in analysis of medical applications and systems. Deep learning techniques have achieved great performance results in dental image segmentation. Segmentation of dental radiographs is a crucial step that helps the dentist to diagnose dental caries. The performance of these deep networks is however restrained by various challenging features of dental carious lesions. Segmentation of dental images becomes difficult due to a vast variety in topologies, intricacies of medical structures, and poor image qualities caused by conditions such as low contrast, noise, irregular, and fuzzy edges borders, which result in unsuccessful segmentation. The dental segmentation method used is based on thresholding and connected component analysis. Images are preprocessed using the Gaussian blur filter to remove noise and corrupted pixels. Images are then enhanced using erosion and dilation morphology operations. Finally, segmentation is done through thresholding, and connected components are identified to extract the Region of Interest (ROI) of the teeth. The method was evaluated on an augmented dataset of 11,114 dental images. It was trained with 10 090 training set images and tested on 1024 testing set images. The proposed method gave results of 93 % for both precision and recall values, respectively.

scholarly journals ROAD NETWORK IDENTIFICATION AND EXTRACTION IN SATELLITE IMAGERY USING OTSU'S METHOD AND CONNECTED COMPONENT ANALYSIS

2018 ◽  
Vol XLII-5 ◽  
pp. 91-98 ◽  
Author(s):  
P. Yadav ◽  
S. Agrawal
Keyword(s):  
High Resolution ◽  
Road Network ◽  
Satellite Images ◽  
Component Analysis ◽  
Morphological Operations ◽  
Connected Component ◽  
Road Detection ◽  
Connected Component Analysis ◽  
High Resolution Satellite Images ◽  
Otsu’S Method

<p><strong>Abstract.</strong> As the high resolution satellite images have become easily available, this has motivated researchers for searching advanced methods for object detection and extraction from satellite images. Roads are important curvilinear object as they are a used in urban planning, emergency response, route planning etc. Automatic road detection from satellite images has now become an important topic in photogrammetry with the advances in remote sensing technology. In this paper, a method for road detection and extraction of satellite images has been introduced. This method uses the concept of histogram equalization, Otsu's method of image segmentation, connected component analysis and morphological operations. The aim of this paper is to discover the potential of high resolution satellite images for detecting and extracting the road network in a robust manner.</p>

Sign in / Sign up

Export Citation Format

Share Document