Automated diagnosis of Lungs Tumor Using Segmentation Techniques
| Autor: | S. Piramu Kailasam, M. Mohammed Sathik |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Local binary patterns
Computer science business.industry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Particle swarm optimization Wavelet transform Scale-invariant feature transform Pattern recognition 030218 nuclear medicine & medical imaging 03 medical and health sciences ComputingMethodologies_PATTERNRECOGNITION 0302 clinical medicine Bag-of-words model in computer vision Histogram Segmentation Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | International Journal Of Engineering And Computer Science. |
| ISSN: | 2319-7242 |
| Popis: | The Objective is to detect the cancerous lung nodules from 3D CT chest image and classify the lung disease and its severity. Although so many researches has been done in this stream, the problem still remains a challenging one. To extract the lung region FCM segmentation is used. Here we used six feature extraction techniques such as bag of visual words based on the histogram oriented gradients, the wavelet transform based features, the local binary pattern, SIFT and Zernike moment . The Particle swarm optimization algorithm is used to select the best features. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|