Automatic segmentation and recognition of lungs and lesion from CT scans of thorax
| Autor: | Dag Rune Olsen, Manish Kakar |
|---|---|
| Rok vydání: | 2008 |
| Předmět: |
Lung Neoplasms
Computer science Initialization Health Informatics Image processing Sensitivity and Specificity Pattern Recognition Automated Imaging Three-Dimensional Fuzzy Logic Image Processing Computer-Assisted Cluster Analysis Humans Radiology Nuclear Medicine and imaging Segmentation Computer vision Cluster analysis Lung Models Statistical Radiological and Ultrasound Technology Artificial neural network Anatomy Cross-Sectional business.industry Norway Pattern recognition Thorax Computer Graphics and Computer-Aided Design Support vector machine Pattern recognition (psychology) Radiography Thoracic Computer Vision and Pattern Recognition Tomography Artificial intelligence Neural Networks Computer business Tomography X-Ray Computed |
| Zdroj: | Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society. 33(1) |
| ISSN: | 1879-0771 |
| Popis: | In this study, a fully automated texture-based segmentation and recognition system for lesion and lungs from CT of thorax is presented. For the segmentation part, we have extracted texture features by Gabor filtering the images, and, then combined these features to segment the target volume by using Fuzzy C Means (FCM) clustering. Since clustering is sensitive to initialization of cluster prototypes, optimal initialization of the cluster prototypes was done by using a Genetic Algorithm. For the recognition stage, we have used cortex like mechanism for extracting statistical features in addition to shape-based features. The segmented regions showed a high degree of imbalance between positive and negative samples, so we employed over and under sampling for balancing the data. Finally, the balanced and normalized data was subjected to Support Vector Machine (SimpleSVM) for training and testing. Results reveal an accuracy of delineation to be 94.06%, 94.32% and 89.04% for left lung, right lung and lesion, respectively. Average sensitivity of the SVM classifier was seen to be 89.48%. |
| Databáze: | OpenAIRE |
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