Feature Extraction Based Lung Nodule Detection in CT Images

摘要

Lung cancer diagnosis using pattern classification is the active research topics in medical image processing. Feature extraction is considered as an essential step in medical image analysis and classification. Computer aided segmentation for computed tomography (CT) and magnetic resonance imaging (MRI) are finding the application in computer aided diagnosis, clinical studies, and treatment planning. In medical images typically suffer from one or more imperfections such as low resolution (in the spatial and spectral domains) and low contrast images. This work proposed the black circular neighborhood algorithm for feature extraction and genetic algorithm (GA) based clustering using the extracted nodules. The performance of our algorithm is to reduce number of false positives (FP) and results are improves the accuracy.