Lung Tumor Segmentation Based On Combination of Concave Hull Region Growing Algorithm

Abstract

In this Paper, the lung tumor segmentation and classification from CT images is done. Image processing is used in the medical field for detection of tumor. Image segmentation is a vital part of image processing. Segmentation is the process of partitioning an image into distinct regions. The proposed algorithm has six steps. They are image acquisition, preprocessing, lung boundary correction, tumor part segmentation, feature extraction and classification. The image is preprocessed using Adaptive median filtering. The lung lobe is extracted usingcanny edge detection. The lung boundary correction is performed using Adaptive Concave Hull algorithm. Segmentation is performed using Region growing based technique. Then for the segmented tumor region, the features are extracted using the GLCM (Gray Level Co-occurrence Matrix) algorithm. From the features extracted, the image is classified as the benign or malignantlung cancer by using the SVM with BOVW (Bag of Visual Word) classifier.