Classification of Cervix Tumor using Bag of Visual Word Classifier

Hema Rajini N

Segmentation, Region growing, Feature extraction, Gray level co-occurrence matrix, Bag of visual word.

A tumor segmentation and classification system has been designed and developed on computed tomography images. Image processing is used in the medical field for detection of tumor. Image segmentation is an important part of image processing. Segmentation is the process of subdividing an image into distinct regions. The algorithm has the steps of preprocessing, cervix extraction, cervix boundary correction, image segmentation, feature extraction and image classification. The image is preprocessed using adaptive median filtering and fuzzy thresholding. The cervix is extracted using canny edge detection and border tracing algorithm. The cervix 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 gray level co-occurrence matrix. After the features are extracted, the image is classified as the benign or malignant cervix by using the bag of visual word classifier.