Lung tumour detection and classification using EK-Mean clustering

In recent years the image processing techniques are used commonly in various medical areas for improving prior detection and treatment stages, in which the time span or elapse is very important to identify the disease in the patient as possible as fast, especially in many tumours such as the lung cancer, breast cancer. This system first segments the region of interest (lung) and then analyses the separately obtained area for nodule detection in order to examine the disease. Even with several lung tumour segmentations have been presented, enhancing tumour segmentation methods are still interesting because lung tumour CT images has some complex characteristics, such as large variation in tumour appearance and uncertain tumour boundaries. To address this problem, tumour segmentation method for CT Images which takes apart non-enhancing lung tumours from healthy tissues has been carried out by clustering method. The proposed method uses pre-processing technique that remove unwanted artifacts using median and wiener filters. Initially, the segmentation of the CT images has been carried out by using K- Means clustering method. To the clustered result, EK-Mean clustering is applied . Further the features like entrpy, Contrast, Correlation,Homogenity and the area are extracted from the tumorous part of Fuzzy Ek- Means segmented Image. For feature extraction, statistic method called Gray Level Co-occurrence Matrix (GLCM). Classification is done by using the supervised neural network called the Back Propagation Network (BPN). Results of the classification gives, whether the CT Image is a normal Image or cancerous.