Content Based Medical Image Retrieval and Clustering Based Segmentation to Diagnose Lung Cancer

摘要

Now a day lung cancer is most serious health problem in the world which causes multiple deaths every year. There are various techniques available for diagnosis of the lung cancer such as CT image, MRI image, X-Ray Image etc. but the CT scan image provides greater details about multiple organs of lungs. Hence today the medical images are generated more and more in their daily activities which are millions in size. Retrieving medical images from the large collection is a challenging task, therefore it emerges content based medical image retrieval system (CBMIR) system. The retrieval system proposed clustering based segmentation for diagnoses of the lung cancer. Basically it has three phases. First is segmentation for segment out the lung image into particular regions, second phase describes the texture feature extraction of lung regions and third is clustering which is used to classify and arranged into images in particular cluster which is further improved the speed and accuracy of system by retrieving images. It is analysis and measures the performance in terms of precision and recall with respect to time.