Middle-East Journal of Scientific Research 23 (3): 436-441, 2015 ISSN 1990-9233 © IDOSI Publications, 2015 DOI: 10.5829/idosi.mejsr.2015.23.03.22088 Corresponding Author: Bottu Balagangadhar, Department of ECE Sathyabama University, Chennai, India. 436 A Survey on PET-CT Lung Tumor Delineation Bottu Balagangadhar and K. Srilatha Department of ECE Sathyabama University, Chennai, India Abstract: Accurate parenchymal lung tumor delineation with PET-CT can be problematic given the inherent tumor heterogeneity and proximity / involvement of extra-parenchymal tissue. In this proposed method, we propose a tumor delineation approach that is based on new tumor–Ground truth method models for a given image. The ground truth method give more efficient result compared to other methods. Then this method extends to segmentation, region based graph methods (n-cut and a-cut) and another segmentation model is k-means algorithm is used. After the tumor detection part move on to classification part, this classification done by neural network based implementation has been used. The result computation is very fast the more efficiency. In background likelihood method not efficient compare other methods. The above model is processed in MATLAB tool and achieve high efficiency in the detection and classification model compared to previous one. Key words: CT Dicom Ground truth PET Segmentation INTRODUCTION applying conventional Thresholding methods and Lung cancer is the one of the most leading cause of Currently, the most effective image technique for early cancer death in men and the second leading cause of detection of lung cancers is PET-CT imaging technique women. Every year, nearly more than 90,000 men and and it is the most reliable tool [2]. 79,000 women are diagnosed because of cancer. Now coming to cancer statistics about 12.7 millions cancer Lung Tumor: Cancer is a group of diseases that are cases and 7.6 million cancer deaths are estimated to have characterized by an abnormal and unregulated growth of occurred in 2008. This result tells us one fourth of the cells. Those cells are we can see in Fig. 1. The main population will be diagnosed with the cancer in their differences are that beginning tumor grows slowly and it lifetime and one fifth of the cases will go to die from the will usually not come back if it is surgically removed. lung tumor disease [1]. There are two groups of lung cancer, those are Small cell Most of them notice their disease when it’s too late Lung cancer (SCLC) and the other one is Non-Small Cell and the surgery will be tough to do. In 2011, lung cancer Lung Cancer (NSCLC), which covers more than 85% of all has been the main cause of death from cancers by 15%. cases. CT scans (Computed Tomography), PET scans Today, several types of techniques are used diagnosis of (Positron Emission Tomography) and MRI (Magnetic the disease, such as Computerized Tomography, Positron Resonance Imaging) is the diagnosing methods are Emission Tomography, Chest Radiograph (x-ray), available to detect Lung cancer; [3]. Magnetic Resonance Imaging (MRI scans). We can calculate the lung tumor based on tumor size These techniques can detect only in its advanced and we can give which stage it is also calculated. Now we stages, but we need the technique to diagnose in its early will see stages of lung tumor. stages. Different software tools are use diagnose the lung This is an early stage of lung tumor; some symptoms cancer, such as 3D-Doctor, Workstation, analyzes, gave clarity about lung cancer. Those symptoms are 3dviewnix, 3D slicer. Some of these software tools as weight loss, chest infections, coughing up blood, difficult image support some visual formats like Dicom and to take a breath, tired, lack of energy and shoulder pain. analyze. Some tools provide features such as processing These are very early symptoms of lung cancer. There and visualization for instant registration, noise many reasons caused by to get a lung cancer. Those suppression, analyzing images for diagnostic purposes, reasons we can see in Figure 3 [4]. representing 2D and 3D data using visualization methods.