## 2 Background  
Lung cancer remains the leading cause of mortality. There is significant evidence indicating that the early detection of lung cancer will decrease the mortality rate, by using asymptomatic screening methods, followed by effective treatment. Most recent research relies on quantitative information, such as size, shape, and the ratio of the affected cells. Computer vision methods are employed to elicit information from medical images, such as the detection of cancerous cells. Many diagnostic ambiguities are removed when transforming these images from their continuous to their digital form. Today, very large amounts of data are produced from medical imaging modalities, such as sputum cytology, computed tomography (CT) and magnetic resonance imaging (MRI) [6]. In the literature, there exists many modalities that have been used for detecting lung cancer, some of them can detect the cancer in early stages, and others can detect the cancer in advanced stages. Some of lung cancer modalities are: computed tomography (CT) scans, positron emission tomography (PET) data, X-ray images and sputum color image analysis. The authors in [7] used CT scans to detect histological images of the lungs and diagnose into cancerous or non-cancerous nodules. The segmentation process is done by using a fuzzy system based on the area and the gray level of the nodule region. These methods attain an accuracy of 90% with high values for sensitivity and specificity that can meet the clinical diagnosis requirement. Other authors [8] used high resolution (HRCT) images to detect small lung nodules by applying a series of 3D cylindrical and spherical filters. The drawback was in the limitation of this method in detecting all cancerous nodules. Using CT scans in detecting lung cancer has a number of limitations with high false-positive rate, because it detects a lot of non-cancerous nodules and it misses many small cancer nodules. The PET scan which is used in conjunction with X-rays or CT scans, is considered the most recent nuclear medicine imaging of the functional processes within the human body. The biggest advantage of a PET scan, compared to other modalities, is that it can reveal how a part of the patient's body is functioning, rather than just how it looks. The author in [9] proposed an automated process of tumor delineation and volume detection from each frame of PET lung images. The spatial and frequency domain features have been used to represent the data. K-nearest neighbor and support vector machines (SVM) classifiers have been used to measure the performance of the features. Wavelet features with a SVM classifier gave a consistent accuracy of 97% with an average sensitivity and specificity of 81% and 99%, respectively. The calculated volume from the detected tumor by the proposed method matched the manually segmented volume by physicians. Their methods succeeded in eliminating the need for manual tumor segmentation, thus reducing physician fatigue to a great extent, however the limitation of the PETscan is that it can be time consuming. It can take from several hours to days for the radiotracer to accumulate in the body part of interest. In addition, the resolution of body structures with nuclear medicine may not be as high as with other imaging techniques, such as CT. The X-ray image, or chest radiography, is one of the most commonly used diagnostic modalities in lung cancer detection and is regarded as one of the cheapest diagnostic tools. The authors in [10] proposed a nodule detection algorithm in chest radiographs. The algorithm consisting of four steps: image acquisition, image preprocessing, nodule candidate segmentation and feature extraction. Active shape model technique for lung image segmentation was used, while gray level co-occurrence matrix technique was adopted for texture features. The limitation can be in its invasive nature (patients' radiation exposure), plus high false positive and negative rates.