State of Art Survey on Lung Tumor Segmentation and Classification Techniques for Various Image Modalities
Lung disease is the excessive growth of lung cells that poses a serious risk to human. The CT imagery is faster and more cost effective than low-radiation MRI. Lung disease survival rate greatly depends on early detection and lung tumor staging. Computed tomography (CT) images are commonly used for the identification of lung tumors. Visual interpretation of database can lead to cancer detection at later stages, since this causes increased death rate. Therefore, image processing tools can be used for early detection of cancer. Lung tumor detection algorithms are reviewed for current tendency to notice the development as well as future challenges in this area. Scientists have developed various techniques for tumor nodule detection from two-dimensional CT images, so the issues related with such approaches are lung separation from history, and tumor distinction from airways. Medical images include factors such as noise, unclear tumor borders and large tumor presence variations that make it difficult to identify exact tumor location. To solve these challenges, there are several tools available to help the radiologist diagnose the disease accurately. This work discusses the review of recent trends in lung tumor. This review is very useful for the medical practitioner and professional researchers.
Keywords: CT imaging, Segmentation, Classification, Computer Aided Detection, Lung tumor.