Tumor Cell Identification in CT Images using Image Processing Approach

In this book, various new algorithmic models for detecting lung component to identify the exact tumor cell in a CT (Computed Tomography) images are proposed. The newly suggested methods are simple and heuristics to detect tumor cell in a CT images. In our work, we shall focus on five tasks to achieve better accuracy for identify the exact tumor cell in a CT images. They are; • Max Min cluster algorithm and histogram information of subcomponents in the lung component to verify the tumor cell in the components. • Quad tree concept and sharpness based method and various filter technique. • Different classifier to detect tumor cell is proposed, in which we have explored sharpness features to identify the tumor cell. • Clustering on number of holes in the components of the canny edge image of the input image. • Spatial information of sub-components in the lung component to verify and ascertain the detected lung components. Then the method studies sharpness of each sub-component in the detected lung component to identify the exact tumor cell Lung cancer is one of the most common and deadly diseases in the world. Detection of lung cancer in its early stage is the key of its cure. Therefore, an attempt to detect lung cancer at an early stage is required, so that it may increase the chances of survival among cancer patients. In this work, unlike existing methods a different classifier to detect tumor cell is proposed. We introduce Max Min cluster algorithm and histogram information of sub-components in the lung component to verify the tumor cell in the components. Then the method studies size of each sub-component in the detected lung component to identify the exact tumor cell. The method is tested on a variety of images and is compared with the existing method to show that the proposed method is superior.