An unconstrained growth of abnormal cells in the lung causes tumors. The aim of the work is the accurate diagnoses of tumor at its early stage through hybrid segmentation algorithm. The objective of this paper is to propose a novel labeled cluster active contour method to improve the performance of automated lung tumor segmentation from 2D CT slices. To the input 2D slice, an 8-connected component analysis is implemented to differentiate the image spatial information and obtain the RGB labeled data. The tumor location is foreseen using an unsupervised k-means clustering algorithm. Then, an automated level set algorithm is carried out to appropriately localize and extract the tumor region. The amount of initial level set curve evolution is controlled by the clustering efficiency of k-Means and labeling efficacy of connected component analysis. The quantitative evaluation of segmentation is carried out based on Shape features like area, perimeter, eccentricity, convex area, solidity, and roundness. The statistical object-based and distance-based metrics were used to find the similarity between manual and proposed methods. Also, performance metrics like accuracy, specificity, sensitivity, precision, and recall are used to validate the segmentation results of the proposed method. The proposed system is evaluated over 42 datasets from the commonly available large dataset LIDC (Lung Image Database Consortium). The accuracy, specificity, sensitivity and precision of the proposed hybrid method are 97.5%, 97.43%, 91.67%, and 98.79%, which exhibited the best competence as compared to traditional methods on the same dataset. The statistical and quantitative analysis shows the efficiency of the present work.