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An efficient Autoseed Region Growing with Morphological Masking(ARGMM) is imple-mented in this paper on the Lung CT Slice to segment the 'Lung Nodules',which may be the potential indicator for the Lung Cancer. The segmentation of lung nodules car-ried out in this paper through Multi-Thresholding, ARGMM and Level Set Evolution. ARGMM takes twice the time compared to Level Set, but still the number of suspected segmented nodules are doubled, which make sure that no potential cancerous nodules go unnoticed at the earlier stages of diagnosis. It is very important not to panic the patient by finding the presence of nodules from Lung CT scan. Only 40 percent of nod-ules can be cancerous. Hence, in this paper an efficient Shape and Texture analysis is computed to quantitatively describe the segmented lung nodules. The Frequency spectrum of the lung nodules is developed and its frequency domain features are com-puted. The Complete Local binary pattern of lung nodules is computed in this paper by constructing the combine histogram of Sign and Magnitude Local Binary Patterns. Lo-cal Configuration Pattern is also determined in this work for lung nodules to numeri-cally model the microscopic information of nodules pattern.