Abstract
This research work intents to remove the intricacies involved in demarcating the malignant and benign of the spiculated Solitary Pulmonary Nodules (SPN). Edges can be classified as irregular edge with corona radiata, lobulation, notching signs and a distinct soft, uncloudy contour edge. These edges are hardly spotted in bronchial carcinoma. This paper develops an algorithm for automatically detecting stipulated nodules using BPN algorithm, from the given computed tomography (CT) lung image. Here, to automate the detection of lung nodule, parametric active contours are used for manual segmentation. Features are extracted from gray level co-occurrence matrix (GLCM) derived from manually segmented lung nodule and used for further classification as nodule and non-nodule/normal image. This paper further classifies spiculated nodule into malignant or benign by fixing a threshold for the average image intensity after administering contrast.
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