Texture Pattern Based Lung Nodule Detection (TPLND) Technique in CT Images
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Lung cancer has become one of the leading causes of cancer related death in both men as well as women which ranges for about 30% of cancer death occurring in the world. Medical image analysis is a complex task in which a human expert makes extensive use of the knowledge of anatomy and imaging techniques. Specially, the detection of the presence of lung nodules is challenging problem from a computer vision point of view. The early detection of the lung cancer can definitely improve the long term health of those people diagnosed with it. Evaluation of the variation of cardiac size from month to month by taking serial chest images remains crucial for the treatment of lung cancer. In this paper, we have proposed an efficient method for detecting the presence of the lung nodule with the help of CT images. The proposed method is carried out using three processes such as segmentation, classification and detection. Here we utilized clustering for classifying the lung images as normal or abnormal image. These methods helped to improve the early detection of the lung nodules. Our proposed method proved to be a highly efficient method for the detection of lung nodule with high rate of accuracy.
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