摘要：At present, the research on detection of lung cancer includes the sample image segmentation, extracting visual features of lung cancer and generating the classification model by training learning, then according to the classification model generated to classify the inspected images. But this kind of method usually needs a large amount of calculation and speed is slow. In order to find the region of interest as soon as possible and improve the detection speed, this paper attempts to introduce the current popular Itti visual attention model into the lung cancer detection. However, because medical images usually have low contrast, the Itti method is not directly applied to extract the region of interest in medical image. Therefore the selective visual attention mechanism combined with shape feature of target is proposed. Firstly some primary features are chosen, such as gray scale, direction, corner point and edge to generate saliency map, and then the significant regions are segmented and judged. Compared to popular lung cancer detection method, this method can improve the detection rate of suspected lung cancer and has great significance for the early detection, early diagnosis and treatment of lung cancer.
