Papers by Keyword: Lung Nodule

Abstract: In this paper, we have proposed a new way to detect lung nodules with image texture features. 104 cases of lung nodules, including 31 benign cases and 73 malignant cases, are collected, and the gray-scale correlation and texture heterogeneity are computed through CT imagings for all patients. We find that the gray correlation parameters are different between benign and malignant nodules. The heterogeneity parameters in malignant nodules are higher than that in benign noduals. The gray-scale texture correlation and heterogeneity parameters have diagnostic value in differentiating benign and malignant lung nodules. This study is an exploring study, which still needs further research.

Abstract: Contemporary computed tomography (CT) technology offers the better potential of screening for the early detection of lung cancer than the traditional x-ray chest radiographs. In order to help improve radiologists’ diagnostic performance and efficiency, many researchers propose to develop computer-aided detection and diagnosis (CAD) system for the detection and characterization of lung nodules depicted on CT images and to evaluate its potentially clinical utility in assisting radiologists. Based on review of computer-aided detection and diagnosis of lung nodules using CT at home and abroad in recent years, this paper presented a new algorithm that achieves an automated way for applying multi-scale nodule enhancement, mathematical morphology and morphological Segmentation.

Abstract: For solving the segmentation problem of vessel attachment nodule, a new adaptive bandwidth chosen method based on EM is proposed and apply it into Mean-shift algorithm to segment vessel attachment nodule. This method has some advantages such as time low complexity and correct bandwidth when comparing it to the method of bandwidth chosen based on statistical analysis rule or optimized rule, Imposing the vertical orientation vectors of vessel’s gradient submitting to normal distribution and the vertical orientation vectors of nodule’s gradient submitting to uniform distribution, modeling the nodule connected vessel, and estimating model parameter by EM, extract bandwidth parameter in Mean-shift based on the weight of uniform distribution. The proposed method was tested on synthetic data set and the clinical chest CT volumes, and all the results were correct. The results revealed that the proposed method is successful in segmentation lung vessel attachment nodule.