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Adaptive Enhancement Technique for Cancerous Lung Nodule in Computed Tomography Images

机译:计算机断层扫描图像中癌性肺结节的自适应增强技术

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Diagnosis the Computed Tomography Images (CT-Images) may take a lot of time by the radiologist. This will increase the radiologist fatigue and may miss some of the cancerous lung nodule lesions. Therefore, an adaptive local enhancement Computer Aided Diagnosis (CAD) system is proposed. The proposed technique is design to enhance the suspicious cancerous regions in the CT-Images. The visual characteristics of the cancerous lung nodules in the CT-Images was the main criteria in designing this technique. The new approach is divided into two phases, pre-processing phase and image enhancement phase. The image noise reduction, thresholding process, and extraction the lung regions are considered as a pre-processing phase. Whereas, the new adaptive local enhancement method for the CTImages were implemented as a second phase. The proposed algorithm is tested and evaluated on 42 normal and cancerous lung nodule CT-Images. As a result, this new approach can efficiently enhance the cancerous lung nodules by 25% comparing with the original images.
机译:诊断计算的断层摄影图像(CT-Images)可以通过放射科医师提高大量时间。这将增加放射科医师疲劳,可能会错过一些癌症肺结节病变。因此,提出了一种自适应局部增强计算机辅助诊断(CAD)系统。所提出的技术是设计,以增强CT图像中的可疑癌症区域。 CT图像中癌肺结节的视觉特征是设计该技术的主要标准。新方法分为两个阶段,预处理阶段和图像增强阶段。图像降噪,阈值处理和提取肺部区域被认为是预处理阶段。虽然,CTImages的新自适应局部增强方法被实施为第二阶段。在42例正常和癌肺结节CT图像上进行测试和评估所提出的算法。结果,这种新方法可以通过与原始图像相比,有效地增强癌症肺结节25%。

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