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Automatic lung cancer detection using color histogram calculation

机译:使用颜色直方图计算自动检测肺癌

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Lung cancer is a disease that caused by uncontrolled cell growth in lung. Lung cancer is still the first worldwide killer. CT Scan Thorax is a method for early detection of lung cancer patients. However, cancer detection in lung CT-Scan image still done manually. In this paper, the segmentation of lung image is proposed. Cancer segmentation will process the lung CT-Scan as an image input with watershed process to cut off cavity area. The result will be processed by color histogram calculation to obtain mean and standard deviation value. This value is useful for evaluate non-cancer area and produce cancer image. Segmentation process will be followed by measurement of cancer and cavity area. The overall output is percentage between the large of cancer area and cavity area. The experiment represented that this method is able to detect lung cancer automatically. The performance segmentation for assessment errors obtained an average cavity area segmentation 12.75% and cancer area segmentation 31.74%.
机译:肺癌是由肺中不受控制的细胞生长引起的疾病。肺癌仍然是全球首个杀手。 CT Scan Thorax是一种早期检测肺癌患者的方法。但是,肺部CT扫描图像中的癌症检测仍需手动完成。本文提出了肺图像的分割方法。癌症分割将把肺部CT-Scan作为图像输入,并进行分水岭处理,以切断空腔区域。结果将通过颜色直方图计算得到平均值和标准偏差值。此值可用于评估非癌区域并产生癌影像。在分割过程之后,将测量癌症和腔的面积。总输出量是大的癌区域和腔区域之间的百分比。实验表明该方法能够自动检测肺癌。评估误差的性能分割获得了平均腔区域分割为12.75%,癌症区域分割为31.74%。

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