Image Transformation With Lung Image Thresholding and Segmentation Method

  • Sahat Sonang Sitanggang Politeknik Bisnis Indonesia
  • Yuhandri Yuhandri Universitas Putra Indonesia YPTK Padang
  • Adil Setiawan Universitas Potensi Utama
Keywords: Transformation, Image, Threshold, Segmentation, Lung


Image transformation is important to obtain and find certain information about an image that was not previously known, such as pixels, geometry, size, and color. Following this, this research aims to analyze image transformation in producing better values using threshold and segmentation methods. The segmentation process is carried out based on two color models, namely hue saturation value (HSV) and red green blue (RGB). The image data used in this study was the x-ray image of the lungs from which is processed using the Matlab 2021a application to help the analysis process.  on the results of the image segmentation analysis carried out in this case, the greater the HSV and RGB threshold values used in the image data, the better and clearer the segmentation of the detected image results. In other words, the size of the thresholding value generated greatly affects the quality, brightness, size, and color of the resulting image. The best lung X-ray image segmentation results were obtained when using the threshold values HSV = 0.9 and RGB = 9.



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How to Cite
Sitanggang, S. S., Yuhandri, Y., & Adil Setiawan. (2023). Image Transformation With Lung Image Thresholding and Segmentation Method. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(2), 278 - 285.
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