METODE SEGMENTASI PARU-PARU DAN JANTUNG PADA CITRA X-RAY THORAX

• Main Authors: , Ainatul Mardhiyah, , Drs. Agus Harjoko, M.Sc., Ph.D
• Format: Theses and Dissertations NonPeerReviewed
• Published: [Yogyakarta] : Universitas Gadjah Mada 2012
• Subjects: ETD
• Online Access: https://repository.ugm.ac.id/99100/; http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=55130
• Institution: Universitas Gadjah Mada

Image segmentation is an important technology for image processing, especially in the medical world. If a doctor or radiologist doing wrong in the process of reading the image it will affect the diagnosis of a disease. This study uses x-ray image of the thorax in the segmentation process. Image acquisition is performed in the early stages of retrieval of image data, then the input image is converted into a size of 256x256 pixels. In order to walk with a maximum image segmentation is necessary to start the process (preprocessing) using Gaussian Lowpass Filter method. Further image preprocessing results are grouped using K-means Clustering method in which the grouping is done based on the difference in pixel values in the image. The results of these groupings form the object of the lungs and heart. The last process in this study is performed segmentation using Geometric Active Contour method. In this method, the curve will deflate into accordance with the form the outer edge of the lung and heart. The conclusion that the K-means Clustering and Geometric Active Contour can be to segment the lungs and heart on x-ray image of the thorax. Tests performed by the method of system ROC (Receiver Operating Characteristic), from 40 x-ray image using K-means Clustering with K = 2 and Geometric Active Contour system can segment the left lung, with a percentage accuracy of 90.03%, sensitivity 62.05%, and spesifity 94.62%. Right lung, with a percentage accuracy of 88.35%, sensitivity 63.71%, and spesifity 93.48%. Heart segmentation using Template Matching system can segment the heart, with a percentage accuracy of 94.33%, sensitivity 64.65%, and spesifity 98.13%.