Metode Segmentasi Paru-paru dan Jantung Pada Citra X-Ray Thorax

https://doi.org/10.22146/ijeis.1961

Ainatul Mardhiyah(1*), Agus Harjoko(2)

(1) Universitas Islam Negeri Maulana Malik Ibrahim Malang
(2) Jurusan Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(*) Corresponding Author

Abstract


Abstrak

Segmentasi citra merupakan salah satu tahapan dalam pengolahan citra yang penting, terutama dalam dunia medis. Apabila seorang dokter atau ahli radiologi salah dalam melakukan proses pembacaan citra maka akan berpengaruh terhadap diagnosa suatu penyakit.

Penelitian ini, menggunakan citra x-ray thorax dengan format grayscale dan ukuran 256x256, agar segmentasi berjalan dengan maksimal dilakukan proses awal (preprocessing) menggunakan metode Gaussian Lowpass Filter. Selanjutnya citra hasil preprocessing dikelompokkan menggunakan metode K-Means Clustering dimana pengelompokan tersebut dilakukan berdasarkan perbedaan nilai piksel pada citra. Hasil dari pengelompokan tersebut membentuk objek paru-paru. Selanjutnya dilakukan segmentasi dengan menggunakan metode Geometric Active Contour. Pada metode ini, kurva akan mengempis atau mengembang sesuai dengan bentuk tepi luar dari paru-paru. Segmentasi jantung menggunakan metode template matching dikarenakan dengan menggunakan K-means Clustering dengan K = 2, objek jantung belum bisa tersegmentasi.

Ujicoba sistem dilakukan dengan metode ROC (Receiver  Operating Characteristic), dari 40 data citra x-ray thorax menggunakan metode k-means clustering untuk K=2 dan Geometric Active Contour sistem dapat mensegmentasi paru-paru kiri dengan prosentase akurasi 90.03%, sensitifitas 62.05%, dan spesifitas 94.62%. Paru-paru kanan dengan prosentase akurasi 88.35%, sensitifitas 63.71%, dan spesifitas 93.48%. Segmentasi jantung dengan template matching didapatkan presentase akurasi 94.33%, sensitifitas 64.65%, dan spesifitas 98.13%.


Kata kuncisegmentasi citra, x-ray thorax, k-means clustering, geometric active contour, template matching

 

Abstract

 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 research uses x-ray thorax in grayscale and 256x256 pixel. In order to 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 based on the difference in image pixel values . Furthermore, segmentation using Geometric Active Contour method. In this method, the curve will deflate into accordance with the form the lung edge. Heart segmentation using template method because k-means clustering with K = 2 cannot segment it.

Tests performed using 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 8.35%, sensitivity 63.71%, and spesifity 93.48%. Heart segmentation using template matching system can segment the heart, accuracy 94.33%, sensitivity 64.55%, and spesifity 98.13%.

 

Keywordsimage segmentation, x-ray thorax, k-means clustering, geometric active contour, template matching


Keywords


image segmentation, x-ray thorax, k-means clustering, geometric active contour, template matching

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References

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[2]     Lailyana, E., 2009, Segmentasi Paru-paru pada citra X-ray menggunakan Level Set, Tesis, Jurusan Teknik Elektro Fakultas Teknologi Industri Institut Teknologi Sepuluh Nopember, Surabaya.

 

[3]     Artawijaya, A., 2010, Sekilas Tentang CTR (Cardio Thoracic Ratio), http://catatanradiograf.blogspot.com, 26 Agustus 2010, diakses 3 Januari 2011.

 

[4]     Rafsyam, Y., 2008, Metode Segmentasi Citra USG untuk Mendeteksi Kista, Tesis, Program Studi Teknik Elektro Universitas Gajah Mada, Yogyakarta.

 

[5]     Hariyadi, M. A., 2010, Lung Segmentation at Image X-ray for Detecting Cardio Thorax Ratio Using Max-Tree Filtering and Geometric Active Contour, Journal of Mathematic and Technology, ISSN:2078-0257, No.4, October 2010.

 

[6]     Gonzalez, R.C., dan Woods, R.E., 2008, Digital Image Processing Third Edition, Prentice-Hall, Inc., New Jersey.

 

[7]     Chan, T., Vese, L., 2001, Active Contour Without Edges. IEEE Transaction on Image Processing, Vol.10, No.2, February 2001.



DOI: https://doi.org/10.22146/ijeis.1961

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