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"Dalam penelitian Computer Aided Diagnose (CAD) Radiografi Paru pasien dewasa dengan metode Fuzzy C Means (FCM), telah dilakukan dalam keadaan tahap awal. Penelitian ini bertujuan untuk mengetahui apakah metode clustering FCM dapat digunakan untuk membuat perangkat penolong untuk melihat abnormalitas pada paru-paru dari 200 data citra Radiografi sinar-X. Pembuatan perangkat dilakukan dengan menggunakan GUI pada Matlab. Perancangan di bagi menjadi dua metode menggunakan metode FCM otomatis dan manual kemudian untuk mengetahui perbedaan nilai piksel digunakan metode ambang rata-rata. Kedua metode ini berdasarkan intensitas derajat keabuan 0-256. Metode FCM digunakan untuk melihat visualisasi abnormalitas secara cepat dan mengetahui garis besar posisi yang abnormal. Kemudian diteruskan dengan segmen kotak dari metode ambang rata-rata untuk mengetahui perbedaan nilai pixel citra abnormalitas dan yang normal. Hasil penelitian nenujukkan bahwa, Kinerja Metode FCM Akurasi 57,7%, sensitifitas 50,0%, spesifikasi 89,5% , Overal Error 42,3% dan Presisi 95,1%. Sedangkan metode Segmen per kotak Akurasi 56,7%, sensitifitas 51,7%, spesifikasi 88,5% , Overal Error 43,3% dan Presisi 96,7%. berdasarkan penelitian dapat disimpulkan bahwa Metode FCM dalam paru hanya bisa menunjukkan visual secara cepat dan garis besar namun tidak memberikan akurasi yang cukup memuaskan, hal ini di karenakan data input yang random tidak dapat dijadikan patokan untuk ukuran keberhasilan.

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In the study Computer Aided Diagnose (CAD) Lung Radiography adult patients with Fuzzy C Means (FCM), has been carried out in a state of infancy. This study aims to determine whether the FCM clustering method can be used to make the device helper to see abnormalities in the lungs of 200 image data of X-ray radiography. Making the device is done by using the GUI in Matlab.

The design is divided into two methods using automated and manual methods FCM then to determine differences in pixel value threshold method is used on average. Both methods are based on the intensity of gray 0-256 degrees. FCM method is used for visualizing abnormalities quickly see and know the outline of an abnormal position. Then forwarded to the segment boxes of the average threshold method to determine differences in pixel values abnormalities and normal image.

That research results, performance FCM method Accuracy 57.7%, 50.0% sensitivity, 89.5% specification, Overal Error 42.3% and 95.1% precision. While the method of segment per box Accuracy 56.7%, 51.7% sensitivity, 88.5% specification, Overal Error 43.3% and 96.7% precision. based study concluded that the method of FCM in the lungs can only show rapid visual and outline but does not give a satisfactory accuracy, it is in because random input data can not be used as a benchmark to measure success."

"[ABSTRAKIntensitas keabuan yang sangat dekat memungkinkan terjadinya kesalahan dalammenginterpretasikan citra hasil Computed Radiography (CR). Maka diperlukanalgoritma yang dapat mempermudah tim medis mendiagnosa kondisi pasienkhususnya bagian paru. Penelitian ini menggunakan tingkat keabuan /intensitascitra sebagai dasar clustering dan segmentasi Region of Interest (ROI ) yang akandilakukan dengan sistem komputerisasi. Sehingga hasil pembacaan lebih akuratdibanding secara manual. Data sampel berupa 100 citra hasil CR pasien parudewasa Rumah Sakit Pusat Pertamina yaitu 50 citra norma sebagai citra acuan dan50 citra uji (normal dan abnormal). Pada clustering diuji coba dengan jumlahcluster (k) bervariasi yaitu 3, 4, .., 10. Citra hasil clustering yang terbaikditunjukkan pada k = 8 karena dapat memvisualisasikan batas warna dengan lebihjelas dibanding dengan k yang lain. Pada segmentasi ROI, citra paru dibagimenjadi 33 daerah sesuai posisi anatomi paru yang terdiri dari 6 daerah apex, 11daerah hilum dan 16 daerah peripheral. Selanjutnya, masing-masing daerahpembagian diukur intensitasnya. Intensitas citra acuan dijadikan dasar untukmenentukan abnormalitas citra uji, intensitas citra uji yang lebih tinggi dariintensitas citra normal dikategorikan sebagai citra abnormal. Akurasi sistem padapenelitian ini adalah 66%.

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ABSTRACTGray intensity is very close to allow for errors in interpreting the ComputedRadiography (CR) image. It would require an algorithm that can facilitate medicalteam to diagnose the patient's condition especially the lungs. Clustering k-meansclustering and segmentation Region of Interest (ROI) will be done by acomputerized system based on the image gray level / intensity. 100 CR imageused as the sample data from Rumah Sakit Pusat Pertamina, 50 image asreferences images and 50 images as tested image. On clustering tested by thenumber of clusters (k) varies the 3, 4, .., 10. The clustering of the best imageresults are shown in k = 8 because it can visualize the color boundaries moreclearly than the other k. At ROI segmentation, lung image is divided into 33regions corresponding anatomical position lung consist of 6 regional apex, hilumarea 11 and 16 peripheral areas. Furthermore, each regional division of themeasured intensity. The intensity of the reference image used as the basis fordetermining abnormality test images, test image intensity higher than normalimage intensity categorized as abnormal image. The system accuracy in this studywas 66%., Gray intensity is very close to allow for errors in interpreting the ComputedRadiography (CR) image. It would require an algorithm that can facilitate medicalteam to diagnose the patient's condition especially the lungs. Clustering k-meansclustering and segmentation Region of Interest (ROI) will be done by acomputerized system based on the image gray level / intensity. 100 CR imageused as the sample data from Rumah Sakit Pusat Pertamina, 50 image asreferences images and 50 images as tested image. On clustering tested by thenumber of clusters (k) varies the 3, 4, .., 10. The clustering of the best imageresults are shown in k = 8 because it can visualize the color boundaries moreclearly than the other k. At ROI segmentation, lung image is divided into 33regions corresponding anatomical position lung consist of 6 regional apex, hilumarea 11 and 16 peripheral areas. Furthermore, each regional division of themeasured intensity. The intensity of the reference image used as the basis fordetermining abnormality test images, test image intensity higher than normalimage intensity categorized as abnormal image. The system accuracy in this studywas 66%.]"

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