Image Smoothing Menggunakan Mean Filtering, Median Filtering, Modus Filtering Dan Gaussian Filtering

Yuwono, Bambang Darmo

doi:10.31315/telematika.v7i1.416

Cited by 18 publications

(14 citation statements)

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“…Nilai SNR terbaik merupakan nilai terbesar (Yuwono, 2010) karena nilai SNR berasal dari nilai kualitas citra filter dibandingkan dengan nilai kualitas citra aslinya. Nilai noise yang tinggi akan menimbulkan artefak yang dapat mengganggu resolusi kontras citra CT-scan yang akhirnya akan mempengaruhi hasil diagnosisi (Athira, 2012).…”

Section: Pembahasanunclassified

The Reduction of Metal Artifacts Using Band Pass Median Filter on Head Ct Scan

et al. 2022

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Background: One of the artifacts found on the CT scan is a metal artifact. Metal artifacts are caused by metal objects present in patients’ bodies. The file of metal artifacts can cover the organ that will be evaluated, and it can be inferred with the pixel value (CT number) assessment of the tissue around the metal. Purpose: To determine the effect of the band pass median filter and find the optimal filter to reduce metal artifacts on the head CT scan. Method: A total of 43 samples of patients’s files from head CT-scan without any contrast were reconstructed using four band pass median filters and obtained R1, R2, R5, and R10 filters. Two radiology specialists were assessed for the reduction of metal artifacts using the ImageJ application. Result: Four variations of the filter affected the reduction of metal artifacts because the band pass median filter maintained a point that was close to its neighboring points and points that were different from its neighboring points by replacing the value of the pixel with the median value of the grey level of neighboring pixels. The optimal filter recommendation is the R1 filter because it has the largest SNR value (16.9773) and the smallest RMSE value (8.57501) so that the result of the image is more informative and has a diagnostic value. Conclusion: The four filter variations were affected by reducing metal artifacts. Images with substantial SNR and fractional RMSE values produced an image that was more informative and still had diagnostic value.

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Section: Pembahasanunclassified

The Reduction of Metal Artifacts Using Band Pass Median Filter on Head Ct Scan

et al. 2022

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“…Jika nilai keypoint kurang dari threshold maka keypoint akan dieliminasi. Untuk menghilangkan keypoint yang berada pada sekitar garis tepi maka digunakan persamaan matriks Hessian 2x2 [2].…”

Section: Menentukan Keypointunclassified

Deteksi Citra Granuloma Melalui Radiograf Periapikal Dengan Metode Scale Invariant Feature Transform Dan Klasifikasi K-Nearest Neighbor

2018

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Radiograf periapikal merupakan komponen yang menghasilkan gambar radiografi dari gigi secara rinci dan jaringan apeks sekitarnya. Gambaran radiografi sangat membantu dokter gigi menegakkan diagnosis dan rencana perawatan kasus gigi impaksi. Dokter gigi mendiagnosa citra perapikal radiograf menggunakan mata namun karena keterbatasan indra penglihatan manusia bisa menyebabkan interpretasi masing-masing dokter gigi berbeda. Pada penelitian ini dibuatlah metode pengolahan citra yang dapat mendeteksi granuloma dari citra periapikal radiograf. Keluaran sistem dapat memberikan hasil yang dapat membantu dokter gigi dalam membuat keputusan dan meningkatkan diagnosis terhadap radiografi periapikal. Pada penelitian ini pembuatan sistem dilakukan dalam beberapa tahap yaitu pre-processing,ektraksi ciri dan klasifikasi. Metode yang akan digunakan dalam penelitian ini adalah Scale Invariant Feature Transformation (SIFT) sebagai metode ekstrasi ciri. SIFT adalah algoritma untuk mendeteksi dan menjelaskan fitur lokal pada citra. Proses klasifikasi menggunakan metode K-Nearest Neighbor (K-NN). K-NN adalah metode untuk mengklasifikasi obyek berdasarkan contoh latih terdekat. Hasil dari sistem ini adalah mampu untuk mengidentifikasi penyakit granuloma dengan akurasi 85,84% dengan waktu komputasi rata-rata 4,04 detik.Katakunci: granuloma, radiografi periapikal, Scale Invariant Feature Transformation (SIFT), K-Nearest Neighbor (K-NN).

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“…Noise tidak hanya merusak sinyal analog tetapi juga merusak sinyal digital. [5] Gambar 7. (a) Pulsa digital asli, (b) Pulsa digital karena pengaruh noise…”

Section: Noiseunclassified

Filtering Sinyal Menggunakan Band Pass Filter

Havis¹,

Fitria²

2018

J. Sifo Mikrosk.

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In our daily lives we find many filters, filters from the word itself are filters. Filters vary, there are air filters to filter dirty air to be clean, filters / filters for coffee and tea to filter coffee or tea pulp, and so on. All of them try to filter something to get what we want. The filter here is a frequency filter, from which the filter will filter the frequency. The existing frequency we filter so get the frequency as we want. In this research, signal filtering will be carried out using Band Pass. The band-pass filter will pass signals with frequencies in a certain range and reduce the signal with other frequencies. The sound sample used is the sound of a cat with just one sample. In this sample signal screening is carried out with 5 different frequencies, namely at frequencies of 8000, 16000, 32000, 48000 and 96000. Data retrieval and processing of voice signals are carried out with the help of audacity and Matlab R2010b software. Sound data in the .wav format is filtered to remove noise and filter using a band pass filter. Sounds issued that are received are destructive which eliminate the sound information that is carried. This of course requires that the sound quality received is not good, so it requires sound signal processing to eliminate noise. Then the voice data is processed by voice signal using the FFT (Fast Fourier Transform) method. The results of this study are the output of sound signals that are clean from noise.

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Image Smoothing Menggunakan Mean Filtering, Median Filtering, Modus Filtering Dan Gaussian Filtering

Cited by 18 publications

References 0 publications

The Reduction of Metal Artifacts Using Band Pass Median Filter on Head Ct Scan

The Reduction of Metal Artifacts Using Band Pass Median Filter on Head Ct Scan

Deteksi Citra Granuloma Melalui Radiograf Periapikal Dengan Metode Scale Invariant Feature Transform Dan Klasifikasi K-Nearest Neighbor

Filtering Sinyal Menggunakan Band Pass Filter

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