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All Journal Jurnal Sarjana Teknik Informatika SISFOTENIKA
Roshandri, Wien Fitrian
STIK Muhammadiyah Pontianak
Computer Science & IT

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Diabetic Wound Segmentation Using Masking Contour Image Processing Wien Fitrian Roshandri; Ema Utami; Agung Budi Prasetio
SISFOTENIKA Vol 11, No 2 (2021): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/jst.v11i2.1114

Abstract

Measuring the wound area in diabetics is still using a manual way with a wound ruler. Whereas the ruler affixed to the wound will become a contaminated agent that can transmit the infection to other recipients. Digital measurement methods are needed to solve the problem. However, clarifying the boundaries between the wound and the skin requires carefulness and high accuracy. For this reason, it has needed an imaging method that can do segmentation between the wound and the skin boundary for diabetic patients based on digital, called digital planimetry. This study uses a masking contour image processing algorithm from the Hue, Saturation, Value (HSV), Then doing iteration five times and gamma filter. So the result of segmentation is formed. This study concludes that the segmentation with this method has not been able to perform the segment properly, and it requires more masking values, but the results of the 5th iteration got a minor error, which is 0.002%. The digital imaging carried out in this study could be developed to be a digital-based diabetic patient wound measurement tool.
Segmentasi Luka Diabetes Menggunakan Algoritma Contour Image Processing Wien Fitrian Roshandri; Ema Utami; Agung Budi Prasetio
Jurnal Sarjana Teknik Informatika Vol 9, No 2 (2021): Juni
Publisher : Teknik Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v9i2.20226

Abstract

Pengukuran luas luka pada penderita diabetes masih menggunakan cara manual dengan penggaris luka. Sedangkan penggaris yang ditempelkan keluka akan menjadi contaminated agent yang dapat menularkan infeksi pada penderita lain. Metode pengukuran digital diperlukan agar masalah tersebut bisa terselesaikan. Tetapi untuk memperjelas batas antara luka dan kulit diperlukan ketelitian dan akurasi yang tinggi. Untuk itu diperlukan metode pencitraan yang dapat melakukan segmentasi antara batas luka dan kulit paada pasien diabetes berbasis digital yang dinamakan digital planimetry. Penelitian ini menggunakan algoritma contour image processing dari nilai hue, saturation, value (HSV).  Kemudian melakukan iterasi sebanyak 5 kali dan filter gamma. Sehingga mendapatkan hasil segmentasi luka. Kesimpulan akhir dari penelitian ini adalah segementasi dengan metode ini belum dapat melakukan segementasi luka dengan baik dan diperlukan tambahan nilai masking yang lebih luas, akan tetapi hasil iterasi ke 5 mendapatkan error terkecil yaitu 0.002%. Pencitraan digital yang dilakukan dalam penelitian ini dapat dikembangkan untuk menjadi alat ukur luas luka pasien diabetes berbasis digital.
Diabetes Wound Perimeter Analysis Using Pixel Per Metric wien Fitrian Roshandri; Ema Utami
SISFOTENIKA Vol 12, No 2 (2022): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/jst.v12i2.1255

Abstract

The study was conducted to test the accuracy of pixel permetric (PPM)-based diabetic wound perimeter using canny edge, gaussian filter, streamlit, OpenCV, Python and aruco marker. K-Means is used to detect, classify and segment three types of wounds namely granulation, necrotics and slough. Images of wounds are taken directly on the patient so the dataset is primary data. Accuracy tests are carried out by comparing manual measurements against digital calculations. Manual measurements use mica as a sketch. The thread is used to shade the mica sketch and the length of the thread will be measured using a ruler. Aruco markers are used as a reference to the length of the object. The results of the study from 7 sample data received an average accuracy error of at least 0.49% and a maximum of 5.75%. This is influenced by various factors including validation of manual measuring results that are still less thorough, sharpness of the image, and calibration of the camera. Of the three types of tissue wounds, granulation is the most identifiable type, followed by slough, and the most difficult to identify is necrosis. The study concludes that the results of the accuracy-test have obtained a value that is in accordance with the problem limit, namely accuracy above 90%, with the independent T-test value homogeneous test is t_hitung< t_tabel equivalent to 0.005535 < 2.228 with a deviation of ꭤ = 0.05 so that it is concluded that there is no significant difference in the two-variable values of manual measurement to digital planimetry measurements. Further research can then test accuracy with artificial intelligence deep learning methods with sample datasets such as uNet, SegNet, and other methods 
Co-Authors Agung Budi Prasetio Ema Utami