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All Journal Jurnal Radiologi Dentomaksilofasial Indonesia (JRDI)
Bambang Hidayat
Department of Electrical Engineering, School of Electrical Engineering, Telkom University, Bandung, Indonesia 40257
Dentistry Energy Health Professions Medicine & Pharmacology Physics

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Image processing of periapical radiograph on granuloma detection by analysis method based on Android Merry Annisa Damayanti; Suhardjo Sitam; Bambang Hidayat; Ivhatry Rizky Octavia Putri Susilo
Jurnal Radiologi Dentomaksilofasial Indonesia (JRDI) Vol 5 No 1 (2021): Jurnal Radiologi Dentomaksilofasial Indonesia (JRDI)
Publisher : Ikatan Radiologi Kedokteran Gigi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32793/jrdi.v5i1.672

Abstract

Objectives: The study assesses periapical radiograph image with various android based analysis method to detect granuloma. Materials and Methods: The study uses survey descriptive cross sectional by using questionnaire. The questionnaire is distributed to 70 random respondents. The methods of the android application used are BLOB (Binary Large Object), DCT and LDA (Discrete Cosine Transform and Linier Discriminant Analysis), DWT and PCA (Discrete Wavelet Transform & Principal Component Analysis), and multiwavelet transformation. The questionnaire assessment included accuracy, effectiveness, attractiveness, innovativeness of the android application. Results: Android application with BLOB has effectivity and accuracy of 62,5%, attractiveness and innovativeness of 75%. Android application with DCT and LDA has effectivity and accuracy of 50 %, attractiveness of 70% and innovativeness of 80%. Android application with DWT and PCA has effectivity of 50%, accuracy of 60%, attractiveness of 66,66% and innovativeness of 80%. Android application with multiwavelet transformation has effectivity and accuracy of 50%, attractiveness of 55% and innovativeness of 73%. Conclusion: Based on assessment, the four methods used to detect granuloma are effective and applicative with android-based application. Android-based Application can detect granuloma with approximately more than 70% successful rate. These methods ease the practitioner to interpret the granuloma image.
Co-Authors Ivhatry Rizky Octavia Putri Susilo Merry Annisa Suhardjo Sitam