Garuda - Garba Rujukan Digital

SEGMENTASI CITRA PANORAMIK GIGI MENGGUNAKAN SIMILARITAS ANTAR GRAY LEVEL BERDASARKAN INDEX OF FUZZINESS Pratamasunu, Gulpi Qorik Oktagalu; Arifin, Agus Zainal; Yuniarti, Anny; Wijaya, Arya Yudhi; Khotimah, Wijayanti Nurul; Navastara, Dini Adni
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 14, No 1, Januari 2016
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v14i1.a513

Metode segmentasi citra berdasarkan teori fuzzy dan similaritas antar gray level mampu mengatasi masalah ambiguitas gray level dan pencahayaan yang tidak merata yang biasa ditemui pada citra medis. Namun, segmentasi dengan penentuan initial seeds-nya berdasarkan jumlah piksel minimum menghasilkan citra yang kurang baik saat diterapkan pada citra dengan kontras yang rendah, seperti yang terdapat pada citra panoramik gigi. Pada penelitian ini diusulkan metode segmentasi citra panoramik gigi dengan penentuan initial seeds berdasarkan index of fuzziness terbesar pada histogram. Histogram dibagi kedalam tiga daerah berdasarkan posisi dari pusat fuzzy region. Kemudian, proses pengukuran similaritas antar gray level yang berada pada fuzzy region dilakukan untuk menemukan threshold yang optimal. Performa metode yang diusulkan diuji menggunakan citra panoramik gigi. Evaluasi performa dilakukan dengan menghitung nilai Misclassification Error antara citra hasil segmentasi dengan citra ground truth. Hasil evaluasi menunjukkan bahwa hasil segmentasi metode yang diusulkan pada citra panoramik gigi memiliki performa yang lebih baik dibandingkan dengan hasil segmentasi dari metode Otsu.

Segmentasi Pembuluh Darah Retina Pada Citra Fundus Menggunakan Gradient Based Adaptive Thresholding Dan Region Growing Sutaji, Deni; Fatichah, Chastine; Navastara, Dini Adni
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 2, No 2 (2016): Juli-Desember
Publisher : Prodi Sistem Informasi - Universitas Pesantren Tinggi Darul Ulum

Â Segmentasi pembuluh darah pada citra fundus retina menjadi hal yang substansial dalam dunia kedokteran, karena dapat digunakan untuk mendeteksi penyakit, seperti: diabetic retinopathy, hypertension, dan cardiovascular. Dokter membutuhkan waktu sekitar dua jam untuk mendeteksi pembuluh darah retina, sehingga diperlukan metode yang dapat membantu screening agar lebih cepat.Penelitian sebelumnya mampu melakukan segmentasi pembuluh darah yang sensitif terhadap variasi ukuran lebar pembuluh darah namun masih terjadi over-segmentasi pada area patologi. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan metode segmentasi pembuluh darah pada citra fundus retina yang dapat mengurangi over-segmentasi pada area patologi menggunakan Gradient Based Adaptive Thresholding dan Region Growing.Metode yang diusulkan terdiri dari 3 tahap, yaitu segmentasi pembuluh darah utama, deteksi area patologi dan segmentasi pembuluh darah tipis. Tahap segmentasi pembuluh darah utama menggunakan high-pass filtering dan tophat reconstruction pada kanal hijau citra yang sudah diperbaiki kontrasnya sehingga lebih jelas perbedaan antara pembuluh darah dan background. Tahap deteksi area patologi menggunakan metode Gradient Based Adaptive Thresholding. Tahap segmentasi pembuluh darah tipis menggunakan Region Growing berdasarkan informasi label pembuluh darah utama dan label area patologi. Hasil segmentasi pembuluh darah utama dan pembuluh darah tipis kemudian digabungkan sehingga menjadi keluaran sistem berupa citra biner pembuluh darah. Berdasarkan hasil uji coba, metode ini mampu melakukan segmentasi pembuluh darah retina dengan baik pada citra fundus DRIVE, yaitu dengan akurasi rata-rata 95.25% dan nilai Area Under Curve (AUC) pada kurva Relative Operating Characteristic (ROC) sebesar 74.28%. Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Kata Kunci: citra fundus retina, gradient based adaptive thresholding, patologi, pembuluh darah retina, region growing, segmentasi.Â Â Segmentation of blood vessels in the retina fundus image becomes substantial in the medical, because it can be used to detect diseases, such as diabetic retinopathy, hypertension, and cardiovascular. Doctor takes about two hours to detect the blood vessels of the retina, so screening methods are needed to make it faster. The previous methods are able to segment the blood vessels that are sensitive to variations in the size of the width of blood vessels, but there is over-segmentation in the area of pathology. Therefore, this study aims to develop a segmentation method of blood vessels in retinal fundus images which can reduce over-segmentation in the area of pathology using Gradient Based Adaptive Thresholding and Region Growing. The proposed method consists of three stages, namely the segmentation of the main blood vessels, detection area of pathology and segmentation thin blood vessels. Main blood vessels segmentation using high-pass filtering and tophat reconstruction on the green channel which adjusted of contras image that results the clearly between object and background. Detection area of pathology using Gradient Based Adaptive thresholding method. Thin blood vessels segmentation using Region Growing based on the information main blood vessel segmentation and detection of pathology area. Output of the main blood vessel segmentation and thin blood vessels are then combined to reconstruct an image of the blood vessels as output system.This method is able to segment the blood vessels in retinal fundus images DRIVE with an accuracy of 95.25% and the value of Area Under Curve (AUC) in the relative operating characteristic curve (ROC) of 74.28%.Keywords: Blood vessel, fundus retina image, gradient based adaptive thresholding, pathology, region growing, segmentation.

KOMBINASI METODE MULTILAYER PERCEPTRON DAN TEORI FUZZY UNTUK KLASIFIKASI DATA MEDIS Navastara, Dini Adni; Safitri, Julia; Purwitasari, Diana
IKRAITH-INFORMATIKA Vol 2 No 2 (2018): IKRAITH INFORMATIKA VOL 2 NO 2 Juli 2018
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (643.502 KB)

Kemajuan teknologi informasi saat ini banyak digunakan untuk membantu komputasi data dalam berbagaipenelitian, salah satunya dalam bidang kesehatan (medis). Dibutuhkan peranan teknologi informasi untukmembantu komputasi dengan melakukan klasifikasi data medis berdasarkan keterangan-keterangan yangmenjelaskan data tersebut. Dalam tahapan klasifikasi terkadang data masih dapat timbul beberapa ketidakpastianyang disebabkan oleh adanya informasi yang kurang tepat, ambiguitas dalam data masukan, tumpang tindih batasbatasantara kelas, dan ketidaktentuan dalam mendefinisikan fitur. Untuk mengatasi permasalahan tersebut,dilakukan implementasi metode Neuro-fuzzy yang menggunakan kombinasi Neural Network dan pendekatan teoriFuzzy Set untuk klasifikasi data medis. Neuro-fuzzy merupakan penggabungan antara sistem Neural Network dansistem fuzzy. Sistem logika fuzzy memiliki kemampuan menangani data pengetahuan dalam persepsi danpenalaran seperti otak manusia tetapi tidak memiliki kemampuan untuk belajar dan beradaptasi. Sedangkan NeuralNetwork memiliki kemampuan untuk belajar dan beradaptasi tetapi tidak memiliki kemampuan penalaran sepertipada sistem logika fuzzy. Salah satu algoritma yang dapat diandalkan dalam klasifikasi data dari domain NeuralNetwork adalah Multilayer Perceptron Backpropagation Network (MLPBPN). Dari hasil uji coba didapatkantingkat akurasi pada dataset Breast Cancer Wisconsin, Mammographic Mass, dan Pima Indians Diabetes masingmasingmencapai 97,512%, 84,666%, dan 81,613%. Selain itu, metode Neuro-Fuzzy dapat meningkatkan akurasirata-rata sebesar 3,536% dari metode ANFIS.

Segmentasi Citra Ikan Tuna Menggunakan Gradient-Barrier Watershed Berbasis Analisis Hierarki Klaster dan Regional Credibility Merging Fadllullah, Arif; Arifin, Agus Zainal; Navastara, Dini Adni
Jurnal Buana Informatika Vol 7, No 3 (2016): Jurnal Buana Informatika Volume 7 Nomor 3 Juli 2016
Publisher : Universitas Atma Jaya Yogyakarta

Abstract. The main issue of object identification in tuna image is the difficulty of extracting the entire contour of tuna physical features, because it is often influenced by uneven illumination and the ambiguity of object edges in tuna image. We propose a novel segmentation method to optimize the determination of tuna region using GBW-AHK and RCM. GBW-AHK is used to optimize the determination of adaptive threshold in order to reduce over-segmented watershed regions. Then, RCM merges the remaining regions based on two merging criteria, thus it produces two main areas of segmentation, the object extraction of tuna and the background. The experimental results on 25 tuna images demonstrate that the proposed method successfully produced an image segmentation with the average value of RAE by 4.77%, ME of 0.63%, MHD of 0.20, and the execution time was 11.61 seconds. Keywords: watershed, gradient-barrier, hierarchical cluster analysis, regional credibility merging, tuna segmentationÂ Abstrak. Kendala utama identifikasi objek tuna pada citra ikan tuna adalah sulitnya mengekstraksi seluruh kontur tubuh ikan, karena seringkali dipengaruhi faktor iluminasi yang tidak merata dan ambiguitas tepi objek pada citra. Penelitian ini mengusulkan metode segmentasi baru yang mengoptimalkan penentuan region objek tuna menggunakan Gradient-Barrier Watershed berbasis Analisis Hierarki Klaster (GBW-AHK) dan Regional Credibility Merging (RCM). Metode GBW-AHK digunakan untuk mengoptimalkan penentuan adaptif threshold untuk mereduksi region watershed yang over-segmentasi. Kemudian RCM melakukan penggabungan region sisa hasil reduksi berdasarkan dua syarat penggabungan hingga dihasilkan dua wilayah utama segmentasi, yakni ekstraksi objek ikan tuna dan background. Hasil eksperimen pada 25 citra ikan tuna membuktikan bahwa metode usulan berhasil melakukan segmentasi dengan nilai rata-rata relative foreground area error (RAE) 4,77%, misclassification error (ME) 0,63%, modified Hausdorff distance (MHD) 0,20, dan waktu eksekusi 11,61 detik. Kata Kunci: watershed, gradient-barrier, analisis hierarki klaster, regional credibility merging, segmentasi tuna

Segmentasi Pembuluh Darah Retina Pada Citra Fundus Menggunakan Gradient Based Adaptive Thresholding Dan Region Growing Sutaji, Deni; Fatichah, Chastine; Navastara, Dini Adni
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 2, No 2 (2016): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v2i2.553

Â Segmentasi pembuluh darah pada citra fundus retina menjadi hal yang substansial dalam dunia kedokteran, karena dapat digunakan untuk mendeteksi penyakit, seperti: diabetic retinopathy, hypertension, dan cardiovascular. Dokter membutuhkan waktu sekitar dua jam untuk mendeteksi pembuluh darah retina, sehingga diperlukan metode yang dapat membantu screening agar lebih cepat.Penelitian sebelumnya mampu melakukan segmentasi pembuluh darah yang sensitif terhadap variasi ukuran lebar pembuluh darah namun masih terjadi over-segmentasi pada area patologi. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan metode segmentasi pembuluh darah pada citra fundus retina yang dapat mengurangi over-segmentasi pada area patologi menggunakan Gradient Based Adaptive Thresholding dan Region Growing.Metode yang diusulkan terdiri dari 3 tahap, yaitu segmentasi pembuluh darah utama, deteksi area patologi dan segmentasi pembuluh darah tipis. Tahap segmentasi pembuluh darah utama menggunakan high-pass filtering dan tophat reconstruction pada kanal hijau citra yang sudah diperbaiki kontrasnya sehingga lebih jelas perbedaan antara pembuluh darah dan background. Tahap deteksi area patologi menggunakan metode Gradient Based Adaptive Thresholding. Tahap segmentasi pembuluh darah tipis menggunakan Region Growing berdasarkan informasi label pembuluh darah utama dan label area patologi. Hasil segmentasi pembuluh darah utama dan pembuluh darah tipis kemudian digabungkan sehingga menjadi keluaran sistem berupa citra biner pembuluh darah. Berdasarkan hasil uji coba, metode ini mampu melakukan segmentasi pembuluh darah retina dengan baik pada citra fundus DRIVE, yaitu dengan akurasi rata-rata 95.25% dan nilai Area Under Curve (AUC) pada kurva Relative Operating Characteristic (ROC) sebesar 74.28%. Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Kata Kunci: citra fundus retina, gradient based adaptive thresholding, patologi, pembuluh darah retina, region growing, segmentasi.Â Â Segmentation of blood vessels in the retina fundus image becomes substantial in the medical, because it can be used to detect diseases, such as diabetic retinopathy, hypertension, and cardiovascular. Doctor takes about two hours to detect the blood vessels of the retina, so screening methods are needed to make it faster. The previous methods are able to segment the blood vessels that are sensitive to variations in the size of the width of blood vessels, but there is over-segmentation in the area of pathology. Therefore, this study aims to develop a segmentation method of blood vessels in retinal fundus images which can reduce over-segmentation in the area of pathology using Gradient Based Adaptive Thresholding and Region Growing. The proposed method consists of three stages, namely the segmentation of the main blood vessels, detection area of pathology and segmentation thin blood vessels. Main blood vessels segmentation using high-pass filtering and tophat reconstruction on the green channel which adjusted of contras image that results the clearly between object and background. Detection area of pathology using Gradient Based Adaptive thresholding method. Thin blood vessels segmentation using Region Growing based on the information main blood vessel segmentation and detection of pathology area. Output of the main blood vessel segmentation and thin blood vessels are then combined to reconstruct an image of the blood vessels as output system.This method is able to segment the blood vessels in retinal fundus images DRIVE with an accuracy of 95.25% and the value of Area Under Curve (AUC) in the relative operating characteristic curve (ROC) of 74.28%.Keywords: Blood vessel, fundus retina image, gradient based adaptive thresholding, pathology, region growing, segmentation.

Fuzzy Region Merging using Fuzzy Similarity Measurement on Image Segmentation Wawan Gunawan; Agus Zainal Arifin; Rarasmaya Indraswari; Dini Adni Navastara
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 6: December 2017
Publisher : Institute of Advanced Engineering and Science

Some image’s regions have unbalance information, such as blurred contour, shade, and uneven brightness. Those regions are called as ambiguous regions. Ambiguous region cause problem during region merging process in interactive image segmentation because that region has double information, both as object and background. We proposed a new region merging strategy using fuzzy similarity measurement for image segmentation. The proposed method has four steps; the first step is initial segmentation using mean-shift algorithm. The second step is giving markers manually to indicate the object and background region. The third step is determining the fuzzy region or ambiguous region in the images. The last step is fuzzy region merging using fuzzy similarity measurement. The experimental results demonstrated that the proposed method is able to segment natural images and dental panoramic images successfully with the average value of misclassification error (ME) 1.96% and 5.47%, respectively.

Automatic image slice marking propagation on segmentation of dental CBCT Agus Zainal Arifin; Evan Tanuwijaya; Baskoro Nugroho; Arif Mudi Priyatno; Rarasmaya Indraswari; Eha Renwi Astuti; Dini Adni Navastara
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 6: December 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i6.13220

Cone Beam Computed Tomography (CBCT) is a radiographic technique that has been commonly used to help doctors provide more detailed information for further examination. Teeth segmentation on CBCT image has many challenges such as low contrast, blurred teeth boundary and irregular contour of the teeth. In addition, because the CBCT produces a lot of slices, in which the neighboring slices have related information, the semi-automatic image segmentation method, that needs manual marking from the user, becomes exhaustive and inefficient. In this research, we propose an automatic image slice marking propagation on segmentation of dental CBCT. The segmentation result of the first slice will be propagated as the marker for the segmentation of the next slices. The experimental results show that the proposed method is successful in segmenting the teeth on CBCT images with the value of Misclassification Error (ME) and Relative Foreground Area Error (RAE) of 0.112 and 0.478, respectively.

Region Based Image Retrieval Using Ratio of Proportional Overlapping Object Agus Zainal Arifin; Rizka Wakhidatus Sholikah; Dimas Fanny H. P.; Dini Adni Navastara
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 4: December 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i4.4289

In Region Based Image Retrieval (RBIR), determination of the relevant block in query region is based on the percentage of image objects that overlap with each sub-blocks. But in some images, the size of relevant objects are small. It may cause the object to be ignored in determining the relevant sub-blocks. Therefore, in this study we proposed a system of RBIR based on the percentage of proportional objects that overlap with sub-blocks. Each sub-blocks is selected as a query region. The color and texture features of the query region will be extracted by using HSV histogram and Local Binary Pattern (LBP), respectively. We also used shape as global feature by applying invariant moment as descriptor. Experimental results show that the proposed method has average precision with 74%.

Rancang Bangun dan Implementasi Aplikasi Electronic Instrument Database System Bagus Jati Santoso; F.X. Arunanto; Siti Rochimah; Dini Adni Navastara
Jurnal Ilmiah FIFO Vol 11, No 1 (2019)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2019.v10i1.001

For all real-time industrial processes in a company, the entire system must be well-controlled to ensure an effective, efficient and reliable operations. Therefore, several electronic instruments are needed to measure physical quantities, such as temperature, pressure and others. As in one of the largest liquefaction natural gas processing company in Indonesia which involves highly extensive production processes, the existence of electronic instruments is unquestionably essential to ensure the progression of the production process while avoiding risks.To increase the speed, accuracy and quality of data over the electrical instrument in the company, it is necessary to build an electronic instrument data processing application system, called the Electronic Instrument Database System (ELIDA), so that it can provide convenience for the users in managing and monitoring the data of electronic instrument. The built ELIDA system will be integrated with several existing Oracle-based systems, including EBS-SCM for supply chain management, EBS-EAM for Enterprise Asset Management, and Oracle HRMS for human resource management so that the data consistency and the operational smoothness of the company can be ensured.Keywords : electronic instrument, database, application AbstrakUntuk semua proses yang berjalan real-time di perusahaan, keseluruhan sistem harus dikendalikan secara baik untuk menjamin operasional yang efektif, efisien, dan handal. Karenanya, beberapa perangkat instrumen elektronik diperlukan untuk mengukur kuantitas fisikal seperti temperatur, tekanan, dan lainnya. Tak terkecuali di salah satu perusahaan pengolahan gas alam cair terbesar di Indonesia dimana melibatkan proses produksi yang sangat panjang, keberadaan perangkat instrumen elektronik mutlak diperlukan untuk menjamin jalannya proses produksi sekaligus menghindarkan terjadinya resiko.Dalam upaya peningkatan kecepatan, keakurasian, dan kualitas data-data instrumen di perusahaan tersebut, perlu dibangun sistem aplikasi pengolahan data instrument elektronik, atau disebut Electronic Instrument Database System (ELIDA), sehingga dapat memberikan kemudahan bagi pengguna data-data instrumen elektronik untuk melakukan pengelolaan dan pemantauan. Sistem ELIDA yang dibangun akan menggunakan framework Spring Hibernate dan terintegrasi dengan beberapa sistem berbasis Oracle yang sudah berjalan sebelumnya, diantaranya EBS-SCM untuk manajemen rantai pasok, EBS-EAM untuk manajamen aset perlengkapan, dan Oracle HRMS untuk manajemen sumber daya manusia sehingga konsistensi data dan kelancaran operasional perusahaan dapat terjamin.Kata Kunci: instrumen elektronik, basis data, aplikasi

MULTI-CLASS REGION MERGING FOR INTERACTIVE IMAGE SEGMENTATION USING HIERARCHICAL CLUSTERING ANALYSIS Khairiyyah Nur Aisyah; Syadza Anggraini; Novi Nur Putriwijaya; Agus Zainal Arifin; Rarasmaya Indraswari; Dini Adni Navastara
Jurnal Ilmu Komputer dan Informasi Vol 12, No 2 (2019): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

In interactive image segmentation, distance calculation between regions and sequence of region merging is being an important thing that needs to be considered to obtain accurate segmentation results. Region merging without regard to label in Hierarchical Clustering Analysis causes the possibility of two different labels merged into a cluster and resulting errors in segmentation. This study proposes a new multi-class region merging strategy for interactive image segmentation using the Hierarchical Clustering Analysis. Marking is given to regions that are considered as objects and background, which are then referred as classes. A different label for each class is given to prevent any classes with different label merged into a cluster. Based on experiment, the mean value of ME and RAE for the results of segmentation using the proposed method are 0.035 and 0.083, respectively. Experimental results show that giving the label on each class is effectively used in multi-class region merging.

Title

Found 34 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 34 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 34 Documents
Search