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All Journal BERKALA FISIKA Journal of Soft Computing Exploration
Josepa ND Simanjuntak
Program Sudi Magister Ilmu Fisika, Universitas Diponegoro Semarang
Computer Science & IT Physics

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Studi Analisis Echo Train Length dalam K-Space Serta Pengaruhnya Terhadap Kualitas Citra Pembobotan T2 FSE pada MRI 1.5 T Simanjuntak, Josepa ND; Nur, Muhammad; Hidayanto, Eko
BERKALA FISIKA 2014: Berkala Fisika Vol. 17 No. 1 Tahun 2014
Publisher : BERKALA FISIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (122.709 KB)

Abstract

The influence of echo train length (ETL) toward image quality of sagital lumbal on MRI using T2-weighted FSE was analyzed on 20 images from four patients. Quantitative data analysis was performed using ROI two methods: image processing method on MRI’s computer and using matlab software, then conducted the statistical test. The result of SNR from these two methods obtained the highest value of SNR at CSF tissue and the highest value of CNR at CSF-corpus tissue and CSF-medulla spinalis with ETL=16. From statistical test of SNR result directly from MRI’s computer image was obtained the significant value at corpus tissue = 0.603, CSF = 0.082, and Fat = 0.213 (P > 0,05), discus = 0.022, Medulla Spinalis (MS) = 0.010 (P < 0.05), and for CNR result of CSF-corpus tissue has significant value = 0,023, corpus-MS = 0.011 (P < 0.05). By using matlab programming method obtained significant SNR result at corpus tissue = 0.000, CSF = 0,000, Fat = 0,000, discus = 0,000, Medulla spinalis = 0,000 (P < 0,05), and for the CNR result of Dicus-corpus tissue has significant value = 0.044, Dicus-MS= 0.045 (P , 0.05). These results pointed out that ETL and T2 weighted influence the Image quality of MRI, which are the image contrast at FSE and software ability of matlab to analyze the image quality of MRI. SNR and contrast are important aspect in the process of image optimization, the higher SNR value provide the better image in giving diagnose information. Keywords : MRI, Echo train length (ETL), K-space, Fast spin echo, Contras to noise ratio, Signal to noise ratio.
Identification of lung cancer using gray level co-occurrence matrix (GLCM) and artificial neural network with backpropagation algorithm Fauziah, Haniifah Hana; Ningtias, Diah Rahayu; Wahyudi, Bayu; Simanjuntak, Josepa ND
Journal of Soft Computing Exploration Vol. 6 No. 1 (2025): March 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i1.543

Abstract

Air pollution is a problem that occurs in various countries, including Indonesia. One of the consequences of poor air quality due to air pollution is health problems in the lungs, one of which is lung cancer. According to WHO data, lung cancer caused 1.80 million deaths in 2020. This is due to limited services to identify lung cancer early, resulting in delays in treatment. This study aims to identify lung cancer using CT-Scan image processing. The identification method uses a Backpropagation Artificial Neural Network (ANN BP) with Gray Level Co-occurrence Matrix (GLCM) feature extraction. Preprocessing is carried out to improve image quality by removing noise using a median filter. Segmentation of preprocessing results using Otsu threshold. Texture features from segmentation can be calculated from the resulting GLCM, such as Angular Second Moment (ASM)/energy, contrast, correlation, Inverse Different Moment (IDM)/homogeneity, and entropy. These values ​​are obtained from angles of 0°, 45°, 90°, and 135°, and a distance between pixels of 2 pixels. Identification using ANN with Backpropagation algorithm. This study used images of normal lungs and lung cancer with 160 training data images and 40 test data images. The best test results were obtained with the best accuracy level of 92.5%.
Co-Authors Diah Rahayu Ningtias, Diah Rahayu Eko Hidayanto Fauziah, Haniifah Hana Muhammad Nur Nurwijayanti