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All Journal Majalah Forum Teknik UGM
Maesadji Tjokronagoro
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Engineering

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Ektraksi Ciri Citra Termogram Payudara Berbasis Dimensi Fraktal Oky Dwi Nurhayati; Thomas Sri Widodo; Adhi Susanto; Maesadji Tjokronagoro
Forum Teknik Vol 33, No 2 (2010)
Publisher : Faculty of Engineering, Universitas Gadjah Mada

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AbstractThe primary purpose of infrared thermography is the locating of thermal differences and anomalies.  Infrared thermography can  detect  numerous  conditions  in  which  an  anomaly is characterized by an increase or decrease in surface temperature. In this research, we specifically applied calculation of fractal dimension method to a total of20 thermograms of normal breasts as well as of those in advanced breast cancer. In addition standard  image  pre-processing  were  also  used  to  enhance  the  detection capabilitity.  Severalmethods in image processing which are pre-processing with canny edge detection, thresholding, calculation of fractal dimension use box-counting and Hausdorff dimension.The results of this research are shown that Hausdorff dimension in the normal thermogramshave range value 0,4 – 0,95 smaller than the advanced thermograms which have value more than  1,26.Finally  this  results  show  that  the  difference  of  fractal dimension  can  be  used  todistinguish between normal and advanced thermograms.Keywords: canny edge detection, thresholding, fractal dimension, box-counting, Hausdorff
Ekstraksi Ciri dan Identifikasi Citra Otak MRI Berbasis Eigenbrain Image Indah Soesanti; Adhi Susanto; Thomas Sri Widodo; Maesadji Tjokronagoro
Forum Teknik Vol 34, No 1 (2011)
Publisher : Faculty of Engineering, Universitas Gadjah Mada

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In this research, we exctract and identify MRI brain images based on eigbrain image.  MRI barain images are used to be input for feature exctraction and identitication. Feature exctraction is done by using the eigbrain image. For all reference image, we find image mean and eigbrain image, and the results are stored. If there is test image, we will find the nearest distance of eigenbrain between test image and reference images. The feature extraction is used to identify the image is whether the normal brain image or the brain image with tumor.The results show that the method successfully classifies MRI images into tree clusters: normal,  glioma, and metastase. The input test images can be identified accurately 100% for image  sizes from 256 x 256 pixels to 64 x 64 pixels.Keywords : feature extraction, image identification, MRI medical image, eigenbrain image.
Co-Authors Adhi Susanto Indah Soesanti Oky Dwi Nurhayati Sulistyo, Henry Surtono, Arif Thomas Sri Widodo Thomas Sri Widodo Thomas Sri Widodo