EVALUASI CIRI CITRA TERMOGRAFI DENGAN METODE WAVELET UNTUK KANKER PAYUDARA

• Main Authors: , Afriliana Kusumadewi, , Prof. Dr. Ir. Th. Sri Widodo, DEA
• Format: Theses and Dissertations NonPeerReviewed
• Published: [Yogyakarta] : Universitas Gadjah Mada 2011
• Subjects: ETD
• Online Access: https://repository.ugm.ac.id/90280/; http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=53156
• Institution: Universitas Gadjah Mada

Image feature extraction is a fundamental part of image analysis. The main concern of image feature extraction is can differentiate an object with other object by paying attention computing complexity to get a feature. Feature extraction in this research is done at image result of thermography for breast cancer. Normal breast cancer thermogram and breast cancer thermogram have texture which depend on scale, so required multi scale analysis to do feature extraction. Hence selected wavelet transform method because wavelet transform is appropriate transform for multi resolution analysis. The excellence wavelet transform for multi resolution analysis are orthogonal, spasial, and good frequency localization, and also ability to form multi resolution decomposition. Wavelet method are used Daubechies2, Daubechies9, Coiflet1, and Symlet2. Image feature of wavelet transform result can be obtained by counting energy which consist in each subband image, that is subband approximation, subband horizontal detail, subband vertical detail, and subband diagonal detail. This research use data 44 normal breast thermogram image, 19 earlier breast cancer thermogram image, and 26 breast cancer stage of disease thermogram image. Pre processing of image done by altering thermogram color image become thermogram grayscale image, continued with determination of ROI and cropping method. The image result of cropping is extracted using 6 level wavelet decomposition. The highest energy at subband detail will become feature for each thermogram image. The thermogram image feature result of examination get from detail subband. The biggest energy subband there are at horizontal subband detail and vertical subband detail. Based on value of energy subband detail the best accurate diagnose feature extraction used wavelet Db2 and Sym2 decomposition at 6 level decomposition, while the ugly result of feature extraction used wavelet Db9 decomposition.