Klasifikasi Nomsupervised Citra Thermal Kanker Payudara Berbasis Fuzzy C-MEANS - Octa Herlina; Thomas Sri Widada; Indah Susanti (Jurnal Nasional Teknik Elektro dan Teknologi Informasi, 2012)
Breast cancer was a disease with the condition of the breast tissue became abnormal due to the development of cancer cells in the breast area. One method of breast cancer nondestructive detection was through shooting the indicated breast cancer by using an infrared camera.The emission variations of infrared radiation on the image captured showed the level of cancer. The results of infrared camera imaging was called as thermograph image processed in computing for the classification of cancer in breast areas according to the characteristics of each image. The image feature extraction was obtained through the calculation of the fractal dimension of the image by using the box counting algorithm. Image classification process was done by using the Fuzzy C Means algorithm to determine the level of the breast cancer size based on the T component of the TNM system, namely T0, T1, T2 and T3 to the 22 image data to obtain the value of the parameter cluster centers in Fuzzy C Means.The results of test showed that the feature extraction of breast thermography image using box counting fractal method gave the different value between normal breast and inflammatory cancer breast tissues. Normal breast tissue (T0) has fractal dimension mean less than T1, there was about 1.161525 with deviation standard value was about 0.593625. Breast with tumor T1 has fractal dimension mean less than T2, there was about 1.45455 with deviation standard value was about 0.4645. Breast with tumor T2 had fractal dimension mean less than T3, there was about 1.6596 with deviation standard value was about 0.2925,and breast with tumor T3 has fractal dimension mean about 1.81294 with deviation standard value was about 0.20199. The classification using Fuzzy C Means in 32x32 pixel box counting testing showed different result with 64x64 pixel box counting testing, there are 27% differences for cluster = 3, and 45% differences for cluster = 4.