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Type of lung disease is very much manifold, but type of lung disease caused by smoking there are only 4, namely Bronchitis, Pneumonia, Emphysema and Lung Cancer. Doctors usually diagnose lung disease from CT scans using the naked eye, then interpret data one by one.This procedure is not effective. The aim of this research is improvement accuracy of lung diseases detection caused by smoking using support vector machine on computed tomography scan (CT scan) images. This study includes 4 (four) main points. First is the development of software for segmentation of lung organ automatically using Active Shape Model (ASM) method. Second is the segmentation of candidates who are considered illness by using Morphology Mathematics. The third process of lung disease detection using Support Vector Machine (SVM). Fourth is visualization of disease or lung disorder using Volume Rendering.

This paper presents experiments and results on lung tuberculosis (TB) identification by using computer. This research's attempt is to reduce patient waiting time in obtaining X-ray diagnosis result on lung TB disease due to the mismatch the ratio of radiologist to the number of patients, especially in remote areas in Indonesia. To imitate radiologist which make visual examination on textural feature of thoracic X-ray images to make diagnosis, we exploit textural features calculated by computer to be used as descriptor in classifying images as TB or non-TB. We used statistical feature of image histograms by calculate five features: mean, standar deviation (std), skewness, kurtosis, and entropy. Features calculated where then reduced to two and one principal feature using Principal Componen Analysis (PCA) method. Finally, we used minimum distance classifier as classifier method based on two and one principal feature as descriptor. This experiment results shown that it is possible to classify TB and non-TB images based on statistical features on image histogram.