Automatic facial acne detection for medical treatment

In this thesis, the implementation of the automatic acne detection system has been made by the various method of image processing. It consists of two main parts; blob detection and Bayesian's classification. First, a blob detection technique is utilized to facial images of patients to reveal circular structures of acne and its amounts. An input image is RGB, then it is converted into HSV and grayscale pictures, these are done to compare and relate points of interest. There may be some misdetection of acne such as blemish and freckle, binary thresholding has been used afterward to contrast and remove the mistakes easier. Using of box shape created by MATLAB, it has been made as an indication to the regions of interest in the results, they are seen as markings. For the second part, the output of blob detection has been fed into feature extraction and Bayesian classification algorithm to filter out the misdetection results from the previous step which boosts the overall system performance. The method of feature extraction composes of acne characteristic calculation such as skewness, kurtosis or entropy. Lastly, an algorithm of classification is applied as training and testing process to have the system automatically distinguish facial contents more deeply. Experimental output increases accuracy, precision and sensitivity with constant value, but all of these depend on location, shape and lighting condition of the facial image.