A Multilevel Thresholding Approach for Acne Detection in Medical Treatment

Abstract

In the quantitative assessment on the success of treatment, the automatic detection of acne pixels from digital color images would be helpful. In this paper, we proposed an automatic acne detection method through the processing of facial images taken by the smartphone based on the image processing. In this approach, the RGB image is transformed into various color spaces based on the differences between features of each acne lesion type. This method has been used the a* channel of the CIELab color space to detect the inflammatory acne (papules and pustules). The S channel of HSV color space was used to detect the non-inflammatory acne (whiteheads and blackheads). A multi-level threshold is then used to make acne extraction and blob detection. The effectiveness of the proposed procedure is shown by experimental results. We showed the possibility of detecting 4 types of acne lesions (whiteheads, blackheads, papules, pustules) with different skin colors and different smartphones in this experiment by applying a combination of several color spaces. The result shows a recall of about 85.71% in detecting different acne types at a reasonable processing time. This is the remise to help doctors to assess the level of acne on the patient's face in an effective and time-saving way.