TY - JFULL AU - Sheema Shuja Khattak and Gule Saman and Imran Khan and Abdus Salam PY - 2015/6/ TI - Maximum Entropy Based Image Segmentation of Human Skin Lesion T2 - International Journal of Computer and Information Engineering SP - 1086 EP - 1091 VL - 9 SN - 1307-6892 UR - https://publications.waset.org/pdf/10001173 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 101, 2015 N2 - Image segmentation plays an important role in medical imaging applications. Therefore, accurate methods are needed for the successful segmentation of medical images for diagnosis and detection of various diseases. In this paper, we have used maximum entropy to achieve image segmentation. Maximum entropy has been calculated using Shannon, Renyi and Tsallis entropies. This work has novelty based on the detection of skin lesion caused by the bite of a parasite called Sand Fly causing the disease is called Cutaneous Leishmaniasis. ER -