International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 05 | May-2016 www.irjet.net p-ISSN: 2395-0072 © 2016, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 3223 SEGMENTATION OF SKIN LESION FROM DIGITAL IMAGES USING MORPHOLOGICAL FILTER M.Yuvaraju 1 , D.Divya 2 , A.Poornima 3 1 Assistant Professor, Department of EEE, Anna University Regional Campus, Coimbatore, Tamil Nadu, India rajaucbe@gmail.com 2 Assistant Professor, Department of EEE, Anna University Regional Campus, Coimbatore, Tamil Nadu, India divyadevarajan@gmail.com 3 PG Scholar, Department of EEE, Anna University Regional Campus, Coimbatore, Tamilnadu, India a.poorni69@gmail.com ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Skin cancer is the deadliest form of skin disease. Its incidence has been rising at a rate of 3% per year. In order to reduce the cost of screening, there is a need for an automated melanoma screening system. Segmentation is significant to detect skin lesion from images. In the proposed method, a novel texture based skin lesion segmentation algorithm is used and probabilistic neural network is used to classify the stages of skin cancer. The feature of the image is extracted by using GLCM algorithm and its features gives better classification with probabilistic neural network. The five different skin lesion is commonly grouped into Basal Cell Carcinoma (BCC), Actinic Keratosis (AK), Squamous Cell Carcinoma (SCC), Melanocytic nevus/mole (MC), Seborrhoeic Keratosis (SK).The system will be used to classify the queried images automatically to choose the stages of abnormality. The morphological filter segmentation is used to detect the skin cancer. The proposed system has higher accuracy, sensitivity, specificity, segmentation compared to other systems. Key Words: Skin cancer, Grey level co-occurrence matrix, Probabilistic Neural Network, Segmentation. 1. INTRODUCTION Skin is commonly used primal in image processing and the applications range from face tracking to signal analysis for different human interactions. Generally skin cancers are the most common prevalent form of cancers in human beings. Surprisingly it is also a deadly type of cancer. Many of the skin cancers are curable at early stages. Also with the technology advancements, early detection is possible [1]. The American cancer society estimates that more than 70,000 new skin cancers are diagnosed every year in the United States alone. The skin cancers can be classified into melanoma and non-melanoma. Melanoma is the most deadly form and are predictable 76,690 people being diagnosed with melanoma and 9480 people quiet of melanoma in the United States. In the United States the life time hazard of receiving melanoma is 1 in 49 [2]. Melanoma reasons for approximately 75% of deaths related with skin cancer. It is a malignant tumor of the melanocytic and generally happens on the trunk or lower extremities. Recent trends have stated that melanoma can be less dangerous if detected at a premature stage. I.e. if detected at stage I the survival rate of the effected person increase to 96% [3]. Due to the increase in the incidence rate of melanoma, researchers are more concerned about proposing such automated systems that diagnose skin lesion correctly. Also it has been found that in order it detect Melanoma at an early stage screening is very much valuable [4] but the cost of screening melanoma is too high. So to reduce the screening cost the automated algorithms have been proposed to automatically screen melanoma. A digital dermoscope acquires images that contribute to early screening of melanoma [5] and all automated systems use dermoscopic images. Dermoscope is a device that is used to capture images of lesion by the dermatologists. It also magnifies the image and acts as a filter. With dermoscopy it becomes difficult to differeniate malignant and benign lesion and in such case a detailed analysis is needed to be done [6]. Recent work with automated melanoma screening