Automated Framework for Segmenting Skin Lesions using Artificial Bee Colony Optimization with Morphological Reconstruction
R. Sumathi1, M.Venkatesulu2
1R. Sumathi, Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, (Tamil Nadu), India.
2M. Venkatesulu, Department of Information Technology, Kalasalingam Academy of Research and Education, (Tamil Nadu), India.
Manuscript received on 23 November 2019 | Revised Manuscript received on 17 December 2019 | Manuscript Published on 30 December 2019 | PP: 144-148 | Volume-9 Issue-1S4 December 2019 | Retrieval Number: A11061291S419/19©BEIESP | DOI: 10.35940/ijeat.A1106.1291S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Nowadays, Many people are affected by skin cancers. Our proposed work designed a framework to extract the skin cancer using artificial bee colony with morphological reconstruction filters, which helps the demonologist to prevent the severity in early stage, Melanoma is the now become a harmful form of skin cancer which leads the skin cells to grow rapidly and form cancerous tumors. We collected various melanoma images from having used samples from public dataset like ISIC archive and a few from clinical datasets. To remove the noise, median filtering is used for preprocessing in the first step, to segment the tumor boundary Artificial bee colony is used and to remove the unwanted pixels using morphological reconstruction filters. Segmentation metrics like precision, recall, accuracy, Mean Square Error, Peak signal to noise ratio and computational time were calculated. Our proposed method yield 97.7% segmentation accuracy when compared with the level set method and Fuzzy C Means clustering techniques
Keywords: Image Segmentation, Median Filter, Artificial Bee Colony Optimization, Morphological Reconstruction Filters.
Scope of the Article: Patterns and Frameworks