Deep CNN based skin lesion image denoising and segmentation using active contour method

• Published in: Engineering and Technology Journal Vol. 37; no. 11A; pp. 464 - 469
• Main Authors: Abd Allah, Hadil Nasrat, Abd al-Jalil, Halah K.
• Format: Journal Article
• Language: English
• Published: Baghdad, Iraq University of Technology 25.11.2019; Unviversity of Technology- Iraq
• Subjects: convolutional neural networks; denoising; segmentation; skin cancer; الجلد; السرطان; الشبكات العصبية; تقطيع الصورة
• ISSN: 1681-6900; 2412-0758
• DOI: 10.30684/etj.37.11A.3

Automatic skin lesion segmentation on skin images is an essential component in diagnosing skin cancer. Image de-noising in skin cancer lesion is a description of processing image which refers to image restoration techniques to develop an image in predefined touch. Then de-noising is the crucial step of image processing to restore the right quality image after that which can use in many processes like segmentation, detection. This work proposes a new technique for skin lesion tumor denoising and segmentation. Initially, using Deep Convolution Neural Network (CNN) to eliminate noise and undesired structures for the images. Then, a new mechanism is proposed to segment the skin lesion into skin images based on active_contour straight with morphological processes. Different noise removal and segmentation techniques on skin lesion images are applying and comparing. The proposed algorithm shows improvement in the results of both noise reduction and segmentation