基于分割技术的不同皮肤病变图像识别与增强

Neetu Mittal, Sudhir Tanwar, S. Khatri
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引用次数: 11

摘要

皮肤病变是皮肤的一部分,由于皮肤疾病而出现异常生长或存在。早期的皮肤病很容易治愈;否则,它们开始扩散到身体的其他部位,可能是致命的。对这类患者来说,早期发现皮肤病至关重要。由于每位患者的各种皮肤科检查程序成本高昂,因此需要一个自动化系统来提供更好的病变图像和视力,以帮助医生进一步诊断并开出正确的处方和药物。本文提出了一种新颖的皮肤损伤自动识别方法。为了提高皮肤病变图像的质量,采用中值滤波和Sobel边缘检测技术对图像进行滤波和分割。通过测量不同皮肤疾病所获得的图像的熵,验证了所提出工作的有效性。该性能在70个样本的数据集上进行了测试,这些样本来自150个不同身体部位的医学图像,包含10种不同类型的皮肤病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification & enhancement of different skin lesion images by segmentation techniques
A skin lesion is a portion of skin with an abnormal growth or presence due to a skin disease. Skin diseases at their early stages can be cured very easily; otherwise they begin to spread to other parts of the body and may be deadly. An early detection of skin disease is essential for such patients. Due to the high cost involved in various dermatology testing procedures for every patient, an automated system is required to give better lesion images and vision to help the doctors to further diagnose and prescribe the correct prescription and medication. In this paper, an innovative approach for automatic identification of skin lesions is proposed. To improve the quality of skin lesion images, Median filtering and Sobel edge detection techniques have been implemented for filtering and segmentation. The efficacy of the proposed work has been verified by measuring the entropy of the resultant images obtained for different skin diseases. The performance is tested on a dataset of 70 samples from 150 medical images of different body parts with 10 different classes of skin diseases.
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