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首页> 外文期刊>American journal of applied sciences >Dermoscopic Image Segmentation using Machine Learning Algorithm
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Dermoscopic Image Segmentation using Machine Learning Algorithm

机译：使用机器学习算法的皮肤镜图像分割

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Problem statement: Malignant melanoma is the most frequent type of skin cancer. Its incidence has been rapidly increasing over the last few decades. Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the structure of interest in medical images. Approach: This study explains the task of segmenting skin lesions in Dermoscopy images based on intelligent systems such as Fuzzy and Neural Networks clustering techniques for the early diagnosis of Malignant Melanoma. The various intelligent systems based clustering techniques used were Fuzzy C Means Algorithm (FCM), Possibilistic C Means Algorithm (PCM), Hierarchical C Means Algorithm (HCM); C-mean based Fuzzy Hopfield Neural Network, Adaline Neural Network and Regression Neural Network. Results: The segmented images were compared with the ground truth image using various parameters such as False Positive Error (FPE), False Negative Error (FNE) Coefficient of similarity, spatial overlap and their performance was evaluated. Conclusion: The experimental results show that Hierarchical C Means algorithm( Fuzzy) provides better segmentation than other (Fuzzy C Means, Possibilistic C Means, Adaline Neural Network, FHNN and GRNN) clustering algorithms. Hierarchical C Means approach can handle uncertainties that exist in the data efficiently and useful for the lesion segmentation in a computer aided diagnosis system to assist the clinical diagnosis of dermatologists.
机译：问题陈述：恶性黑色素瘤是最常见的皮肤癌类型。在过去的几十年中，其发病率迅速增加。为了促进医学图像中感兴趣结构的表征和可视化，医学图像分割是最重要和最关键的过程。方法：本研究解释了基于智能系统（例如模糊和神经网络聚类技术）对皮肤镜图像中的皮肤病变进行分割的任务，用于早期诊断恶性黑色素瘤。所使用的各种基于智能系统的聚类技术是：模糊C均值算法（FCM），可能C均值算法（PCM），分层C均值算法（HCM）；基于C均值的模糊Hopfield神经网络，Adaline神经网络和回归神经网络。结果：使用各种参数将分割后的图像与地面真实图像进行比较，例如误报率（FPE），误报率（FNE）相似度，空间重叠率和性能。结论：实验结果表明，层次C均值算法（模糊）提供了比其他（模糊C均值，可能性C均值，Adaline神经网络，FHNN和GRNN）聚类算法更好的分割效果。分层C均值方法可以有效地处理数据中存在的不确定性，并且对计算机辅助诊断系统中的病变分割有帮助，以帮助皮肤科医生进行临床诊断。

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