基于肿瘤区域分割的乳房x线图像乳腺癌检测

Vikramathithan A C, D. Shashikumar
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引用次数: 5

摘要

在我们提出的乳腺癌恶性检测研究中,使用模糊最小最大神经网络技术进行。大多数女性在患这种乳腺癌的早期阶段,乳房x光照片大多起着至关重要的作用。首先,使用自适应中值滤波器对输入的乳房x线图像进行平滑处理,然后利用基于Histon的模糊c均值聚类对组织进行分割。我们从分割后的图像中提取特征,这些特征包括统计特征和语义特征。然后我们可以借助这些特征来识别恶性区域。分割的区域是恶性的还是良性的,使用最优模糊最小最大神经网络和灰狼优化算法,在这些的帮助下,我们将识别乳腺癌区域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Breast cancer detection in mammogram image with segmentation of tumour region
In our proposed breast cancer malignant detection study are performed with the aid of fuzzy min max neural network technique. Majority of women's are affected in this breast cancer at a early stage the mammogram images are mostly play in a vital role. Initially the input mammogram image smoothened with the aid of adaptive median filer from that smoothened image we are segmenting tissues with the aid of Histon based fuzzy c-means clustering. We are extracting features from that segmented image the features are statistical and semantic features. Then we can identify the malignant region with the aid of these features. The segmented region is maligned or benign using an optimal fuzzy min max neural network with grey wolf optimisation algorithm with the aid of these we will identify a breast cancer region.
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