论文标题
使用直方图处理和特征提取癌症分类的医疗图像增强
Medical Image Enhancement Using Histogram Processing and Feature Extraction for Cancer Classification
论文作者
论文摘要
MRI（磁共振成像）是一种用于分析和诊断大脑中癌症或肿瘤等图像所定义的问题的技术。医生需要良好的对比图像以实现更好的治疗目的，因为它包含了最大的疾病信息。 MRI图像是低对比度图像，这使诊断困难；因此，需要更好地定位图像像素。直方图均衡技术有助于增强图像，从而提供改善的视觉质量和明确的问题。对比度和亮度的增强，以使其不会失去其原始信息并保留亮度。我们比较本文中不同的均衡技术。对这些技术进行了严格的研究和详细研究。还将它们列出来比较图像中存在的各种参数。此外，我们还使用K-均值算法将肿瘤部分从大脑中脱离并提取。对于分类和特征提取，使用的方法是支持向量机（SVM）。这项研究工作的主要目标是帮助医疗领域的图像处理。
MRI (Magnetic Resonance Imaging) is a technique used to analyze and diagnose the problem defined by images like cancer or tumor in a brain. Physicians require good contrast images for better treatment purpose as it contains maximum information of the disease. MRI images are low contrast images which make diagnoses difficult; hence better localization of image pixels is required. Histogram Equalization techniques help to enhance the image so that it gives an improved visual quality and a well defined problem. The contrast and brightness is enhanced in such a way that it does not lose its original information and the brightness is preserved. We compare the different equalization techniques in this paper; the techniques are critically studied and elaborated. They are also tabulated to compare various parameters present in the image. In addition we have also segmented and extracted the tumor part out of the brain using K-means algorithm. For classification and feature extraction the method used is Support Vector Machine (SVM). The main goal of this research work is to help the medical field with a light of image processing.
