For the last many decades, the research is towards the classification of medical images in the early phase of its detection. But, the task becomes challenging due to the absence of the color information, like in natural scene images, and low illumination. In this paper, a multi-scale spectral approach is proposed for the classification of medical images. The proposed approach uses a dyadic filter bank extended to six scales for simultaneous modulation of the frequency and amplitude signal of the medical image. The modulated signal strength is used for enhancing the contrast of the image as a preprocessing step. The 32 bin spectral histogram is used to fetch the features using different modulation components at each scale of the dyadic filter bank. The proposed method has experimented with two medical imaging databases - one is malignant Brain tumor MRI scans collected from SMS medical college Jaipur. The second database is from the TCIA data repository having three datasets of Lung-CT and Brain MRI. These datasets have experimented with SVM using a quadratic kernel function. The experimental results show that the proposed approach fetches better textural information as compared with traditional texture analysis methods. Based on the analysis of the experimentation results, it is recommended that the use of the spectral features gives better early detection of the abnormalities for medical imaging datasets.

在过去的几十年中，研究一直致力于在其检测的早期对医学图像进行分类。但是，由于缺乏自然场景图像中的颜色信息和低照度，因此任务变得具有挑战性。本文提出了一种多尺度光谱方法对医学图像进行分类。所提出的方法使用扩展到六个比例的二进阶滤波器组，以同时调制医学图像的频率和幅度信号。调制信号强度被用作增强图像的对比度的预处理步骤。32 bin频谱直方图用于在二进式滤波器组的每个比例上使用不同的调制分量来获取特征。该方法已在两个医学成像数据库中进行了实验-一个是从SMS斋浦尔医学院收集的恶性脑肿瘤MRI扫描图。第二个数据库来自TCIA数据库，该数据库具有三个肺CT和脑MRI数据集。这些数据集已使用二次核函数对SVM进行了实验。实验结果表明，与传统的纹理分析方法相比，该方法可获取更好的纹理信息。基于对实验结果的分析，建议使用光谱特征可以更好地及早发现医学影像数据集的异常。这些数据集已使用二次核函数对SVM进行了实验。实验结果表明，与传统的纹理分析方法相比，该方法可获取更好的纹理信息。基于对实验结果的分析，建议使用光谱特征可以更好地及早发现医学影像数据集的异常。这些数据集已使用二次核函数对SVM进行了实验。实验结果表明，与传统的纹理分析方法相比，该方法可获取更好的纹理信息。基于对实验结果的分析，建议使用光谱特征可以更好地及早发现医学影像数据集的异常。