近年来医学图像的自动分析显著提高了疾病诊断的速度和精度。医学图像，如超

In recent years, automatic analysis of medical images has significantly improved the speed and accuracy of disease diagnosis. Medical images, such as ultrasound, MRI, and X-ray images, often have distortions, are unclear, and cannot highlight important areas. These characteristics make the image unsuitable for traditional object recognition and classification techniques, because these techniques rely on the detection of object boundaries. Texture analysis does not have such dependent features, and has achieved good results in many medical applications. 

In recent years, the automatic analysis of medical images has significantly improved the speed and accuracy of disease diagnosis. Medical images, such as ultrasound, MRI, and X-ray images, are often distorted, unseen, and unable to highlight important areas. These features make images unsuitable for use in traditional object recognition and classification techniques, which depend on the detection of object boundaries. Texture analysis does not have such dependency characteristics, and has achieved good results in many medical applications.

In recent years, the automatic analysis of medical images has significantly improved the speed and accuracy of disease diagnosis. Medical images, such as ultrasound, MRI, and X-ray images, often have areas that are distorted, unclear, and difficult to highlight. These characteristics make the image not suitable for the traditional object recognition and classification technology, because these technologies rely on the detection of object boundary. Texture analysis does not have such dependent features, and has achieved good results in many medical applications. A kind of