中文说明：应用背景现在的前列腺疾病在成人和老年人中很常见。因为所有类型的前列腺疾病都有类似的症状，很难诊断恶性前列腺癌的早期阶段。在这项工作中，试图找出类型的利用纹理分析法对腹部电脑断层图像的前列腺疾病。前列腺区域是分段的图像切片。利用不断变换为顺序基于分割的图像纹理特征提取真正的变换映射（SMRT）。六个不同的功能集是由不同的子子图像的大小和块大小来源。每个利用遗传算法优化特征集。基于分类准确率的最佳特征集选择。KNN分类使用。SMRT纹理特征集与子图像的大小和块大小32给出了较高的分类精度。关键技术前列腺癌是602岁以上男性的恶性肿瘤之一。数千人死亡对这种病每年。前列腺癌是可以治愈的早期阶段。但在早期并没有表现出明显的症状阶段。疾病需要长达十年的生命威胁。然而，一些前列腺癌可以生长和传播quickly1。教育人是重要的，了解的风险的进展和各种治疗方案。
English Description：
Application backgroundNowadays prostate disease is very common in adult and elderly men. Since all types of prostate diseases are having similar symptoms, it is difficult to diagnose malignant prostate at an early stage. In this work an attempt is made to identify the types of prostate diseases from abdomen CT images of the patients using texture analysis. Prostate region is segmented from the CT image slice. Texture features are extracted from the segmented images using an evolving transform named Sequency based Mapped Real Transform (SMRT). Six different SMRT feature sets are derived by varying sub image size and block size. Each feature set is optimized using Genetic Algorithm (GA). The best feature set is selected based on classification accuracy. KNN classifier used. SMRT texture feature set with sub image size and block size 32 gives high classification accuracy.Key TechnologyProstate cancer is o