Image registration approaches based on standard information criteria were widely employed, showing promising results in the registration of medical monomodal images. Feature-based registering is an effective clinical application technique since computational costs can be reduced significantly. The following four steps are generally used for most registration methods: detection of features, extraction of features, matching features and transformation determination. The accuracy of the registration procedure is dependent on matching a feature and detecting control points (CP). Thus, this paper supports this feature for monomodal registration of magnetic resonance imaging (MRI). MRI is gold-standard imaging to detect prostate cancer progression as it provides better information for soft tissue visualization. In this study, registration of MRI images taken from different time frames is developed to ease the physician in integrating the information about the evolution of the tumour. The accuracy of the registration process depends on matching features and CP detection by calculating the iterative closest point (ICP). The prostate volumes are calculated, and the result shows minimal errors. This registration method has been applied in coronal, sagittal and axial views from five patient datasets. The accuracy of automatic registration results is 0.1mm (axial), 1.1mm (coronal) and 0.2mm (sagittal). These accuracies are comparable to gold-standard manual registration by the experts. There was no significant difference between the automatically and manually registered MRI monomodal. Thus, the proposed method enables the physician to diagnose prostate cancer as it can provide important information about the disease progression and decide the necessary therapies regarding the patient’s condition