Abstract

This study focuses on the implementation of an improved method for detecting single and multi-tissues in MR brain images and accurately estimating their respective areas. The proposed approach utilizes hybrid clustering techniques and estimates tissue areas in terms of square millimeters (mm²) by leveraging typography and digital imaging units. The implementation demonstrates enhanced accuracy and reduces computational time compared to existing algorithms. To validate the effectiveness of the proposed method, simulation results were compared with those obtained using other algorithms. The findings indicate that the proposed approach yields favorable outcomes in terms of tissue detection and area estimation. Furthermore, this methodology holds the potential to be extended to 3D multi-modal medical image segmentation, incorporating more effective and accurate clustering algorithms. The successful implementation of this approach contributes to the advancement of tissue detection and area estimation in MR brain images, offering improved accuracy and computational efficiency. This work paves the way for further research and development in the field of medical image analysis, with potential applications in clinical diagnosis and treatment planning

Description

.