# Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis
## 5 Discussion
Gaussian filtering can effectively remove image noise and better preserve edge details, thus improving the image quality. In morphological processing, the opening operation can remove small objects, burrs, and separate connected objects, making the main part of the image clearer and facilitating the accurate identification of object edges. Meanwhile, the closing operation can fill the internal holes of the target object, making the edges of the object more complete and continuous. The preprocessing method combining Gaussian filtering and morphological processing creates favorable conditions for subsequent edge detection and plays an important role in improving the accuracy and effect of CIED edge detection. The CIED method is based on three MSB planes. It can highlight important features, reduce the effect of noise, increase contrast, and improve computational efficiency [46]. Three MSB planes contain the main structural and contour information within the image, and these significant shapes and structural details are emphasized. Usually, the noise is more obvious in the LSB planes, while the information in the three MSB planes is more stable and reliable [47]. In the process of edge detection, choosing the three MSB planes can reduce the interference of noise in edge detection results and improve the performance of edge detection. The three MSB planes enhance the contrast of an image without additional contrast processing, making edges sharper. By highlighting different gray-level regions in the image, the three MSB planes enhance edge detection by increasing the contrast between the edges and the background. This is especially useful for some images with low contrast. Utilizing three MSB planes for edge detection also improves computational efficiency. This is because the CIED is based on bit planes. It does not use complex pixel values for calculation. Instead, it uses simple binary values of zero and one, so it is faster and more efficient. Based on the computational characteristics of CIED, in order to further improve the real-time performance of CIED, we explore the application of the CIED method to edge devices or embedded systems to achieve real-time edge detection. These devices usually have limited computing resources, and further algorithm optimization is required to ensure real-time performance. We are considering using frameworks such as CUDA or TensorRT to optimize the computational efficiency of CIED in future research so that it can run on embedded systems. This can help expand the application of CIED in fields such as real-time medical image analysis, and assist with creating patient-centered applications [48]. In future research, to enhance the adaptability and accuracy of the CIED, we plan to progress step by step from ensemble learning methods, through machine learning methods to deep learning methods. First, through ensemble learning, based on the CIED method, multiple edge detection operators can be integrated. Through ensemble strategies such as voting and weighted averaging, we will try to enhance the robustness and detection precision. Second, within the bit plane, various image features such as gradient information and texture information can be extracted. A multi-feature fusion strategy will be designed. Via feature selection and feature fusion techniques, the richness and representativeness of the features will be improved. Traditional machine learning models such as Support Vector Machine (SVM) and Random Forest (RF) will be introduced to classify and optimize the extracted bit plane features, further enhancing the detection effect. Finally, we will explore deep learning methods, employing models such as Convolutional Neural Network (CNN) and U-Net to automatically learn edge features from bit planes, and try to further improve the performance of CIED.