# Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis
## 4 Experimental Results and Analysis
### Performance Evaluation of the CIED  
We evaluate the edge detection method using medical images from the MIEDT dataset. The MIEDT dataset has played a crucial role in this research, and its data content has provided support for edge detection methods. For the convenience of research and use, this dataset can be accessed through Kaggle. The specific steps are as follows: First, we visit the address corresponding to the dataset ([[https://www.kaggle.com/datasets/lidang7/8/miedt-dataset](https://www.kaggle.com/datasets/lidang7/8/miedt-dataset)]([https://www.kaggle.com/datasets/lidang7/8/miedt-dataset](https://www.kaggle.com/datasets/lidang7/8/miedt-dataset)) (accessed on 12 January 2025)), and the dataset can be obtained through the download operation. After obtaining the dataset, according to the detailed instructions, we can further understand the structure of the dataset, the data format, and how to use code to load the data and other information. To evaluate the intuitive visual effort, we use the CIED method for edge detection on medical images. Partial results are shown in Figure 5. The first row MI_1 to MI_5 is the original medical images, the second row is the detected edge images of CIED, and the third row is the GT of the medical images. In addition, Figure 5 also shows the recall and F1-score of the edge detection results. We can intuitively observe that CIED can extract complete and clear edges stably even though these images come from different types and have different contrast distributions. From the detection results of MI_1 in Figure 5, the CIED can extract the boundary of different tissues and structures of the brain completely. From the detection results of MI_2 and MI_3 in Figure 5, CIED can extract the complete heart contour and can accurately detect the contour edges of the coronary arteries. As a result of MI_4 and MI_5 in Figure 5, CIED can accurately extract the edges of skin lesions and meanwhile capture their subtle features. For example, as shown in the results of MI_5 in Figure 5, the detected edge in the green box further targets the core lesion area. In addition, recall is high in all edge detection results, indicating that CIED has a high edge detection rate. Overall, in terms of visual effect, CIED can not only effectively extract the key contours in medical images, but also extract the edges with slight variations, and these edges can further help doctors target the potential lesion areas. To further quantitatively evaluate the CIED, we record the evaluation results of edge detection with four evaluation metrics including accuracy, precision, recall, and F1-score. Figure 6 demonstrates the performance of CIED under each evaluation metric. Figure 6a shows the accuracy of the CIED method. It stays at a high level with an average value of 0.978. This indicates that the edge pixels obtained by CIED have a relatively high classification accuracy. However, since the number of edge pixels is generally small, relying on the accuracy alone is not comprehensive. Figure 6b shows that the recall of the CIED method performs well. It can detect most of the real edge information with an average value as high as 0.917. This indicates that CIED can effectively detect edge information in different types of medical images. The CIED is highly effective in detecting most of the real edge information in medical images, hardly missing any important edge details. Figure 6c indicates that the precision of CIED method performance is mediocre on some medical images, where the mean value is 0.408. This is because the CIED extracts edge information as comprehensively and accurately as possible to prevent missed diagnoses in medical diagnosis. In clinical practice, the complete and accurate delineation of the edges of the lesion area is crucial for accurate diagnosis and the formulation of subsequent treatment plans. Even the omission of tiny edge details may lead to missed diagnoses, which then affects the treatment outcomes and prognoses of patients.