# Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis
## 3 Proposed Methods  
Medical image-assisted diagnosis is crucial for disease diagnosis and treatment, and edge detection methods play a significant role in it. Many existing edge detection techniques rely mainly on the calculation of the gray-scale gradient, which performs in medical images with low contrast and blurred edges not well and struggles to meet the diagnostic accuracy and efficiency requirements. To solve the above problems, we propose the CIED method. The CIED can extract clear and complete edges on medical images of different contrasts with less noise interference. The grayscale image's pixels can be regarded as binary values ranging from 0 to 255. A normal grayscale image consists of 8-bit planes. The three MSB planes of the image are rich in the main contour information and have a natural advantage for extracting the image edges, while the five LSB planes are rich in the details of the image and are not conducive to highlighting the global structure of the image. Based on the properties of bit planes, the CIED takes advantage of the three MSB planes. Figure 3 illustrates the demonstration of the CIED. The CIED includes four main steps: image preprocessing, grayscale image bit plane decomposition, \(3\times 3\) neighborhood analysis and process, and edge detection result fusion. If the original image to be detected is a color image, its conversion to a grayscale image is necessary. In the image preprocessing stage, the method combining Gaussian filtering and morphological processing is adopted to process the original grayscale image. First, we use Gaussian filtering to process the image. This can effectively reduce Gaussian noise, make the image smoother, and better preserve the edge and detailed information of tissues and organs in medical images. Then, the combination of morphological opening and closing operations can further process the image. The opening operation can remove small bright noise points and to a certain extent smooth the contours of objects. The closing operation can connect the broken parts of the edges of objects and fill in small holes, making the edges of objects more complete. This processing procedure can fully utilize the advantages of the two methods. It can better highlight and optimize the important  
Figure 2: Bit-plane decomposition of the brain CT image. structures and edges in medical images while reducing noise. After preprocessing, we can obtain the processed image. The processed image is decomposed into 8-bit planes, and 3 MSB planes are selected. As shown in Figure 3, the three MSB planes are Bit Plane 7, Bit Plane 6, and Bit Plane 5. These bit planes contain almost all the edge information. Bit Plane 7 tends to highlight the most significant, overall contour information of the main objects or regions in the image, presenting a relatively rough but clear edge outline. Bit Plane 6 tends to show more detailed edge profile information than Bit Plane 7 and starts to show edges of relatively large localized features on the basis of the main profile. Bit Plane 5 contains a bit more rich edge detail. In addition to the major and larger localized edge contours covered in the previous two planes, there are also some smaller localized edge variations and other information. At the same time, we use the Otsu method to obtain the binary image. The binary image obtained after processing by [PERSON]'s method can divide the original image pixels into the background part and the foreground part by automatically determining the threshold value, which can separate the target from the background and highlight the main features [35]. As shown in Figure 3, the binary image is capable of outlining the general outline of a grayscale image, which also contains the relatively simple and more obvious edge information in the grayscale image. Here, the binary image can be considered as a layer of the bit plane. There are four bit planes in total, including 3 MSB planes and a binary image.