Published Paper Details:

Heart block diseases are one of the most deadly diseases with a high mortality rate. The shape and size of the block are random during the growth process. Heart Block segmentation is a heart block assisted diagnosis technology that separates different block structures such as edema and active and block necrosis tissues from normal heart tissue. Magnetic resonance imaging (MRI) technology has the advantages of no radiation impact on the human body, good imaging effect on structural tissues, and an ability to realize tomographic imaging of any orientation. Therefore, doctors often use MRI heart block images to analyze and process heart blocks. In these images, the block structure is only characterized by gray scale changes, and the developed images obtained by different equipment and different conditions may also be different. This makes it difficult for traditional image segmentation methods to deal well with the segmentation of heart block images. Considering that the traditional single-mode MRI heart block images contain incomplete heart block information, it is difficult to segment the single-mode heart block images to meet clinical needs. In this work, an automatic thresolding algorithm is introduced to process the diagnosis of MRI heart block images. In the absence of added noise, the proposed algorithm has better advantages than traditional methods. Experimental results show that the proposed algorithm has better noise immunity than a comparable algorithm.