Abstract
Segmentation of the whole-cardiac CT image sequence is the key to computer-aided diagnosis and study of lesions in the heart. Due to the dilation, contraction and the flow of the blood, the cardiac CT images are prone to weak boundaries and artifacts. Traditional manual segmentation methods are time-consuming and labor-intensive to produce over-segmentation. Therefore, an automatic cardiac CT image sequence segmentation technique is proposed. This technique was employed using deep learning algorithm to understand the segmentation function from the ground truth data. Using the convolution neural network (CNN) on the central location of the heart, filtering ribs, muscles and other contrasting contrast are not an obvious part of the removal of the heart area. Staked denoising auto-encoders are used to automatically deduce the contours of the heart. Therefore, nine cardiac CT image sequence datasets are used to validate the method. The results showed that the algorithm proposed in this paper has best segmentation impact to such cardiac CT images which have a complex background, the distinctness between the background and the target area which is not obvious; and the internal structure diversification. It can filter out most of the non-heart tissue part, which is more conducive to the doctor observing patient’s heart health.
Highlights
Cardiac disease is the leading cause of death in Human
Cardiac CT image sequence segmentation is the key to the diagnosis of cardiac diseases using CT images
In order to determine the aspect of every step, the effect of convolution neural network (CNN) for three image segments close the base/middle and the point of the heart as shown in Fig. 8, illustrates the location results of the different cardiac segment
Summary
Cardiac disease is the leading cause of death in Human. The heart as a substantial organ, having only Imaging data to understand the location of its internal lesions, is an effective means of noninvasive diagnosis of cardiac disease [1]. Improvement of the segmentation accuracy of whole-heart CT image sequences has got a major concentration of cardiac disease research [2]. The traditional approaches mainly obtain the edge points of the target manually by the anatomical knowledge and experience by the clinician [3] These methods have a high accuracy but time-consuming, labor-intensive, and the doctor’s experience surely affect the accuracy of the segmentation strongly. With the development of computer and image processing technology, the researchers have developed a series of methods which semi-automatically extract the target through input of specified calculation parameters and human-computer interaction. These methods can combine a prior knowledge such as anatomical knowledge, which let it be more popular. The accuracy of the segmentation cannot be ensured and the calculation is extremely expensive [4]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Computer Science and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
