Abstract
Internet of Things (IoT) brings telemedicine a new chance. This enables the specialist to consult the patient’s condition despite the fact that they are in different places. Medical image segmentation is needed for analysis, storage, and protection of medical image in telemedicine. Therefore, a variety of methods have been researched for fast and accurate medical image segmentation. Performing segmentation in various organs, the accurate judgment of the region is needed in medical image. However, the removal of region occurs by the lack of information to determine the region in a small region. In this paper, we researched how to reconstruct segmentation region in a small region in order to improve the segmentation results. We generated predicted segmentation of slices using volume data with linear equation and proposed improvement method for small regions using the predicted segmentation. In order to verify the performance of the proposed method, lung region by chest CT images was segmented. As a result of experiments, volume data segmentation accuracy rose from 0.978 to 0.981 and from 0.281 to 0.187 with a standard deviation improvement confirmed.
Highlights
Telemedicine is defined by the World Health Organization (WHO) as “the practice of medical care using interactive audiovisual and data communications
Besides patients’ benefit, Internet of Things (IoT) even helps entire health industry, in which wide scope of medical devices are connected to existing health network, patient crucial life signal is captured by sensors and transmitted to remote medical center, and the doctor is able to remotely monitor patient condition and provide medical suggestion and aiding [11, 12]
We researched how to improve the performance of exact segmentation of a small region with volume data which is a bunch of medical images
Summary
Telemedicine is defined by the World Health Organization (WHO) as “the practice of medical care using interactive audiovisual and data communications This includes the delivery of medical care services, diagnosis, consultation, treatment, as well as health education and the transfer of medical data” [1]. In the field of medical image segmentation, many researchers are studying a variety of ways to obtain fast and accurate automatic segmentation methods for medical images. We researched how to improve the performance of exact segmentation of a small region with volume data which is a bunch of medical images. Damaged or removed small regions need reconstruction to improve performance of segmentation. Using chest CT images among the medical images, we improved the segmentation result and evaluated the performance through the proposed method.
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