A Study on Medical Virtual Reality, Medical Imaging, and Diagnostic Imaging System

As one of image pre-processing method to detect, recognize, and estimate lesion or characteristic region in medical image processing, there are many studies improved performance and precision of processing by contrast enhancement or super-resolution. However, it is not clarified how condition is better to apply these methods. Therefore, we experimented and discussed on affect for color laparoscopic image quality by the difference of contrast enhancement method. As a result, we obtained knowledge of high similarity among patterns of adaptive histogram equalization in three methods. However, under these conditions, in the case of considering the region segmentation, it is not clarified how processing precision is better. In this paper, first we processed the contrast enhancement for the color laparoscopic frame image cut from surgery video under laparoscopy. Next, we processed super-resolution for generated image. Finally, we compared and discussed by Peak Signal to Noise Ratio (PSNR), Structural SIMilarity (SSIM), and texture features for contrast.

In medical images, since there are body region and border that it is hard for medical worker to distinguish by the only image diagnosis, we estimate that the progress of work, time, and emergency are needed. Therefore, it is problem of emergency to develop the medical information system enable to support medical workers by using high performance computer. In this study, first, we carried out texture analysis for region of laparoscopic image. Next, based on results, we experimented whether region segmentation of laparoscopic image is possible or impossible. Finally, we discussed how each texture features are affected to region segmentation.

In this paper, first, we generated medical images cut as frame still image from laparoscopic video acquired by endoscopy. Using these images, we processed to encode and decode by H.265/HEVC in certain image regions, and we generated evaluation images. Next, we evaluated objectively seeing from the coded image quality by using PSNR (Peak Signal to Noise Ratio), considering the automatic detection of coded defect region information. Furthermore, we analyzed for color information by measuring both the luminance using S-CIELAB color space and the color difference using CIEDE2000. Finally, we try to classify effectively using Support Vector Machine (SVM), and we discussed including the automatic detection of coded defect region information whether it is possible for application of medical image diagnosis or not.

Up to now, the diagnostic imaging was carried out based on manual handling by professional doctors and health care workers. However, it is enable to diagnose images automatically by development of the computer systems. Therefore, it is required for approach from information science and engineering fields. For generating multi-view 3D images, if we are able to support whether workers are able to use correctly or not, and if the coded defect detection and restoration for their images are possible, we consider possibility towards medical applications in the near future. In this paper, first, we diagnosed automatically for multi-view 3D images in the case of occurring defect by encoded and decoded degradation at all or certain viewpoints by H.265/HEVC. Next, we assessed and estimated quantitatively in terms of the coded image quality in order to clarify how we are able to detect the coded defect.

Up to now, the diagnostic imaging was carried out based on manual handling by professional doctors and health care workers. However, it is enable to diagnose images automatically by development of the computer systems. Therefore, it is required for approach from information science and engineering fields. For generating multi-view 3D images, if we are able to support whether workers are able to use correctly or not, and if the coded defect detection and restoration for their images are possible, we consider possibility towards medical applications in the near future. In this paper, first, we diagnosed automatically for multi-view 3D images in the case of occurring defect by encoded and decoded degradation at all or certain viewpoints by H.265/HEVC. Next, we assessed and estimated quantitatively in terms of the coded image quality in order to clarify how we are able to detect the coded defect.