Article

Image Diagnosis for Coded Defect Detection on Multi-view 3D Images

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Abstract

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.

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... As previous study, in the case of occurring coded defect by H.265/HEVC with all or certain viewpoints of multi-view 3D CG image, we diagnosed this image automatically by computer, and then we assessed and estimated quantitatively from point of view of the coded image quality how coded defect is enable to detect [6]. As a result, in the case of occurring the coded defect with all or certain viewpoints, we found knowledge that it is possible to detect from the point of view of coded image quality. ...
... After that, we calculate color difference ∆ 00 by using CIEDE2000. (6). We process all patterns and compare each pattern. ...
... On the other hand, focused on "Fair (Fair Detection)", PSNR in = 20, 25 is satisfied. From knowledge of [6], for 3D CG images, focused on better than "Fair Detection", in the case of = , 20, 1/2 of all patterns, in the case of = 25, 30, 2/3 of all patterns, in the case of = 35, 40, 51, all, are satisfied. From this, it is easy for the coded degradation to perceive objectively since for laparoscopic image used in this study, there are many patterns in the case of less than 40 dB. ...
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... This is one of the importance component for not only broadcasting field but also medical image diagnosis. 1 If the image contents used in this study can be acquired 4K quality from starting, it is better, however, there is not always condition such as this. Therefore, it needs to improve image quality by contrast enhancement or super-resolution as preprocessing. ...
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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.
... As our previous works, first, we studied on medical image diagnosis including automatic detection of coded defect information in the laparoscopic images. We tried to assess objectively such as Peak Signal to Noise Ratio (PSNR) [1], classification method such as Support Vector Machine (SVM). On the other hand, we tried to analyze color information by comparing to measurement values such as luminosity and color difference by S-CIELAB color space. ...
... In this section, we describe for related work focused on the following. (1). Region segmentation in medical imaging field (Section 2.1) (2). ...
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