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

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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. ...
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.
... 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. ...
... In this paper, for color laparoscopic frame image cut from surgical video under laparoscopy, we carried out processing contrast enhancement using appropriate parameter obtained our previous study. 1,2,4,5 And then, in the case of processing SRCNN by image regions, we discussed comparing among PSNR, SSIM, and texture feature for contrast whether we are able to estimate and divide image regions or not. ...
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). ...
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.
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The emerging need for the current medical devices to achieve immediate visualization and performing diagnostic imaging at real time augurs the demand for high computational power of the associated electronic circuitry. The demand for such a high computational requirement is often met by using software methods to accelerate the computation which is possible only to a certain extent, impairing the feasibility of real time imaging and diagnosis. In this paper, a new method of using Digital Signal Processors (DSP) with a specialized Pipelined Vision Processor (PVP) embedded at the hardware level to accelerate the routinely time consuming imaging computation is proposed and validated. A lab prototype is built for the feasibility study and clinical validation of the proposed technique. This unique architecture of the PVP in a dual core Digital Signal Processor offers a high performance accelerated framework along with it’s large on chip memory resources and reduced bandwidth requirement provides as an ideal architecture for reliable medical computational needs. We have taken two sets sample studies from SPECT for validation – 27 cases of Thyroid Medical History and 20 cases of Glomerular Filtration Rate of Kidneys. The results were compared to definitive post scan SIEMENS image analysis software. From the statistical results, it is clearly shown that, this method achieved very superior accuracy and 250% acceleration of computational speed. OAPA
Conference Paper
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Lossy image compression is increasingly used in medical applications , but great care must be taken to ensure that no diagnostically relevant features are altered. Guidelines based on compression ratios are often use to mitigate this issue, but are criticized due to the considerable compressibility variations between images. Objective image quality assessment metrics should be used instead, but the most common, mean squared error, is known to be poorly correlated with our perception of quality. Structural similarity (SSIM) is probably currently the most popular alternative, but it is also increasingly criticized. Using computed tomography simulations, this paper shows some of the limitations of SSIM when used with medical images : uniform pooling, distortion underestimation near hard edges, instabilities in regions of low variance and insensitivity in regions high intensities. Furthermore, this paper demonstrates the effect of these limitations when SSIM is used to bound compression in a block coder such as JPEG 2000.
In this paper, we propose a novel medical image segmentation using iterative deep learning framework. We have combined an iterative learning approach and an encoder-decoder network to improve segmentation results, which enables to precisely localize the regions of interest (ROIs) including complex shapes or detailed textures of medical images in an iterative manner. The proposed iterative deep convolutional encoder-decoder network consists of two main paths: convolutional encoder path and convolutional decoder path with iterative learning. Experimental results show that the proposed iterative deep learning framework is able to yield excellent medical image segmentation performances for various medical images. The effectiveness of the proposed method has been proved by comparing with other state-of-the-art medical image segmentation methods.
The way of clinical image viewing has changed dramatically by deploying picture archiving and communication system (PACS) in clinical practices. This change has been caused by the digitization of clinical diagnostic images and based on digital imaging and communications in medicine (DICOM) 3.0 standard published in 1992. In the PACS environment, medical displays such as liquid crystal display (LCD) monitors are applying for soft-copy reading. These changes in the way of the clinical image viewing caused by the PACS resulted in development of a new concept of the clinical image quality. In digital radiography, there are various factors that affect the image quality in the PACS environment. In addition, image quality can practically be classified into 3 classes; Raw data image class, Processed image class, and Display image class. Therefore, we must apply appropriate procedure to evaluate the image quality of digital radiography.
Previously, it is not obvious to what extent was accepted for the assessors when we see the 3D image (including multi-view 3D) which the luminance change may affect the stereoscopic effect and assessment generally. We think that we can conduct a general evaluation, along with a subjective evaluation, of the luminance component using both the S-CIELAB color space and CIEDE2000. In this study, first, we performed three types of subjective evaluation experiments for contrast enhancement in an image by using the eight viewpoints parallax barrier method. Next, we analyzed the results statistically by using a support vector machine (SVM). Further, we objectively evaluated the luminance value measurement by using CIEDE2000 in the S-CIELAB color space. Then, we checked whether the objective evaluation value was related to the subjective evaluation value. From results, we were able to see the characteristic relationship between subjective assessment and objective assessment.
Recently, the use of 3D video systems without glasses has increased, and therefore 3D image quality and presence evaluation is important. There are various stereo-logical image quality evaluation methods for multi-view 3D systems without glasses. However, there is no uniform method for evaluating 3D video systems. In this study, we focus on camera interval and JPEG coding degradation with a multi-view 3D system. Previously, many studies have examined camera interval or JPEG coding degradation with 3D glasses or the binocular method. In such systems, viewers perceive stereoscopic and depth effects. Moreover, they can see from different angles, increasing viewpoints with multi-view 3D systems. However, viewers feel discomfort when changing their viewpoint. Hence, we consider, in particular, the accommodation of the camera interval and JPEG coding degradation while changing viewpoints. We have performed subjective evaluations using the absolute category rating system to assess the effects of changing the camera interval of 3D CG images or video content using an 8 viewpoint lenticular lens method. We measure assessors' ability to identify the degree of the camera interval. We analyze the results of our subjective evaluations statistically and discuss the results. Using the optimal camera interval, we perform a subjective quality evaluation employing the double stimulus impairment scale to determine assessors' ability to identify JPEG coding degradation by degree. The experimental results of this subjective evaluation are also statistically analyzed.
Many previous studies on image quality assessment of 3D still images or video clips have been conducted. In particular, it is important to know the region in which assessors are interested or on which they focus in images or video clips, as represented by the ROI (Region of Interest). For multi-view 3D images, it is obvious that there are a number of viewpoints; however, it is not clear whether assessors focus on objects or background regions. It is also not clear on what assessors focus depending on whether the background region is colored or gray scale. Furthermore, while case studies on coded degradation in 2D or binocular stereoscopic videos have been conducted, no such case studies on multi-view 3D videos exist, and therefore, no results are available for coded degradation according to the object or background region in multi-view 3D images. In addition, in the case where the background region is gray scale or not, it was not revealed that there were affection for gaze point environment of assessors and subjective image quality. In this study, we conducted experiments on the subjective evaluation of the assessor in the case of coded degradation by JPEG coding of the background or object or both in 3D CG images using an eight viewpoint parallax barrier method. Then, we analyzed the results statistically and classified the evaluation scores using an SVM.
Digital formatted imaging examinations are considered and their advantages over conventional methods emphasized. One display technique, the three-dimensional rendering of solid surfaces of multiple objects, is examined, and a large number of Computerized Tomography (CT) data applications is completed, where the heuristic reconstruction algorithm performs on a visual par with the optimal algorithm of Fuchs et al. The optimal algorithm, however, is much slower than the heuristic algorithm; furthermore, it requires much greater storage. The quantitative aspects of these algorithms, namely the polyhedral surface area and volume, show virtually no difference in the many cases in which comparisons are made.
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