Graph-Based Rate Control in Pathology Imaging With Lossless Region of Interest Coding

Sánchez, Vı́ctor; Hernández-Cabronero, Miguel

doi:10.1109/tmi.2018.2824819

Cited by 14 publications

(8 citation statements)

References 45 publications

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“…Thus, these regions can be coded with fewer bit rates. Sanchez and Hernández-Cabronero [16] present a novel graph-based rate control method for ROI coding in the pathology image. Their approach is designed for block-based predictive transform coding methods, by compressing the non-ROI with a lossy method while ROI with a lossless method.…”

Section: B Medical Image or Video Codingmentioning

confidence: 99%

Perceptual-Based HEVC Intra Coding Optimization Using Deep Convolution Networks

Sun

Zuo

et al. 2019

IEEE Access

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In this paper, we propose a novel perceptual-based intra coding optimization algorithm for the High Efficiency Video Coding (HEVC) using deep convolution networks (DCNs). According to the saliency map, the algorithm can intelligently adjust bit rate allocation between the salient and non-salient regions of the video. The proposed strategy mainly consists of two techniques, saliency map extraction, and intelligent bit rate allocation. First, we train a DCN model to generate the saliency map that highlights semantically salient regions. Compared with the texture-based region of interest (ROI) extraction techniques, our model is more consistent with the human visual system (HVS). Second, based on the saliency map, a modified rate-distortion optimization (RDO) method is designed to adaptively adjust bit rate allocation. As a result, the quality of the salient regions will be improved by allocating more bits while allocating fewer bit rates for the non-salient regions. The experimental results demonstrate that our approach can deal with multiple types of video to enhance the visual experience. For conventional videos, the proposed method achieves 0.64-dB PSNR improvement for the salient regions and saves 3.02% bit rate on average compared with HM16.7. Moreover, for conversational videos, the proposed method can significantly reduce the bit rate by 8.65% without dropping the quality of important regions.

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Section: B Medical Image or Video Codingmentioning

confidence: 99%

Perceptual-Based HEVC Intra Coding Optimization Using Deep Convolution Networks

Sun

Zuo

et al. 2019

IEEE Access

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“…WSI scanners generate high resolution (submicron) images that can aid in the diagnosis of disease by permitting convenient visualization and navigation of tissue slide images. In clinical pathology departments, hundreds to thousands of tissue slides are processed each day and the number of cases has been steadily increasing, creating a challenging workload for digital pathology [1].…”

Section: Introductionmentioning

confidence: 99%

Focus Quality Assessment of High-Throughput Whole Slide Imaging in Digital Pathology

Hosseini

Brawley-Hayes

Zhang

et al. 2020

IEEE Trans. Med. Imaging

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One of the challenges facing the adoption of digital pathology workflows for clinical use is the need for automated quality control. As the scanners sometimes determine focus inaccurately, the resultant image blur deteriorates the scanned slide to the point of being unusable. Also, the scanned slide images tend to be extremely large when scanned at greater or equal 20X image resolution. Hence, for digital pathology to be clinically useful, it is necessary to use computational tools to quickly and accurately quantify the image focus quality and determine whether an image needs to be re-scanned. We propose a no-reference focus quality assessment metric specifically for digital pathology images, that operates by using a sum of evenderivative filter bases to synthesize a human visual system-like kernel, which is modeled as the inverse of the lens' point spread function. This kernel is then applied to a digital pathology image to modify high-frequency image information deteriorated by the scanner's optics and quantify the focus quality at the patch level. We show in several experiments that our method correlates better with ground-truth z-level data than other methods, and is more computationally efficient. We also extend our method to generate a local slide-level focus quality heatmap, which can be used for automated slide quality control, and demonstrate the utility of our method for clinical scan quality control by comparison with subjective slide quality scores.Index Terms-no-reference focus quality assessment, whole slide imaging, digital pathology, out-of-focus heatmap, human visual system, MaxPol derivative library Routine diagnosis of pathology slides requires high quality highthroughput images, which are directly affected by the dynamic environment of the physical optics and sensor electronics of the scanner [2]-[4].

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“…Coded targets are widely applied in the fields of machine vision, photogrammetry, augmented reality, and 3D reconstruction, as their unique identity information allows automatic identification and robust image matching. Coded targets can be classified into ring-shaped coded targets [1][2][3][4][5], point coded targets [6][7][8], and specially designed coded targets [9][10][11][12][13][14]. A ring-shaped coded target has a circular structure, mainly composed of a circular target at the center and coded bits surrounding it in a concentric ring.…”

Section: Introductionmentioning

confidence: 99%

Design of Chinese Character Coded Targets for Feature Point Recognition Under Motion-Blur Effect

Shi

Zhang

2020

IEEE Access

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Coded targets have been widely used as a type of active visual feature points in fields such as close-range photogrammetry, robot navigation, 3D reconstruction, and augmented reality. However, coded targets in degraded images, such as images with motion blur effect, are hard to recognize. To this end, a set of novel Chinese character coded targets (CCTs) is designed and tested. A CCT is a square visual marker, shown as a relative small circular feature overlaid in the middle of a square Chinese character. A white circular ring is embedded in a black circle concentrically to serve as the circular feature, which facilitates extraction of the center point of the marker for localization. Whereas the distinctive peripheral Chinese character appearance of each CCT is utilized for identification. By synthesizing simulated CCTs with different degrees of motion blur and various postures with the real background images, a Faster Regionbased Convolutional Neural Network (Faster R-CNN) is trained to locate and recognize the CCTs in motion blurred images. Experimental results on both artificial and actual motion blurred images demonstrate the superiorities of the designed CCTs as well as the proposed localization and recognition pipeline.

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Graph-Based Rate Control in Pathology Imaging With Lossless Region of Interest Coding

Cited by 14 publications

References 45 publications

Perceptual-Based HEVC Intra Coding Optimization Using Deep Convolution Networks

Perceptual-Based HEVC Intra Coding Optimization Using Deep Convolution Networks

Focus Quality Assessment of High-Throughput Whole Slide Imaging in Digital Pathology

Design of Chinese Character Coded Targets for Feature Point Recognition Under Motion-Blur Effect

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