Conventional radiography is the widely used imaging technology in diagnosing, monitoring, and prognosticating musculoskeletal (MSK) diseases because of its easy availability, versatility, and cost-effectiveness. Bone overlaps are prevalent in conventional radiographs, and can impede the accurate assessment of bone characteristics by radiologists or algorithms, posing significant challenges to conventional clinical diagnosis and computer-aided diagnosis. This work initiated the study of a challenging scenario - bone layer separation in conventional radiographs, in which separate overlapped bone regions enable the independent assessment of the bone characteristics of each bone layer and lay the groundwork for MSK disease diagnosis and its automation. This work proposed a Bone Layer Separation GAN (BLS-GAN) framework that can produce high-quality bone layer images with reasonable bone characteristics and texture. This framework introduced a reconstructor based on conventional radiography imaging principles, which achieved efficient reconstruction and mitigates the recurrent calculations and training instability issues caused by soft tissue in the overlapped regions. Additionally, pre-training with synthetic images was implemented to enhance the stability of both the training process and the results. The generated images passed the visual Turing test, and improved performance in downstream tasks. This work affirms the feasibility of extracting bone layer images from conventional radiographs, which holds promise for leveraging layer separation technology to facilitate more comprehensive analytical research in MSK diagnosis, monitoring, and prognosis.

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by joint inflammation and progressive structural damage. Joint space width (JSW) is a critical indicator in conventional radiography (CR) for evaluating disease progression, which has become a prominent research topic in computer-aided diagnostic (CAD) systems. However, deep learning-based radiological CAD systems for JSW analysis face significant challenges in data quality, including data imbalance, limited variety, and annotation difficulties. This work introduced a challenging image synthesis scenario and proposed Layer Separation Networks (LSN) to accurately separate the soft tissue layer, the upper bone layer, and the lower bone layer in conventional radiographs of finger joints. Using these layers, the adjustable JSW images can be synthesized to address data quality challenges and achieve ground truth (GT) generation. Experimental results demonstrated that LSN-based synthetic images closely resemble real radiographs, and significantly enhanced the performance in downstream tasks. The code and dataset are available at: https://github.com/pokeblow/LSN.

Presents corrections to the article "A Sub-Pixel Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritis".

Rheumatoid arthritis (RA) is a chronic autoimmune disease that primarily affects peripheral synovial joints, like fingers, wrists and feet. Radiology plays a critical role in the diagnosis and monitoring of RA. Limited by the current spatial resolution of radiographic imaging, joint space narrowing (JSN) progression of RA for the same reason above can be less than one pixel per year with universal spatial resolution. Insensitive monitoring of JSN can hinder the radiologist/rheumatologist from making a proper and timely clinical judgment. In this paper, we propose a novel and sensitive method that we call partial image phase-only correlation which aims to automatically quantify JSN progression in the early RA. The majority of the current literature utilizes the mean error, root-mean-square deviation and standard deviation to report the accuracy at pixel level. Our work measures JSN progression between a baseline and its follow-up finger joint images by using the phase spectrum in the frequency domain. Using this study, the mean error can be reduced to 0.0130 mm when applied to phantom radiographs with ground truth, and 0.0519 mm standard deviation for clinical radiography. With the sub-pixel accuracy far beyond usual manual measurements, we are optimistic that the proposed work is a promising scheme for automatically quantifying JSN progression.

The rising demand for high quality display has ensued active research in high dynamic range (HDR) imaging, which has the potential to replace the standard dynamic range imaging. This is due to HDR’s features like accurate reproducibility of a scene with its entire spectrum of visible lighting and color depth. But this capability comes with expensive capture, display, storage and distribution resource requirements. Also, display of HDR images/video content on an ordinary display device with limited dynamic range requires some form of adaptation. Many adaptation algorithms, widely known as tone mapping (TM) operators, have been studied and proposed in the last few decades. In this article, we present a comprehensive survey of 60 TM algorithms that have been implemented on hardware for acceleration and real-time performance. In this state-of-the-art survey, we will discuss those TM algorithms which have been implemented on GPU, FPGA, and ASIC in terms of their hardware specifications and performance. Output image quality is an important metric for TM algorithms. From our literature survey we found that, various objective quality metrics have been used to demonstrate the quality of those algorithms hardware implementation. We have compiled those metrics used in this survey, and analyzed the relationship between hardware cost, image quality and computational efficiency. Currently, machine learning-based (ML) algorithms have become an important tool to solve many image processing tasks, and this article concludes with a discussion on the future research directions to realize ML-based TM operators on hardware.

We present a fast global and locally adaptive tone mapping algorithm and its field-programmable gate array (FPGA) implementation. The specially designed tone mapping function, which is based on local histogram equalization, controls global, and local characteristics individually. In contrast to other tonemap operators, our algorithm manages light/dark halos separately and by using local tonemap function alone, it can effectively suppress noise. We validated the effectiveness of our algorithms using subjective and objective assessment. Using an average of the bins, we achieve fast smoothed local histogram estimation with fewer bins while maintaining high accuracy. Our new implementation method requires minimal data access and reduced memory as it operates with a downscaled frame size of 240 × 135 pixels. Relative local area size is 248 × 248 @Full-HD resolution (1920 × 1080). For low-latency pixel output, the system performs the tone mapping using pixel information from the previous frame. When we implemented the system on FPGA (TB-7K-325TIMG and Xilinx Kintex-7), we achieved lightweight hardware as the total usage rate is about 25% of the available FPGA resource. Using an online 1080p video we demonstrate, a real-time video processing using our hardware tone mapping system.

In this study, we propose an efficient stereo matching method which estimates sparse disparities using global phase only correlation (POC). Conventionally, cost functions are to be calculated for all disparity candidates and the associated computational cost has been impediment in achieving a realtime performance. Therefore, we consider to use full image 2D phase only correlation (FIPOC) for detecting the valid disparities candidates. This would require comparatively fewer calculations for the same number of disparities. Since, the FIPOC output indicates the disparity distribution of two stereo images, we can sort the disparity candidates and choose them for sparse calculation. In our proposed method, the searchable disparity range is half of the input image size, which is much wider than that of the conventional methods. When we apply the FIPOC to naive sum of absolute difference (SAD) stereo matching method, the combined algorithm would require fewer calculations while maintaining the same accuracy. In our evaluation, the proposed method achieves 194 disparity stereo matching in 70 ms on$398 \times 288$images without the need for SIMD instruction, multi-thread operation, or additional hardware while using a Intel Core i5-5257U.

To replicate human visual perception, we analyze processing images with optical illusion using edge preserving filters and smoothed local histogram equalization (LHE). Images with the optical illusions are good models for gradual/rapid changes in contrast and strong edges, which are good cases for assessing the robustness of image filters. Here, we study and analyze the performance of smoothed LHE filters while processing perceptual illusion. Our studies conclude that, smoothed LHEs are useful in retaining actual edge forms in these images as they can operate using large kernel sizes. These large kernel size filters can construct sawtooth like edge and it corresponds to adequately wide halos. We also demonstrate the usefulness of smoothed LHE like tone mapping techniques in preserving naturalness, and we confirmed it by performing subjective visual test.