Heterogeneous Computing for Vertebra Detection and Segmentation in X-Ray Images

X-ray image segmentation is an important issue in medical image analysis. Due to inconsistent X-ray absorption, the intensities are usually unevenly distributed and noisy in the processed organ, thus the object segmentation becomes difficult. In this paper we propose a new segmentation method for patella from the lateral knee X-ray images based on the active shape model (ASM). At first, a patella shape model is constructed by principal component analysis (PCA) of corresponding landmarks obtained from a set of training shape. As the knee X-ray image usually contains many anatomical structures, we design a strategy based on edge tracing to place the initial shape model as close to the patella boundary as possible. Then, the shape model is deformed and fitted to the patella boundary by using a dual-optimization approach that includes a genetic algorithm (GA) to get the global geometric transform and ASM to deform the shape model iteratively. Consequently, the proposed method can cope with different knee X-ray images and can segment the patella in an automatic procedure. In the experiment, 20 images were tested and promising results are obtained by the proposed method. This method is found useful for the clinical evaluation and biomechanical study of knee.

Image and video processing algorithms present a necessary tool for various domains related to computer vision such as medical applications, pattern recognition and real time video processing methods. The performance of these algorithms have been severely hampered by their high intensive computation since the new video standards, especially those in high definitions require more resources and memory to achieve their computations. In this paper, we propose a new framework for multimedia (single image, multiple images, multiple videos, video in real time) processing that exploits the full computing power of heterogeneous machines. This framework enables to select firstly the computing units (CPU or/and GPU) for processing, and secondly the methods to be applied depending on the type of media to process and the algorithm complexity. The framework exploits efficient scheduling strategies, and allows to reduce significantly data transfer times thanks to an efficient management of GPU memories and to the overlapping of data copies by kernels executions. Otherwise, the framework includes several GPU-based image and video primitive functions, such as silhouette extraction, corners detection, contours extraction, sparse and dense optical flow estimation. These primitives are exploited in different applications such as vertebra segmentation in X-ray and MR images, videos indexation, event detection and localization in multi-user scenarios. Experimental results have been obtained by applying the framework on different computer vision methods showing a global speedup ranging from 5 to 100, by comparison with sequential CPU implementations.

We propose a new method for fully-automatic landmark detection and shape segmentation in X-ray images. Our algorithm works by estimating the displacements from image patches to the (unknown) landmark positions and then integrating them via voting. The fundamental contribution is that, we jointly estimate the displacements from all patches to multiple landmarks together, by considering not only the training data but also geometric constraints on the test image. The various constraints constitute a convex objective function that can be solved efficiently. Validated on three challenging datasets, our method achieves high accuracy in landmark detection, and, combined with statistical shape model, gives a better performance in shape segmentation compared to the state-of-the-art methods.

Interactive defect segmentation in X-Ray images based on deep learning

X-ray imaging has been broadly adopted as a nondestructive testing method for product quality inspection. Deep learning has demonstrated powerful image scene understanding capabilities. In this article, U-Net with resnet101 is taken as the baseline for defect segmentation. First, there exist gray inhomogeneous and low-contrast regions in X-ray images, which can hardly be segmented. Contrast-limited adaptive histogram equalization (CLAHE) could be used to improve the contrast and consistency of the X-ray image. A two-stream convolutional neural network (CNN) is proposed that takes the original image and CLAHE processed image as inputs to address this issue. And then in CNN, low-level feature maps are lacking semantic information, which may lead to worse results. A gated multilayer fusion module is proposed to adaptively fuse the high-level features into low-level features. Furthermore, loss functions (such as cross entropy) in semantic segmentation are usually pixel level, ignoring the regional information. A weighted intersection over union (IOU) loss function is proposed to introduce IOU information to guide the model to focus on the objects that are easy to mine. The experimental results prove that the three proposed methods have better performance than the baseline for our dataset, achieving 42.2 in mIoU, 59.2 in Dice, and 54.5%, 74.9%, and 86.3% in small, middle, and large object recall rate, respectively.

A Cascade Method based on UNet3+ for Automated Vertebrae Segmentation in X-ray Images

Revision joint replacements are challenging surgical tasks. Knowing the exact type of primary prosthesis is essential to avoid long preoperative organisation, long operation times, and especially loss of bone and soft-tissue during operation. In daily routine there is often no information about the primary prosthesis. We are developing methods for identifying implanted prostheses from x-ray images by means of matching template images generated from prosthesis CAD data. The application is separated into three major components: The "Template Image Generation" adds 3d models of endoprostheses to a database. The "X-ray Image Segmentation" extracts endoprostheses from provided sets of x-ray images. The "Template Matching" finds the best matching prosthesis types in the data base. At the current stage, one prosthesis model (Corin, Knee ProthesisUniglide) was used for evaluating these algorithms. Very accurate identifications with accuracies of about 90% for lateral and over 70% for frontal images could be achieved. The current results of this feasibility study are very promising. A reliable and fast prosthesis identification process seems realistic to support the surgeon when planning and performing revision arthroplasty. Further improvements of segmentation accuracies and extending the prosthesis data base are intended next steps towards this goal.

In this paper, we propose a method for automatic determination of position and orientation of spine in digitized spine X-rays using mathematical morphology. As the X-ray images are usually highly smeared, vertebrae segmentation is a complex process. The image is first coarsely segmented to obtain the location and orientation information of the spine. The state-of-the-art technique is based on the deformation model of a template, and as the vertebrae shape usually shows variation from case to case, accurate representation using a template is a difficult process. The proposed method makes use of the vertebrae morphometry and gray-scale profile of the spine. The top-hat transformation-based method is proposed to enhance the ridge points in the posterior boundary of the spine. For cases containing external objects such as ornaments, H-Maxima transform is used for segmentation and removal of these objects. The Radon transform is then used to estimate the location and orientation of the line joining the ridge point clusters appearing on the boundary of the vertebra body. The method was validated for 100 cervical spine X-ray images, and in all cases, the error in orientation was within the accepted tolerable limit of 15 degrees. The average error was found to be 4.6 degrees. A point on the posterior boundary was located with an accuracy of +/-5.2 mm. The accurate information about location and orientation of the spine is necessary for fine-grained segmentation of the vertebrae using techniques such as active shape modeling. Accurate vertebrae segmentation is needed in successful feature extraction for applications such as content-based image retrieval of biomedical images.

Category-fragment Segmentation Framework for Pelvic Fracture Segmentation in X-ray Images

In recent years, much research has been focused on dental problem classification, detection, and segmentation using Deep Convolutional Neural Networks (CNN or DCNN) models. Early detection of oral issues is crucial to maintain oral health and prevent the need for invasive treatments. Dental radiographs assist in this by providing detailed images. Integrating intelligent methods with conventional imaging techniques can result in more accurate diagnoses and treatment plans for patients, dental professionals, and service providers. Our study presents a novel unified method that utilizes CNNs to segment dental restorations and cavities in X-ray images. Various datasets, including publicly available one annotated in this study, were used to evaluate the method’s performance. The first step in our method is to detect teeth, identifying each tooth’s number and location in accordance with the Universal Teeth Numbering (UTN) system. This study has also presented a new post-processing technique to address teeth ordering predicting issues in X-ray images. The detection model achieved high precision and recall metrics of over 90% after post-processing, outperforming existing methods. The second step aims to crop images of each tooth from the initial network and precisely segment the cavity and restoration. The segmentation model achieved high precision and recall of over 60%. We proportionately projected the predicted outcomes onto the original image for the final evaluation. The overall performance of our proposed method exceeded that of state-of the-art models. This method detects abnormalities that may go unnoticed by humans, reducing workload and minimizing bias in treatment assessment.

The subject of research in the article is the effectiveness of adaptive methods of preprocessing of medical images, in particular adaptive bilateral filter and modified CLAHE, in the tasks of segmentation of dental X-ray images. These methods make it possible to preserve important image details and effectively reduce noise, even in cases of high variability of images coming from different sources. The goal of the work is to study the impact of adaptive preprocessing methods on increasing the accuracy of segmentation of medical images and to determine the optimal combination of methods that provide the best results in segmentation tasks. The article addresses the following tasks: experimental comparison of adaptive preprocessing methods with traditional approaches, evaluation of segmentation efficiency using metrics such as Dice Score, Jacquard Coefficient (IoU Score), Precision and Sensitivity/Completeness (Recall)), as well as analysis of the effect of pre-processing on the quality of segmentation. The following methods are used: mathematical modeling, neural network training based on the U-Net model with a pre-trained timm-resnest101e encoder, image scaling to 512x512 pixels, training with a fixed learning rate of 0.001. The following results were obtained: the combined use of the adaptive bilateral filter and the modified CLAHE provided the highest segmentation quality indicators (Dice Score 0.9603 and Jacquard Coefficient (IoU Score) 0.94501), surpassing traditional methods. This proves the advantage of adaptive approaches in preserving the contours of objects and reducing noise. Conclusions: the application of adaptive preprocessing methods significantly improves the accuracy of segmentation of medical images. The combined approach including the adaptive bilateral filter and the modified CLAHE is the most effective for medical imaging tasks, which increases the accuracy of diagnosis and the reliability of automated decision support systems.

Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation

Automating the process of analysis of Panoramic X-Ray images is important to help dentist procedures and diagnosis. Tooth segmentation from the radiographic images and feature extraction are essential steps. The authors propose a segmentation approach based on mathematical morphology, quadtree decomposition for mask generation, thresholding, and snake models. The feature extraction stage is steered by a shape model based on Principal Component Analysis (PCA). First, the authors take the quadtree decomposition of a low-pass version of the original image and select the smallest blocks to generate a mask. Then, the original image is processed by Otsu's thresholding. The result is improved by morphological operators and the quadtree mask is applied to address overlapping, a common problem in X-ray images. The obtained regions are searched and the larger ones are selected to find tooth candidates. The boundary of the obtained regions are extracted and aligned with the shape model in order to recognize the target tooth (molar). The selected curve is used in a search method to initialize a snake technique. Finally, morphometric data extraction is performed to obtain tooth measurements for dentist diagnosis. Experiments show the advantages of the proposed method to extract teeth from X-Ray images and discuss its drawbacks.

Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images

A deep unsupervised saliency model for lung segmentation in chest X-ray images