Pixel2point: 3D object reconstruction from a single image using CNN and initial sphere

3D reconstruction from a single image has many useful applications. However, it is a challenging and ill-posed problem as various candidates can be a solution for the reconstruction. In this paper, we propose a simple, yet powerful, CNN model that generates a point cloud of an object from a single image. 3D data can be represented in different ways. Point clouds have proven to be a common and simple representation. The proposed model was trained end-to-end on synthetic data with 3D supervision. It takes a single image of an object and generates a point cloud with a fixed number of points. An initial point cloud of a sphere shape is used to improve the generated point cloud. The proposed model was tested on synthetic and real data. Qualitative evaluations demonstrate that the proposed model is able to generate point clouds that are very close to the ground-truth. Also, the initial point cloud has improved the final results as it distributes the points on the object surface evenly. Furthermore, the proposed method outperforms the state-of-the-art in solving this problem quantitatively and qualitatively on synthetic and real images. The proposed model illustrates an outstanding generalization to the new and unseen images and scenes.TU Berlin, Open-Access-Mittel – 202

Role of image contrast enhancement technique for ophthalmologist as diagnostic tool for diabetic retinopathy

Analysing the retinal colour fundus is a critical step before any proposed computerised automatic detection of eye disease, especially Diabetic Retinopathy (DR). The retinal colour fundus image contains noise and varying low contrast of the blood vessel against its surrounding background. It makes it difficult to analyse the proper order of the vessel's network for detecting DR disease progress. The invasive method Fluorescein Angiogram Fundus (FFA) resolves these problems, but is not recommended due to an agent injection that leads to other side effects on the patient's health, in the worst cases death. In this research work, we propose a new image enhancement method based on a morphological operation along with proposed threshold based stationary wavelet transform for retinal fundus images and Contrast Limited Adaptive Histogram Equalisation (CLAHE) for the vessel enhancement. The experimental results show much better results than the FFA images. Experimental results are based on three databases of retinal colour fundus images and FFA images. The performance is evaluated by measuring the contrast enhancement factor of retinal colour fundus images and FFA images. The results show that the proposed image enhancement method is superior to other non-invasive image enhancement methods as well as invasive methods, thus it will play an important role in imaging retinal blood vessels. An average contrast improvement factor of 5.63 on colour fundus images is achieved as well as 5.57 on FFA images. This significant contribution to the enhancement of the contrast of retinal colour fundus will be one primary tool to reduce the use of an invasive method.8 page(s

Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research

Since early 2020, the whole world has been facing the deadly and highly contagious disease named coronavirus disease (COVID-19) and the World Health Organization declared the pandemic on 11 March 2020. Over 23 million positive cases of COVID-19 have been reported till late August 2020. Medical images such as chest X-rays and Computed Tomography scans are becoming one of the main leading clinical diagnosis tools in fighting against COVID-19, underpinned by Artificial Intelligence based techniques, resulting in rapid decision-making in saving lives. This article provides an extensive review of AI-based methods to assist medical practitioners with comprehensive knowledge of the efficient AI-based methods for efficient COVID-19 diagnosis. Nearly all the reported methods so far along with their pros and cons as well as recommendations for improvements are discussed, including image acquisition, segmentation, classification, and follow-up diagnosis phases developed between 2019 and 2020. AI and machine learning technologies have boosted the accuracy of Covid-19 diagnosis, and most of the widely used deep learning methods have been implemented and worked well with a small amount of data for COVID-19 diagnosis. This review presents a detailed mythological analysis for the evaluation of AI-based methods used in the process of detecting COVID-19 from medical images. However, due to the quick outbreak of Covid-19, there are not many ground-truth datasets available for the communities. It is necessary to combine clinical experts’ observations and information from images to have a reliable and efficient COVID-19 diagnosis. This paper suggests that future research may focus on multi-modality based models as well as how to select the best model architecture where AI can introduce more intelligence to medical systems to capture the characteristics of diseases by learning from multi-modality data to obtain reliable results for COVID-19 diagnosis for timely treatment

Automatic retinal vessel extraction algorithm based on contrast-sensitive schemes

Retinal vessel segmentation plays a key role in the detection of numerous eye diseases, and its reliable computerised implementation becomes important for automatic retinal disease screening systems. A large number of retinal vessel segmentation algorithms have been reported, primarily based on three main steps including making the background uniform, second-order Gaussian detector application and finally the region-grown bi-narization. Although these methods improve the accuracy levels, their sensitivity to low-contrast vessels still needs attention. In this paper, some contrast-sensitive approaches are discussed that once embedded in the conventional algorithm results in improved sensitivity for a given retinal vessel extraction technique. The impact of these add-on modules is assessed on publicly available databases like DRIVE and STARE and found to provide promising results.5 page(s

Impact of Retinal Vessel Image Coherence on Retinal Blood Vessel Segmentation

Retinal vessel segmentation is critical in detecting retinal blood vessels for a variety of eye disorders, and a consistent computerized method is required for automatic eye disorder screening. Many methods of retinal blood vessel segmentation are implemented, but these methods only yielded accuracy and lack of good sensitivity due to the coherence of retinal blood vessel segmentation. Another main factor of low sensitivity is the proper technique to handle the low-varying contrast problem. In this study, we proposed a five-step technique for assessing the impact of retinal blood vessel coherence on retinal blood vessel segmentation. The proposed technique for retinal blood vessels involved four steps and is known as the preprocessing module. These four stages of the pre-processing module handle the retinal image process in the first stage, uneven illumination and noise issues using morphological operations in the second stage, and image conversion to grayscale using principal component analysis (PCA) in the third step. The fourth step is the main step of contributing to the coherence of retinal blood vessels using anisotropic diffusion filtering and testing their different schemes and get a better coherent image on the optimized anisotropic diffusion filtering. The last step included double thresholds with morphological image reconstruction techniques to produce a segmented image of the vessel. The performances of the proposed method are validated on the publicly available database named DRIVE and STARE. Sensitivity values of 0.811 and 0.821 on STARE and DRIVE respectively meet and surpass other existing methods, and comparable accuracy values of 0.961 and 0.954 on STARE and DRIVE databases to existing methods. This proposed new method for retinal blood vessel segmentations can help medical experts diagnose eye disease and recommend treatment in a timely manner

Comparative study of the results obtained by our proposed method and those of Nguyen et al. [49], Hou [50], and Zhao et al. [51] is presented.

Fig (a) shows the result obtained by Nguyen, while Fig (b) shows the result obtained by Hou. Fig (c) and (d) show the results obtained by FR and IUWT-based Zhao, respectively. Fig (e) presents the results obtained by our proposed method, and Fig (h) shows the ground truth image.</p