Eurasip Journal on Image and Video Processing最新文献

The autoimmune disorders such as rheumatoid, arthritis, and scleroderma are connective tissue diseases (CTD). Autoimmune diseases are generally diagnosed using the antinuclear antibody (ANA) blood test. This test uses indirect immune fluorescence (IIf) image analysis to detect the presence of liquid substance antibodies at intervals the blood, which is responsible for CTDs. Typically human alveolar epithelial cells type 2 (HEp2) are utilized as the substrate for the microscope slides. The various fluorescence antibody patterns on HEp-2 cells permits the differential designation-diagnosis. The segmentation of HEp-2 cells of IIf images is therefore a crucial step in the ANA test. However, not only this task is extremely challenging, but physicians also often have a considerable number of IIf images to examine.In this study, we propose a new methodology for HEp2 segmentation from IIf images by maximum modified quantum entropy. Besides, we have used a new criterion with a flexible representation of the quantum image(FRQI). The proposed methodology determines the optimum threshold based on the quantum entropy measure, by maximizing the measure of class separability for the obtained classes over all the gray levels. We tested the suggested algorithm over all images of the MIVIA HEp 2 image data set.To objectively assess the proposed methodology, segmentation accuracy (SA), Jaccard similarity (JS), the F1-measure,the Matthews correlation coefficient(MCC), and the peak signal-to-noise ratio (PSNR) were used to evaluate performance. We have compared the proposed methodology with quantum entropy, Kapur and Otsu algorithms, respectively.The results show that the proposed algorithm is better than quantum entropy and Kapur methods. In addition, it overcomes the limitations of the Otsu method concerning the images which has positive skew histogram.This study can contribute to create a computer-aided decision (CAD) framework for the diagnosis of immune system diseases

Depth is essential information for autonomous robotics applications that need environmental depth values. The depth could be acquired by finding the matching pixels between stereo image pairs. Depth information is an inference from a matching cost volume that is composed of the distances between the possible pixel points on the pre-aligned horizontal axis of stereo images. Most approaches use matching costs to identify matches between stereo images and obtain depth information. Recently, researchers have been using convolutional neural network-based solutions to handle this matching problem. In this paper, a novel method has been proposed for the refinement of matching costs by using recurrent neural networks. Our motivation is to enhance the depth values obtained from matching costs. For this purpose, to attain an enhanced disparity map by utilizing the sequential information of matching costs in the horizontal space, recurrent neural networks are used. Exploiting this sequential information, we aimed to determine the position of the correct matching point by using recurrent neural networks, as in the case of speech processing problems. We used existing stereo algorithms to obtain the initial matching costs and then improved the results by utilizing recurrent neural networks. The results are evaluated on the KITTI 2012 and KITTI 2015 datasets. The results show that the matching cost three-pixel error is decreased by an average of 14.5% in both datasets.