Journal of X-Ray Science and Technology - Volume 29, issue 4

Authors: Qi, Yanjie | Yang, Zehui | Kang, Lin

Article Type: Research Article

Abstract: Due to the limitation of dynamic range of the imaging device, the fixed-voltage X-ray images often produce overexposed or underexposed regions. Some structure information of the composite steel component is lost. This problem can be solved by fusing the multi-exposure X-ray images taken by using different voltages in order to produce images with more detailed structures or information. Due to the lack of research on multi-exposure X-ray image fusion technology, there is no evaluation method specially for multi-exposure X-ray image fusion. For the multi-exposure X-ray fusion images obtained by different fusion algorithms may have problems such as the detail loss …and structure disorder. To address these problems, this study proposes a new multi-exposure X-ray image fusion quality evaluation method based on contrast sensitivity function (CSF) and gradient amplitude similarity. First, with the idea of information fusion, multiple reference images are fused into a new reference image. Next, the gradient amplitude similarity between the new reference image and the test image is calculated. Then, the whole evaluation value can be obtained by weighting CSF. In the experiments of MEF Database, the SROCC of the proposed algorithm is about 0.8914, and the PLCC is about 0.9287, which shows that the proposed algorithm is more consistent with subjective perception in MEF Database. Thus, this study demonstrates a new objective evaluation method, which generates the results that are consistent with the subjective feelings of human eyes. Show more

Keywords: Multi-exposure X-ray image fusion, quality assessment, contrast sensitivity function (CSF), gradient amplitude similarity

DOI: 10.3233/XST-210871

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 4, pp. 697-709, 2021

Authors: Zhou, Jiang | Zhou, Yan | Hu, Hao | Shen, Mei-Ping | Ge, Ying-Qian | Tao, Xin-Wei | Xu, Xiao-Quan | Su, Guo-Yi | Wu, Fei-Yun

Article Type: Research Article

Abstract: OBJECTIVE: To assess the feasibility of using virtual non-contrast (VNC) images derived from dual-energy computed tomography (DECT) to replace true non-contrast (TNC) images of papillary thyroid carcinoma (PTC) patients. METHODS: Images of 96 PTC patients were retrospectively analyzed. TNC images were acquired under the single-energy mode of DECT after the plain scanning. The arterial and venous phase VNC (VNC-a and VNC-v) images were generated by the post-processing algorithm from the arterial phase and venous phase of contrast-enhanced CT images, respectively. Mean attenuation values, image noise, number and length of calcification were measured. Radiation dose was also calculated. Last, …subjective score of image quality was evaluated by a 5-point scale. RESULTS: Signal-to-noise ratio (SNR) of each tissue in TNC images is significantly higher than that of VNC images (p <0.050). Contrast-to-noise ratio (CNR) of fat, muscle, thyroid nodules and internal carotid artery in TNC images is significantly higher than that of VNC images, while CNR in TNC images is lower for cervical vertebra (p <0.001). Calcification is detected on TNC images of 44 patients, while it is omitted on VNC images of 14 patients (31.8%). The subjective score of TNC images is higher than VNC images (p <0.001). The effective dose reduction is 47.6% by avoiding plain scanning. CONCLUSIONS: Considering the different attenuation value, SNR, CNR and especially reduced detection rate of calcification, we deem that VNC images cannot be directly used to replace TNC images in PTC patients, despite the reduced radiation dose. Show more

Keywords: Dual-energy computed tomography (DECT), virtual non-contrast images, papillary thyroid carcinoma, image quality, radiation dose

DOI: 10.3233/XST-210884

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 4, pp. 711-720, 2021

Authors: Tamoor, Maria | Younas, Irfan

Article Type: Research Article

Abstract: Medical image segmentation is a key step to assist diagnosis of several diseases, and accuracy of a segmentation method is important for further treatments of different diseases. Different medical imaging modalities have different challenges such as intensity inhomogeneity, noise, low contrast, and ill-defined boundaries, which make automated segmentation a difficult task. To handle these issues, we propose a new fully automated method for medical image segmentation, which utilizes the advantages of thresholding and an active contour model. In this study, a Harris Hawks optimizer is applied to determine the optimal thresholding value, which is used to obtain the initial contour …for segmentation. The obtained contour is further refined by using a spatially varying Gaussian kernel in the active contour model. The proposed method is then validated using a standard skin dataset (ISBI 2016), which consists of variable-sized lesions and different challenging artifacts, and a standard cardiac magnetic resonance dataset (ACDC, MICCAI 2017) with a wide spectrum of normal hearts, congenital heart diseases, and cardiac dysfunction. Experimental results show that the proposed method can effectively segment the region of interest and produce superior segmentation results for skin (overall Dice Score 0.90) and cardiac dataset (overall Dice Score 0.93), as compared to other state-of-the-art algorithms. Show more

Keywords: Image segmentation, swarm optimization algorithm, active contour model, skin lesion segmentation, and machine learning

DOI: 10.3233/XST-210879

Citation: Journal of X-Ray Science and Technology, vol. 29, no. 4, pp. 721-739, 2021