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The aorta, including the thoracic and abdominal aortas, distributes blood from the heart to the upper and lower circulatory systems. Abdominal aortic sclerosis can cause abdominal aortic aneurysm. When the diameter of the aorta increases by more than 1.5 times its normal size (e.g., ≥4−5 cm, mean diameter in an adult is 2−3 cm), the risk of aneurysm rupture gradually increases. Aneurysm rupture and its associated bleeding have high mortality rates. Hence, the prediction of large diameter aortic aneurysms is an important issue with great clinical implications. In computed tomography angiography (CTA) imaging examination, an assistive tool for automatic image segmentation must be developed to help cardiologists and radiologists evaluate the risk of rupture progression and make appropriate treatment decisions. In this study, two‐dimensional (2D) spatial convolution operations (corner detection) and a multilayer machine vision classifier enabled the automatic segmentation of the luminal region, vascular intima, and calcified plaques in the abdominal aorta. The 2D spatial convolution operations can efficiently increase focus, remove unwanted noise in the convolutional layer, and segment calcified plaques and the vessel border by using a general regression neural network (GRNN)‐based classifier. The classifier's outputs can also be visualized in color presentation (red, green, and blue color series) to identify vessel borders and calcified plaques. Luminal change rate (LCR), calcified plaque rate (CPR), and ellipse ratio (ER) can be defined as criteria for evaluating changes in the luminal region and locating calcified plaques, which can be used to identify the locations of cross‐sectional shape changes and calcified plaques. Based on the collected CTA images from the clinical case studies, the proposed multilayer classifier has promising results for the intended medical purpose. With the LCR index, the correlation coefficients (R2) for manual image segmentation and automatic image segmentation is >0.90 in positive correlation for vessel borders segmentation. The proposed method is an adaptive learning scheme with potential clinical applications in automatic CTA image segmentation for further abdominal aortic aneurysm (AAA) risk evaluation. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

While breast tumor symptoms [benign (B) or malignant (M) tumors] or lymph node metastasis can be detected at an early stage, the timely discovery of abnormalities could simultaneously improve survival rates and enhance therapeutic efficacy. During first‐line mammography examination, upon finding any solid‐white region, clinicians or radiologists can manually select the region of interest (ROI) from lateral‐view images with a specific bounding box (BB). This study used two‐dimensional (2D) fractional‐order convolution (FOC) operations with fractional‐order parameters (v = 0.3−0.4) to sharpen and retain the structure of possible focus, inhibit the features of background tissue, and remove unwanted noise. The gray‐scale values of breast mammography can be readjusted to enhance the image contrast using a nonlinear intensity transformation function. Abnormal contour levels can automatically be searched on the basis of the edge detection of normalized gradients to interpret the main ROI region using a contour algorithm. Hence, the main ROI can be rapidly identified on each breast mammography. The central region of the main ROI can then be divided into five sub ROIs, which are fed to a multilayer machine vision classifier. We used a general regression neural network (GRNN)‐based classifier to separate normal (no tumor) images from abnormal (B or M tumor) ones and obtain an automatic screening support tool that can decisively confirm suspicious lesions and achieve accurate detection. Using images from the Mammographic Image Analysis Society (MIAS) digital breast mammogram database, we applied the proposed GRNN‐based classifier with K‐fold cross‐validation to emphasize the possible focus and provide additional confidence during imaging examination. Results showed an average recall (%), an average precision (%), an average accuracy (%), and an average F1 score of 87.31%, 90.50%, 88.67%, and 0.8883%, respectively, for the rapid screening of B and M tumors on digital breast X‐ray images. The GRNN‐based classifier is superior to traditional techniques in terms of adaptive learning schemes, computational time consumption, and design cycle. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Breast tumor ranks fourth among various cancers in terms of mortality rate in Taiwan, and it is also the most commonly prevalent cancer in females. Early detection of any malignant lesions can increase the survival rate and also decline the mortality rate through current advanced medical therapies. Acoustic radiation force impulse (ARFI) is a new imaging technique for distinguishing breast lesions in the early stage based on localized tissue displacement, which is quantitated by virtual touch tissue imaging (VTI). Digital ARFI-VTI is an initial breast imaging modality and appears to be more effective in women aged >30 years. Therefore, image enhancement process is a key technique to enhance a low-contrast image in a region of interest (ROI) for visualizing texture details and morphological features. In this study, two-dimensional (2D) fractional-order convolution, as a 2D sliding filter window (eight filters are selected), is applied to enhance ARFI-VTI images for an accurate extrapolation of lesions in an ROI. Then, the maximum pooling is performed to reduce the dimensions of the feature patterns from 32×32 to 16×16 size. A multilayer machine vision classifier, as a generalized regression neural network (GRNN), is then used to screen subjects with benign or malignant tumors. With a 10fold cross-validation, promising results such as mean recall (%), mean precision (%), mean accuracy (%), and mean F1 score of 92.92±3.43%, 80.42±6.45%, 87.78±2.17%, and 0.8615±0.0495, respectively, are achieved for quantifying the performance of the proposed classifier. Breast tumors visualized on ARFI-VTI images can be useful as digitalized images for rapid screening of malignant from benign lesions by the proposed machine vision classifier.

Lung diseases can result in acute breathing problems and prevent the human body from acquiring enough oxygen. These diseases, such as pneumonia (P), pleural effusion (Ef), lung cancer, pneumothorax (Pt), pulmonary fibrosis (F), infiltration (In) and emphysema (E), adversely affect airways, alveoli, blood vessels, pleura and other parts of the respiratory system. The death rates of P and lung cancer are higher than those of other typical lung diseases. In visualization examination, chest radiography, such as anterior-posterior or lateral image viewing, is a straightforward approach used by clinicians/radiologists to diagnose and locate possible lung abnormalities rapidly. However, a chest X-ray image of patients may show multiple abnormalities associated with coexisting conditions, such as P, E, F, Pt, atelectasis, lung cancer or surgical interventions, which further complicate diagnosis. In addition, poor-quality X-ray images and manual inspection have limitations in digital image-automated classification. Hence, this study intends to propose a multilayer machine vision classifier to automatically identify the possible class of lung diseases within a bounding region of interest (ROI) on a chest X-ray image. For digital image texture analysis, a two-dimensional (2D) fractional-order convolution (FOC) operation with a fractional-order parameter, $v =0.3-0.5$ , is used to enhance the symptomatic feature and remove unwanted noises. Then, maximum pooling is performed to reduce the dimensions of feature patterns and accelerate complex computations. A multilayer machine vision classifier with radial Bayesian network and gray relational analysis is used to screen subjects with typical lung diseases. Anterior-posterior chest X-ray images from the NIH chest X-ray database (NIH Clinical Center) are enrolled. For digital chest X-ray images, with $K$ -fold cross-validation, the proposed multilayer machine vision classifier is applied to facilitate the diagnosis of typical lung diseases on specific bounding ROIs, as promising results with mean recall (%), mean precision (%), mean accuracy (%) and mean F1 score of 98.68%, 82.42%, 83.57% and 0.8981, respectively, for assessing the performance of proposed multilayer classifier for rapidly screening lung lesions on digital chest X-ray images.