Morphological Processing Algorithm Research Articles

Detection of Abnormal Blood Flow Region Based on Near Infrared Correlation Spectroscopy

Blood flow measurement of microvessels in human tissues is of vital importance for the diagnosis and treatment of many diseases. In this paper, the detection method of abnormal blood flow regions based on near-infrared correlation spectroscopy is studied. We used the NL-Bregman-TV imaging algorithm to realize Blood flow imaging. However, due to the limitation of the number and distribution of detectors, the pixels obtained from images are extremely low, which cannot meet the practical requirements of the visual and the abnormal blood flow range measurement. In this paper, the bicubic interpolation method is used to improve the resolution of low-pixel blood flow images. The parameter index of the normalized similarity was proposed to help judge the effect of the interpolation method on the resolution of this kind of image. Aiming at the extraction of abnormal regions, a threshold segmentation algorithm based on the histogram difference method and a morphological processing algorithm is proposed to extract the contour of abnormal blood flow. The method proposed in this paper can be used to accurately locate and extract the clear and smooth contour of abnormal blood flow.

Machine Vision Algorithm for Identifying Packaging Components of HN-3 Arterial Blood Sample Collector

The arterial blood sample collector produced in large quantities often fails to meet the requirements due to missing components in the packaging bag, and traditional manual detection methods are both inefficient and inaccurate. To solve this problem, a PyCharm-integrated development environment was used to study image processing and recognition algorithms for identifying components inside the packaging bag of the HN-3 arterial blood sample collector. The machine vision system was used to capture images of the packaging bags of the HN-3 Arterial blood sample collector. Template matching was employed to extract the packaging ROI, and the threshold segmentation method in the HSV color model was used to extract material features based on the packaging ROI. Morphological processing algorithms such as dilation or erosion were used to enhance the connectivity of the extracted features. The existence of components was determined by setting thresholds for the connected domain area or length. The results of the recognition experiment show that the false detection rate is 0.2%, the missed detection rate is 0%, and the average image processing time per product is no more than 39 ms. Compared with manual recognition methods, the efficiency and accuracy have been improved by 36.5 times and 2.3%, respectively. The experimental results confirm the effectiveness of the image processing algorithm.

Weed Identification in Maize Fields Based on Improved Swin-Unet

The maize field environment is complex. Weeds and maize have similar colors and may overlap, and lighting and weather conditions vary. Thus, many methods for the automated differentiation of maize and weeds achieve poor segmentation or cannot be used in real time. In this paper, a weed recognition model based on improved Swin-Unet is proposed. The model first performs semantic segmentation of maize seedlings and uses the resulting mask to identify weeds. U-Net acts as the semantic segmentation framework, and a Swin transformer module is introduced to improve performance. DropBlock regularization, which randomly hides some blocks in crop feature maps, is applied to enhance the generalization ability of the model. Finally, weed areas are identified and segmented with the aid of an improved morphological processing algorithm. The DeepLabv3+, PSANet, Mask R-CNN, original Swin-Unet, and proposed models are trained on a dataset of maize seedling images. The proposed Swin-Unet model outperforms the others, achieving a mean intersection over union of 92.75%, mean pixel accuracy of 95.57%, and inference speed of 15.1 FPS. Our model could be used for accurate, real-time segmentation of crops and weeds and as a reference for the development of intelligent agricultural equipment.

Non-Invasive Skin Cancer Diagnosis Using Hyperspectral Imaging for In-Situ Clinical Support.

Skin cancer is one of the most common forms of cancer worldwide and its early detection its key to achieve an effective treatment of the lesion. Commonly, skin cancer diagnosis is based on dermatologist expertise and pathological assessment of biopsies. Although there are diagnosis aid systems based on morphological processing algorithms using conventional imaging, currently, these systems have reached their limit and are not able to outperform dermatologists. In this sense, hyperspectral (HS) imaging (HSI) arises as a new non-invasive technology able to facilitate the detection and classification of pigmented skin lesions (PSLs), employing the spectral properties of the captured sample within and beyond the human eye capabilities. This paper presents a research carried out to develop a dermatological acquisition system based on HSI, employing 125 spectral bands captured between 450 and 950 nm. A database composed of 76 HS PSL images from 61 patients was obtained and labeled and classified into benign and malignant classes. A processing framework is proposed for the automatic identification and classification of the PSL based on a combination of unsupervised and supervised algorithms. Sensitivity and specificity results of 87.5% and 100%, respectively, were obtained in the discrimination of malignant and benign PSLs. This preliminary study demonstrates, as a proof-of-concept, the potential of HSI technology to assist dermatologists in the discrimination of benign and malignant PSLs during clinical routine practice using a real-time and non-invasive hand-held device.

Research on Digital Camouflage Pattern Generation Algorithm Based on Adversarial Autoencoder Network

In the past, most of the digital camouflage used textural features to extract the configuration features of spots in gray images, unable to effectively utilize the position relationship between color information. In order to overcome this shortcoming, a new digital camouflage pattern design model was proposed based on the model of adversarial autoencoder network. Firstly, the complexity and performance of several main color extraction algorithms were analyzed and compared, and combined with AFK-MC2 algorithm and color similarity coefficient, a fast camouflage main color clustering method was proposed. Then a deep convolution adversarial autoencoder network was designed to extract and describe the configuration features of the spots in background pattern. In order to diffuse pixel spot and achieve the effect of spatial color blending, a morphological processing algorithm was proposed to process the generated camouflage patterns. Finally, two sets of grassland and woodland datasets were established, respectively. The influence of the number of latent variables of network on the training process was tested on the dataset, and the number of camouflage feature descriptions was determined to be greater than or equal to 10. In order to verify the effectiveness of the generated camouflage, the spots in background region and target region were randomly selected, and the Euclidean distance between the feature parameters of these spots was calculated. Both the visual and experimental results demonstrate that the generated spots have high fusion with the background.

Analysis of the ship target detection in high-resolution SAR images based on information theory and Harris corner detection

In order to make up the shortcomings of some existing ship target detection algorithms for high-resolution synthetic aperture radar (SAR) images, a ship target detection algorithm based on information theory and Harris corner detection for SAR images is proposed in this paper. Firstly, the SAR image is pretreated, and next, it is divided into superpixel patches by using the improved simple linear iterative clustering (SLIC) superpixel generation algorithm. Then, the self-information value of the superpixel patches is calculated, and the threshold T1 is set to select the candidate superpixel patches. And then, the extended neighborhood weighted information entropy growth rate threshold T2 is set to eliminate the false alarm candidate superpixel patches. Finally, the Harris corner detection algorithm is used to process the detection result and the number of the corner threshold T3 is set to filter out the false alarm patches, and the final SAR image target detection result is obtained. The effectiveness and superiority of the proposed algorithm are verified by comparing the proposed method with the results of constant false alarm rate (CFAR) detection algorithm combined with morphological processing algorithm and other ship target detection algorithms.

Kernelized Fuzzy C-means Clustering with Adaptive Thresholding for Segmenting Liver Tumors

Accurate liver segmentation from computed tomography (CT) scan images is an essential and crucial step in computer-aided diagnosis of liver tumor. The automatic and unsupervised methods are used to segment CT image that reduce the time and eliminate the need for manual interface. In this paper, the adaptive threshold, morphological processing, and kernel fuzzy C-means (KFCM) clustering algorithm have been used with spatial information for visualizing and measuring the tumor area of liver which is segmented from CT abdominal images. The proposed process was tested and evaluated to a series of test images on MICCAI 2008 liver tumor segmentation challenge datasets to extract the tumor region. The area of the tumor reason has been calculated also. The KFCM algorithm introduces a kernel function on fuzzy c-means clustering (FCM) to reduce the effect of noise and improves the ability of clustering. Experimental results showed positive results for the proposed algorithm. The PSNR and MSE have been calculated for FCM and KFCM method to compare the results to segment the images. An experimental result shows that the proposed method could be used effectively to segment the liver cancer region in terms of high PSNR and low MSE values and achieves high consistency to detect small changes in images.

Rapid Automated Target Segmentation and Tracking on 4D Data without Initial Contours.

Purpose. To achieve rapid automated delineation of gross target volume (GTV) and to quantify changes in volume/position of the target for radiotherapy planning using four-dimensional (4D) CT. Methods and Materials. Novel morphological processing and successive localization (MPSL) algorithms were designed and implemented for achieving autosegmentation. Contours automatically generated using MPSL method were compared with contours generated using state-of-the-art deformable registration methods (using Elastix© and MIMVista software). Metrics such as the Dice similarity coefficient, sensitivity, and positive predictive value (PPV) were analyzed. The target motion tracked using the centroid of the GTV estimated using MPSL method was compared with motion tracked using deformable registration methods. Results. MPSL algorithm segmented the GTV in 4DCT images in 27.0 ± 11.1 seconds per phase (512 × 512 resolution) as compared to 142.3 ± 11.3 seconds per phase for deformable registration based methods in 9 cases. Dice coefficients between MPSL generated GTV contours and manual contours (considered as ground-truth) were 0.865 ± 0.037. In comparison, the Dice coefficients between ground-truth and contours generated using deformable registration based methods were 0.909 ± 0.051. Conclusions. The MPSL method achieved similar segmentation accuracy as compared to state-of-the-art deformable registration based segmentation methods, but with significant reduction in time required for GTV segmentation.

МАТЕМАТИЧНЕ ЗАБЕЗПЕЧЕННЯ РОЗПІЗНАВАННЯ ТА СУПРОВОДУ РУХОМОГО ОБ’ЄКТА В РЕЖИМІ РЕАЛЬНОГО ЧАСУ ДЛЯ ВІДЕО

Розглянуто та проаналізовано методи класифікації рухомого об’єкта на відео. Запропоновано модифіковані методи класифікації та ведення рухомої цілі на основі гістограмних оцінок і метод ідентифікації рухомих точок на відео на основі міжкадрової різниці. Отримано алгоритми, які використовуються для розроблення інформаційної технології оброблення відео

멀티미디어 콘텐츠 제어를 위한 사용자 경험 기반 동작 인식 기술

In this paper, a series of algorithms are proposed for controlling different kinds of multimedia contents and realizing interact between human and computer by using single input device. Human gesture recognition based on NUI is presented firstly in my paper. Since the image information we get it from camera is not sensitive for further processing, we transform it to YCbCr color space, and then morphological processing algorithm is used to delete unuseful noise. Boundary Energy and depth information is extracted for hand detection. After we receive the image of hand detection, PCA algorithm is used to recognize hand posture, difference image and moment method are used to detect hand centroid and extract trajectory of hand movement. 8 direction codes are defined for quantifying gesture trajectory, so the symbol value will be affirmed. Furthermore, HMM algorithm is used for hand gesture recognition based on the symbol value. According to series of methods we presented, we can control multimedia contents by using human gesture recognition. Through large numbers of experiments, the algorithms we presented have satisfying performance, hand detection rate is up to 94.25%, gesture recognition rate exceed 92.6%, hand posture recognition rate can achieve 85.86%, and face detection rate is up to 89.58%. According to these experiment results, we can control many kinds of multimedia contents on computer effectively, such as video player, MP3, e-book and so on.

Application of image morphology in detecting and extracting the initial welding position

A method of image morphology in detecting and extracting the initial welding position during the autonomous welding process is described. During the process, firstly visual sensing technology is used to capture the straight seam image, and secondly the image edges are detected by morphological corrosion edge detection algorithm, with which can retain the critical information while filter other interferences effectively at the same time. Then morphological processing algorithm is used to conduct the direction of filter by selecting the multidirectional linear structuring elements and finally get the initial weld position point coordinates with the Hough transform. The algorithm is simple, rapid, self-adaptability with high accuracy for interferences except long lines so as to accomplish the entire process of detecting the initial welding position. It can meet the practical demands of automatic guidance for robotic welding.

Analysis of phasic and tonic electromyographic signal characteristics: Electromyographic synthesis and comparison of novel morphological and linear-envelope approaches

The pattern of tonic and phasic components in an EMG signal reflects the underlying behaviour of the central nervous system (CNS) in controlling the musculature. One avenue for gaining a better understanding of this behaviour is to seek a quantitative characterisation of these phasic and tonic components. We propose that these signal characteristics can range between unvarying, tonic and intermittent, phasic activation through a continuum of EMG amplitude modulation. In this paper, we present two new algorithms for quantifying amplitude modulation: a linear-envelope approach, and a mathematical morphology approach. In addition we present an algorithm for synthesising EMG signals with known amplitude modulation. The efficacy of the synthesis algorithm is demonstrated using real EMG data. We present an evaluation and comparison of the two algorithms for quantifying amplitude modulation based on synthetic data generated by the proposed synthesis algorithm. The results demonstrate that the EMG synthesis parameters represent 91.9% and 96.2% of the variance of linear-envelopes extracted from lumbo-pelvic muscle EMG signals collected from subjects performing a repetitive-movement task. This depended, however, on the muscle and movement-speed considered ( F = 4.02, p < 0.001). Coefficients of determination between input and output amplitude modulation variables were used to quantify the accuracy of the linear-envelope and morphological signal processing algorithms. The linear-envelope algorithm exhibited higher coefficients of determination than the most accurate morphological approach (and hence greater accuracy, T = 8.16, p < 0.001). Similarly, the standard deviation of the coefficients of determination was 1.691 times smaller ( p < 0.001). This signal processing algorithm represents a novel tool for the quantification of amplitude modulation in continuous EMG signals and can be used in the study of CNS motor control of the musculature in repetitive-movement tasks.