Morphological Gradient Method Research Articles

Detection of retinopathy disease using morphological gradient and segmentation approaches in fundus images

Background and objectiveDiabetes-related cases can cause glaucoma, cataracts, optic neuritis, paralysis of the eye muscles, or various retinal damages over time. Diabetic retinopathy is the most common form of blindness that occurs with diabetes. Diabetic retinopathy is a disease that occurs when the blood vessels in the retina of the eye become damaged, leading to loss of vision in advanced stages. This disease can occur in any diabetic patient, and the most important factor in treating the disease is early diagnosis. Nowadays, deep learning models and machine learning methods, which are open to technological developments, are already used in early diagnosis systems. In this study, two publicly available datasets were used. The datasets consist of five types according to the severity of diabetic retinopathy. The objectives of the proposed approach in diabetic retinopathy detection are to positively contribute to the performance of CNN models by processing fundus images through preprocessing steps (morphological gradient and segmentation approaches). The other goal is to detect efficient sets from type-based activation sets obtained from CNN models using Atom Search Optimization method and increase the classification success. MethodsThe proposed approach consists of three steps. In the first step, the Morphological Gradient method is used to prevent parasitism in each image, and the ocular vessels in fundus images are extracted using the segmentation method. In the second step, the datasets are trained with transfer learning models and the activations for each class type in the last fully connected layers of these models are extracted. In the last step, the Atom Search optimization method is used, and the most dominant activation class is selected from the extracted activations on a class basis. ResultsWhen classified by the severity of diabetic retinopathy, an overall accuracy of 99.59% was achieved for dataset #1 and 99.81% for dataset #2. ConclusionsIn this study, it was found that the overall accuracy achieved with the proposed approach increased. To achieve this increase, the application of preprocessing steps and the selection of the dominant activation sets from the deep learning models were implemented using the Atom Search optimization method.

Filter technique of medical image on multiple morphological gradient (MMG) method

Filter technique is supportive for reducing image noise. This paper presents a study on filtering medical images, i.e., CT-Scan, Chest X-ray and Panoramic X-ray collected from two of the most prominent public hospitals in Padang City, Indonesia. The aim of this study preserved to facilitate in diagnosing objects in x-ray medical images. This study used filter technique, i.e. Blur, Emboss, Gaussian, Laplacian, Roberts, Sharpen, or Sobel techniques as pre-processing step. The filter process performed before edge detection and edge clarification. MMG method used in this study to clarify the edge detection. Thus, this research showed the hesitation decline (confidence increase) of the diagnosis of objects contained in medical images.

Real-time adaptive visible and infrared image registration based on morphological gradient and C_SIFT

Since the visible and infrared images have different imaging mechanisms, the difficulty of image registration has greatly increased. The grayscale difference between visible and infrared images is very disadvantageous for extracting feature points in homogenous region, but they both retain the obvious contour edge in the scene. After using the morphological gradient method, the grayscale edge of visible and infrared images can be obtained and their similarity is greatly improved, and their difference may be seen as the difference in brightness or grayscale. Therefore, we proposed a novel algorithm to realise real-time adaptive registration of visible and infrared images using morphological gradient and C_SIFT. Firstly, the morphological gradient method is used to extract the rough edges of visible and infrared images for aligning their visual features as a single similar type. Secondly, the C_SIFT feature detection operator is used to detect and extract feature points from the extracted edges. The C_SIFT uses the centroid method to describe the main direction of feature points, makes rotation invariance feasible. Finally, to verify the effectiveness of the proposed algorithm, we carried out a series of experiments in eight various scenarios. The experimental results show that the proposed algorithm has achieved good experimental results. The registration of visible and infrared images can be completed quickly by the proposed algorithm, and the registration accuracy is satisfactory.

Mapping channel edges in seismic data using curvelet transform and morphological filter

Mapping channel edges is a significant issue in 3D seismic data interpretation. In this research, curvelet transform was employed in channel edge enhancement, owing to its high ability to depict curve edges. The default parameters of curvelet transform were used to decompose the data. Hence, there are 6 scales and 16 directions in the 2nd level of decomposition for the real data of this study. Utilizing the modified top-hat algorithm, we calculated the maximum curvelet coefficients in all sub-bands. Employing top-hat in curvelet domain is more effective than the soft or hard thresholding to enhance the channel edges. Channel edges were further detected through morphological gradient algorithm with multi-length and multi-direction structuring elements. A directional feature of the proposed structuring element rendered the curvelet morphological gradient method used in the edge detection. Final edge map resulting from the weighted average of all sub-edge images was obtained from the structuring elements. Channel edge detection by the morphological gradient creates a large number of false edges. However, the combination of the morphological gradient with the curvelet transform eliminates many of those artifacts. The proposed algorithm was applied to both synthetic and real seismic data set containing channels. The findings resulted in a proper channel edge map as good as Canny, Sobel, and Laplacian of Gaussian edge detectors.

Combined radiogrammetry and texture analysis for early diagnosis of osteoporosis using Indian and Swiss data

Osteoporosis is a bone disorder characterized by bone loss and decreased bone strength. The most widely used technique for detection of osteoporosis is the measurement of bone mineral density (BMD) using dual energy X-ray absorptiometry (DXA). But DXA scans are expensive and not widely available in low-income economies. In this paper, we propose a low cost pre-screening tool for the detection of low bone mass, using cortical radiogrammetry of third metacarpal bone and trabecular texture analysis of distal radius from hand and wrist radiographs. An automatic segmentation algorithm to automatically locate and segment the third metacarpal bone and distal radius region of interest (ROI) is proposed. Cortical measurements such as combined cortical thickness (CCT), cortical area (CA), percent cortical area (PCA) and Barnett Nordin index (BNI) were taken from the shaft of third metacarpal bone. Texture analysis of trabecular network at the distal radius was performed using features obtained from histogram, gray level Co-occurrence matrix (GLCM) and morphological gradient method (MGM). The significant cortical and texture features were selected using independent sample t-test and used to train classifiers to classify healthy subjects and people with low bone mass. The proposed pre-screening tool was validated on two ethnic groups, Indian sample population and Swiss sample population. Data of 134 subjects from Indian sample population and 65 subjects from Swiss sample population were analysed. The proposed automatic segmentation approach shows a detection accuracy of 86% in detecting the third metacarpal bone shaft and 90% in accurately locating the distal radius ROI. Comparison of the automatic radiogrammetry to the ground truth provided by experts show a mean absolute error of 0.04 mm for cortical width of healthy group, 0.12 mm for cortical width of low bone mass group, 0.22 mm for medullary width of healthy group, and 0.26 mm for medullary width of low bone mass group. Independent sample t-test was used to select the most discriminant features, to be used as input for training the classifiers. Pearson correlation analysis of the extracted features with DXA-BMD of lumbar spine (DXA-LS) shows significantly high correlation values. Classifiers were trained with the most significant features in the Indian and Swiss sample data. Weighted KNN classifier shows the best test accuracy of 78% for Indian sample data and 100% for Swiss sample data. Hence, combined automatic radiogrammetry and texture analysis is shown to be an effective low cost pre-screening tool for early diagnosis of osteoporosis.

Edge Detection on Objects of Medical Image with Enhancement Multiple Morphological Gradient Method

Medical image is an invaluable tool in the detection of diseases or abnormalities in human organs. The low quality of medical images cause difficulty in observing the objects contained in the image, causing errors in detection. This research develops a method for improving the quality of medical images so that the edges of objects more clearly. This method is called Enhancement multiple Morphological Gradient Enhancement (EmMG). The method used at medical images had different formats, that as Computed Tomography Scan (CT-Scan) with format type Windows bitmap (bmp), Chest X-Ray with format type Joint Photographic Experts Group (jpg) and Panoramic X-Ray with format type Portable Network Graphics (png). The method developed produces images that can further clarify the edge of the object in the medical image, making it easier to detect diseases or abnormalities in the human body. This method can used as one of the solutions in medical help to improve the accuracy in detecting objects in medical images because the edge of the objects seen clearly.

An improved method for edge detection based on interval type-2 fuzzy logic

In this paper, a method for edge detection in digital images based on the morphological gradient and fuzzy logic is described. A basic method for edge detection was improved using fuzzy logic. An advantage of the improved method is that there is no need of applying filtering to the image. The simulation results were obtained with a type-1 fuzzy inference system (T1FIS) and with an interval type-2 fuzzy inference system (IT2FIS) for improving the edge detection method. We show that the images obtained with fuzzy logic are better than the ones obtained with only the morphological gradient method. In particular the IT2FIS achieved the best results, because of the flexibility to model the uncertainty in the gradient values and the gray ranges for the edge images. In both TIFIS and IT2FIS the membership function parameters were obtained directly from the images; this allows application of the proposed method to images with different gray scales.

Accurate fault location based on transients extraction using mathematical morphology

A multi-resolution morphological gradient method is developed to efficiently extract fault-generated transients and accurately identify fault locations in a power transmission line system.

Unraveling the role of structure and density in determining vertebral bone strength.

The strength of bone is determined not only by bone density but also by structure. Therefore, quantification of the structure in radiographs by texture parameters may result in a better prediction of fracture risk. Since in radiographs density and structure are strongly correlated, the predictive power of texture parameters should be corrected for the influence of BMD to determine the additional information conveyed by these parameters. In this study, we evaluated the predictive power of various texture parameters based on the Grey-Level Dependence Method and the Morphological Gradient Method. This study was performed on 67 vertebrae obtained from 20 male and 12 female human cadaver thoracolumbar spines. BMD and area of the vertebral body were determined from QCT images and texture parameters were derived from direct magnification (DIMA) radiographs. The fracture force, measured under conditions simulating the in vivo situation, was corrected with the area of the vertebra to yield the fracture stress (FS). Results of the study indicate that BMD correlates significantly with FS r = 0.82 (P < 0. 001, n = 24) and r = 0.94 (P < 0.001, n = 43) for female and male vertebrae, respectively. Correlation coefficients of the investigated texture parameters were as high as 0.80 (P < 0.001) and 0.67 (P < 0.001) for the female and male vertebrae, respectively. Multiple regression analysis showed that in female vertebrae, the addition of one texture parameter to BMD results in a better prediction of strength. The multiple correlation coefficient was 0. 87 (P < 0.001) in this case. In male vertebrae, BMD was the best predictor of fracture stress. These results suggest that texture parameters, as measured in magnification radiographs, can predict bone strength. Whereas in all cases BMD is the best single predictor of bone strength, for women texture parameters contain useful additional information.