Set Of Shape Features Research Articles

MODELING AND ENHANCING LOW-QUALITY RETINAL FUNDUS IMAGES

Retinal Fundus images are widely used for the clinical screening and diagnosis of eye diseases. However, fundus images captured by operators with various levels of experience have a large variation in quality. Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. However, due to the special optical beam of fundus imaging and structure of the retina, natural image enhancement methods cannot be utilized directly to address this. The proposed system analyzed the main contribution is a new set of shape features, called Dynamic Shape Features, which do not require precise segmentation of the regions to be classified. Then, based on the degradation model, a clinically oriented fundus enhancement network (cofe-Net) is proposed to suppress global degradation factors, while simultaneously preserving anatomical retinal structures and pathological characteristics for clinical observation and analysis. Moreover, the system show that the fundus correction method can benefit medical image analysis applications, e.g., retinal vessel segmentation and optic disc/cup detection. Key Words: retinal fundus, retinal vessel segmentation, dynamic shape features, optic disc/cup detection

A secured QR pattern based E health care CAD system for CXR image analyzes

Early detection is the only way to control the transmission and elimination of tuberculosis (TB) and expert interpretations are always required to ensure the detection and explore all other lung abnormalities like lung cancer. Detection of TB disease through fully automated chest radiography (CXR) image analysis is one of the most challenging tasks to accomplish. This method is aimed at proposing e-health care measurement schemes for automated computer-aided diagnosis (CAD) systems for TB detection. This paper introduces the randomized key driven QR pattern generation and color pattern formulation for WBAN authentication. Here based on user defined key values the R, G and B channel integration is formulate which is used to validate the valid patients with improved security. This quick response (QR) pattern also enables an authentication framework for controlled data access, etc. This work starts with a deformable gradient-based automatic lung ROI segmentation using an active contour model. And then the oriented texture classification is carried out for multi-level Gray level co-occurrence matrix (GLCM) texture feature generation alongside with shape feature set. Finally, a template-based e-health care system is accomplished with highly secured patient validation and authenticated data assessment. Through experimental results, the robustness of color QR variant pattern analysis is also proved and its flexibility and embedding capacity are also validated.

Statistical shape modelling to analyse the talus in paediatric clubfoot.

One fifth of idiopathic clubfoot deformities cannot be fully corrected by Serial Ponseti casting and deformity recurs in 20%-30% of cases. To avoid x-ray exposure, the joints with largely unossified bones are diagnosed with magnetic resonance images (MRI). Typically, geometric measurements are made in the MRI planes; however, this method is inaccurate compared to measurements on three-dimensional (3D) models of the joint. More accurate measurements using the 3D bone shapes may be better at identifying differences between groups; and therefore, improve diagnosis. The entire set of shape features from MRI can be analysed simultaneously through statistical shape modelling (SSM) which assesses bone morphology of clubfoot in a more sensitive way. A method for SSM of the talus is developed in this study and the shape of the normal talus is compared with the one in clubfeet with residual deformity through both geometric measurements and SSM. Significant differences between two groups were found by both methods; and therefore, might contribute to improve diagnosis of clubfoot.

An integrated method for variation pattern recognition of BIW OCMM online measurement data

ABSTRACT In order to improve the quality of the body-in-white (BIW), optical coordination measurement machines (OCMM) are used to measure the dimensional variation for BIW. The big OCMM online measurement data with low signal-to-noise ratio makes the variation patterns recognition to be difficult and challenges the traditional statistical process control (SPC) technology and the common variation recognition approaches. In this paper, we propose an automatic and integrated method to recognise the control chart patterns (CCPs), which includes three main modules. The Jarque-Bera test is applied in the wavelet denoising module. The feature extraction module extracts a combination set of shape features and statistical features. In the classifier module, a two-hidden-layer Backpropagation neural network (BPN) is trained and tested. In the experiment, the proposed method is also compared with other CCPs recognition methods. Finally, a practice case is studied to show the application of the integrated method and validate the high recognition accuracy of the integrated system.

Machine learning for buildings' characterization and power-law recovery of urban metrics.

In this paper we focus on a critical component of the city: its building stock, which holds much of its socio-economic activities. In our case, the lack of a comprehensive database about their features and its limitation to a surveyed subset lead us to adopt data-driven techniques to extend our knowledge to the near-city-scale. Neural networks and random forests are applied to identify the buildings' number of floors and construction periods' dependencies on a set of shape features: area, perimeter, and height along with the annual electricity consumption, relying a surveyed data in the city of Beirut. The predicted results are then compared with established scaling laws of urban forms, which constitutes a further consistency check and validation of our workflow.

Welding Seam Trajectory Recognition for Automated Skip Welding Guidance of a Spatially Intermittent Welding Seam Based on Laser Vision Sensor.

To solve the problems of low teaching programming efficiency and poor flexibility in robot welding of complex box girder structures, a method of seam trajectory recognition based on laser scanning displacement sensing was proposed for automated guidance of a welding torch in the skip welding of a spatially intermittent welding seam. Firstly, a laser scanning displacement sensing system for measuring angles adaptively is developed to detect corner features of complex structures. Secondly, a weld trajectory recognition algorithm based on Euclidean distance discrimination is proposed. The algorithm extracts the shape features by constructing the characteristic triangle of the weld trajectory, and then processes the set of shape features by discrete Fourier analysis to solve the feature vector used to describe the shape. Finally, based on the Euclidean distance between the feature vector of the test sample and the class matching library, the class to which the sample belongs is identified to distinguish the weld trajectory. The experimental results show that the classification accuracy rate of four typical spatial discontinuous welds in complex box girder structure is 100%. The overall processing time for weld trajectory detection and classification does not exceed 65 ms. Based on this method, the field test was completed in the folding special container production line. The results show that the system proposed in this paper can accurately identify discontinuous welds during high-speed metal active gas arc welding (MAG) welding with a welding speed of 1.2 m/min, and guide the welding torch to automatically complete the skip welding, which greatly improves the welding manufacturing efficiency and quality stability in the processing of complex box girder components. This method does not require a time-consuming pre-welding teaching programming and visual inspection system calibration, and provides a new technical approach for highly efficient and flexible welding manufacturing of discontinuous welding seams of complex structures, which is expected to be applied to the welding manufacturing of core components in heavy and large industries such as port cranes, large logistics transportation equipment, and rail transit.

Haemorrhages detection using geometrical techniques

ABSTRACT Aim With an increasing percentage of retinal pathology because of diabetes, eye screening for diabetic retinopathy (DR) is in demand throughout the world. Haemorrhages (HEs) are one of the common signs for the red lesion detection for early diagnosis of this progressive degenerative disease of the retina. The detection of HEs in early stage prevents further progression of the eye disease and reduces the risk of blindness. Methods: The proposed method is based on morphological segmentation and geometrical feature techniques for HEs extraction. This approach uses preprocessing, removal of other retinal image details, determining connected components analysis and applying a specific shape feature set which results in improving the recognition of HEs. Results: The proposed algorithm demonstrated 95.47% accuracy for a Diaretdb1 database at image level detection. Moreover, the proposed method achieved better performance results for HEs extraction when individual images were analysed and compared with the true HEs count according to the multiple expert evaluations. Conclusion: The results obtained prove the potential use of the proposed algorithm in terms of true HEs count to facilitate the DR grading criteria related to HEs.

Combining Segmentation and Edge Detection for Efficient Ore Grain Detection in an Electromagnetic Mill Classification System.

This paper presents a machine vision method for detection and classification of copper ore grains. We proposed a new method that combines both seeded regions growing segmentation and edge detection, where region growing is limited only to grain boundaries. First, a 2D Fast Fourier Transform (2DFFT) and Gray-Level Co-occurrence Matrix (GLCM) are calculated to improve the detection results and processing time by eliminating poor quality samples. Next, detection of copper ore grains is performed, based on region growing, improved by the first and second derivatives with a modified Niblack’s theory and a threshold selection method. Finally, all the detected grains are characterized by a set of shape features, which are used to classify the grains into separate fractions. The efficiency of the algorithm was evaluated with real copper ore samples of known granularity. The proposed method generates information on different granularity fractions at a time with a number of grain shape features.

An approach for classification of malignant and benign microcalcification clusters

The only reliable and successful treatment of breast cancer is its detection through mammography at initial stage. Clusters of microcalcifications are important signs of breast cancer. Manual interpretation of mammographic images, in which the suspicious regions are indicated as areas of varying intensities, is not error free due to a number of reasons. These errors can be reduced by using computer-aided diagnosis systems that result in reduction of either false positives or true negatives. The purpose of the study in this paper is to develop a methodology for distinguishing malignant microcalcification clusters from benign microcalcification clusters. The proposed approach first enhances the region of interest by using morphological operations. Then, two types of features, cluster shape features and cluster texture features, are extracted. A Support Vector Machine is used for classification. A new set of shape features based on the recursive subsampling method is added to the feature set, which improves the classification accuracy of the system. It has been found that these features are capable of differentiating malignant and benign tissue regions. To investigate the performance of the proposed approach, mammogram images are taken from Digital Database for Screening Mammography database and an accuracy of 94.25% has been achieved. The experiments have shown that the proposed classification system minimizes the classification errors and is more efficient in correct diagnosis.

Evaluation of support vector machine and artificial neural networks in weed detection using shape features

Weed detection is still a challenging problem for robotic weed removal. Small tolerance between the cutting tine and main crop position requires highly precise discrimination of the weed against the main crop. Close similarities between the shape features of sugar beet and common weeds make it impossible to define an exclusive feature to be able to efficiently detect all the weeds with acceptable accuracy. Therefore in this study, it was tried to integrate several shape features to establish a pattern for each variety of the plants. To enable the vision system in the detection of the weeds based on their pattern, support vector machine and artificial neural networks were employed. Four species of common weeds in sugar beet fields were studied. Shape feature sets included Fourier descriptors and moment invariant features. Results showed that the overall classification accuracy of ANN was 92.92%, where 92.50% of weeds were correctly classified. Higher accuracies were obtained when the SVM was used as the classifier with an overall accuracy of 95.00% whereas 93.33% of weeds were correctly classified. Also, 93.33% and 96.67% of sugar beet plants were correctly classified by ANN and SVM respectively.

A Hessian plate filter and shape feature-based approach to automatically localizing the NT VOI of 3D ultrasound data

The thickness of nuchal translucency (NT) is one of the most important parameters to be detected in prenatal screening during the first trimester of pregnancy. In this paper, an automatic method is proposed for the localization of the volume of interest (VOI) of NT in three-dimensional (3D) ultrasound data. With the size and position of the fetal head and the equation of the sagittal plane in the 3D ultrasound data as the prior knowledge, the method can be divided into three steps. First, a Hessian-based plate filter is proposed to enhance the possible plates in the volume. Second, several VOIs with large filter responses are selected as candidates. Those VOIs consisting of several connected planes are segmented according to the prior knowledge of the sagittal plane. Third, a set of shape features is proposed to select the NT VOI with the maximum probability. Automatic fetal head localization and sagittal plane detection can be achieved using our previously proposed methodology, which means fully automatic NT volume detection could be reached by using our method. The proposed method produces 86.36% localization accuracy with the manual selection as the ground truth.

Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening.

The development of an automatic telemedicine system for computer-aided screening and grading of diabetic retinopathy depends on reliable detection of retinal lesions in fundus images. In this paper, a novel method for automatic detection of both microaneurysms and hemorrhages in color fundus images is described and validated. The main contribution is a new set of shape features, called Dynamic Shape Features, that do not require precise segmentation of the regions to be classified. These features represent the evolution of the shape during image flooding and allow to discriminate between lesions and vessel segments. The method is validated per-lesion and per-image using six databases, four of which are publicly available. It proves to be robust with respect to variability in image resolution, quality and acquisition system. On the Retinopathy Online Challenge's database, the method achieves a FROC score of 0.420 which ranks it fourth. On the Messidor database, when detecting images with diabetic retinopathy, the proposed method achieves an area under the ROC curve of 0.899, comparable to the score of human experts, and it outperforms state-of-the-art approaches.

Age‐invariant face recognition system using combined shape and texture features

This work presents an approach for combining texture and shape feature sets towards age-invariant face recognition. Physiological studies have proven that the human visual system can recognise familiar faces at different ages from the face outline alone. Based on this scientific fact, the phase congruency features for shape analysis were adopted to produce a face edge map. This was beneficial in tracking the craniofacial growth pattern for each subject. Craniofacial growth is common during childhood years, but after the age of 18, the texture variations start to show as the effect of facial aging. Therefore, in order to handle such texture variations, a variance of the well-known local binary pattern (LBP) texture descriptor, known as LBP variance was adopted. The results showed that fusing the shape and the texture features set yielded better performance than the individual performance of each feature set. Moreover, the individual verification accuracy for each feature set was improved when they were transformed to a kernel discriminative common vectors presentation. The system achieved an overall verification accuracy of above 93% when it was evaluated over the FG-NET face aging database.

Baseline shape diffeomorphometry patterns of subcortical and ventricular structures in predicting conversion of mild cognitive impairment to Alzheimer's disease.

In this paper, we propose a novel predictor for the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). This predictor is based on the shape diffeomorphometry patterns of subcortical and ventricular structures (left and right amygdala, hippocampus, thalamus, caudate, putamen, globus pallidus, and lateral ventricle) of 607 baseline scans from the Alzheimer's Disease Neuroimaging Initiative database, including a total of 210 healthy control subjects, 222 MCI subjects, and 175 AD subjects. The optimal predictor is obtained via a feature selection procedure applied to all of the 14 sets of shape features via linear discriminant analysis, resulting in a combination of the shape diffeomorphometry patterns of the left hippocampus, the left lateral ventricle, the right thalamus, the right caudate, and the bilateral putamen. Via 10-fold cross-validation, we substantiate our method by successfully differentiating 77.04% (104/135) of the MCI subjects who converted to AD within 36 months and 71.26% (62/87) of the non-converters. To be specific, for the MCI-converters, we are capable of correctly predicting 82.35% (14/17) of subjects converting in 6 months, 77.5% (31/40) of subjects converting in 12 months, 74.07% (20/27) of subjects converting in 18 months, 78.13% (25/32) of subjects converting in 24 months, and 73.68% (14/19) of subject converting in 36 months. Statistically significant correlation maps were observed between the shape diffeomorphometry features of each of the 14 structures, especially the bilateral amygdala, hippocampus, lateral ventricle, and two neuropsychological test scores--the Alzheimer's Disease Assessment Scale-Cognitive Behavior Section and the Mini-Mental State Examination.

Classification of Very High Spatial Resolution Imagery Based on a New Pixel Shape Feature Set

This letter presents a novel spatial features extraction method for the high spatial resolution multispectral imagery (HSRMI) classification. First, Canny filter algorithm is applied to extract the edge information to obtain the fuzzy edge map. Secondly, adaptive threshold value for each pixel's homogeneous region (PHR) calculation is determined based on the fuzzy edge map and original image. Next, the PHR for every pixel is obtained based on the fuzzy edge map, adaptive threshold value and original image. And then, the pixel shape feature set (PSFS) is extracted based on the PHR. Lastly, SVM classifier is applied to classify the hybrid spectral and PSFS. Two different experiments were performed to evaluate the performance of PSFS, in comparison with spectral, gray level co-occurrence matrix (GLCM) and the existing pixel shape index (PSI). Experimental results indicate that the PSFS achieved the highest accuracy, hence, providing an effective spectral-spatial classification method for the HSRMI.

Evaluation of Texture and Shape Features for Classification of Four Paddy Varieties

This research is aimed at evaluating the texture and shape features using the most commonly used neural network architectures for cereal grain classification. An evaluation of the classification accuracy of texture and shape features and neural network was done to classify four Paddy (rice) grains, namely, Karjat-6(K6), Ratnagiri-2(R2), Ratnagiri-4(R4), and Ratnagiri-24(R24). Algorithms were written to extract the features from the high-resolution images of kernels of four grain types and used as input features for classification. Different feature models were tested for their ability to classify these cereal grains. Effect of using different parameters on the accuracy of classification was studied. The most suitable feature from the features for accurate classification was identified. The shape feature set outperformed the texture feature set in almost all the instances of classification.

Statistical process control using optimized neural networks: A case study

The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy.

Multiple Classifier Combination For Recognition Of Wheat Leaf Diseases

Abstract Wheat industry is an important constituent of Northern China's overall agricultural economy. Proper disease detection using computer vision and pattern recognition has being investigated to minimize the loss, and fmally achieve intelligent healthy farming. This paper proposes a new strategy of Multi-Classifier System based on SVM (support vector machine) for pattern recognition of wheat leaf diseases for higher recognition accuracy. Diseased leaf samples with Powdery Mildew, Rust Puccinia Triticina, Leaf Blight, Puccinia Striifomus were collected in the field and images were captured before a uniform black background. Three feature sets including color feature set, shape feature set and texture feature set were created for classification analysis. The proposed combination strategy was based on stacked generalization and included two-level structure: base-level was a module of three kinds of SVM-based classifiers trained by three feature sets and meta-level was one module of SVM-based decision cla...

Recognition of control chart patterns using swarm intelligence and neural networks based on the statistical and shape features

AbstractAutomatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in the manufacturing processes. In this paper, a hybrid intelligent system is proposed for the recognition of control chart patterns. In this system, we have used a proper set of shape features and statistical features as the efficient characteristics of the patterns. Then we proposed a hybrid heuristic recognition system based on particle swarm optimization to improve the generalization performance of the radial basis function neural network classifier. For this purpose, we have optimized the classifier design by searching for the best value of the parameters that tune its discriminate function. The obtained results show that the proposed technique has high recognition accuracy in comparison with other techniques. This recognition accuracy is achieved with fewer training samples. IEEJ Trans 2010 DOI: 10.1002/tee.20610

Classification of breast masses via nonlinear transformation of features based on a kernel matrix

We propose methods to perform a certain nonlinear transformation of features based on a kernel matrix, before the classification step, aiming to improve the discriminating power of the comparatively weak edge-sharpness and texture features of breast masses in mammograms, and seek better incorporation of features representing different radiological characteristics than shape features only. Kernel principal component analysis (KPCA) is applied to improve the discriminating power of each single feature in an expanded feature space and the discriminating capability of different feature combinations in other transformed, more informative, lower-dimensional feature spaces. A kernel partial least squares (KPLS) method is developed to derive score vectors for a shape feature set, and an edge-sharpness and texture feature set, respectively, with minimal covariance between each other, to help in achieving improved diagnosis using multiple radiological characteristics of breast masses. Fisher's linear discriminant analysis (FLDA) is employed to evaluate the classification capability of the transformed features. The methods were tested with a set of 57 regions in mammograms, of which 20 are related to malignant tumors and 37 to benign masses, represented using five shape features, three edge-sharpness features, and 14 texture features. The classification performance of the edge-sharpness and texture features, via KPCA transformation, was significantly improved from 0.75 to 0.85 in terms of the area under the receiver operating characteristics curve (Az). The classification performance of all of the shape, edge-sharpness, and texture features, via KPLS transformation, was improved from 0.95 to 1.0 in Az value.