Multimodal Fusion of Face and Palm Using Local Color Binary Patterns and Haralick Features

Face recognizable proof has drawn in numerous scientists because of its novel benefit, for example, non-contact measure for include obtaining. Varieties in brightening, posture and appearance are significant difficulties of face acknowledgment particularly when pictures are taken as dim scale. To mitigate these difficulties partially many exploration works have been completed by considering shading pictures and they have yielded better face acknowledgment rate. A strategy for perceiving face utilizing shading nearby surface highlights is depicted. Test results show that Face ID approaches utilizing shading neighborhood surface highlights astonishingly yield preferred acknowledgment rates over Face acknowledgment approaches utilizing just shading or surface data. Especially, contrasted and grayscale surface highlights, the proposed shading neighborhood surface highlights can give great coordinating with rates to confront pictures taken under extreme varieties in enlightenment and furthermore for low goal face pictures. The other biometric framework utilizes palmprint as quality for the recognizable proof and validation of people. The principal point is to extract Haralick highlights and utilization of probabilistic neural organizations for confirmation utilizing palmprint biometric quality. PolyUdatabase tests are taken from around 200 clients every client's 2 examples are gained. This palm print biometric recognizes the phony (fake) palmprint made of POP (Plaster of paris) and separates among living and non-living dependent on the entropy highlight. Test results portray that the eleven Haralick feature values are acquired in execution stage and productive precision is accomplished.

Generic Wrapper Based Model using Haralick Features for Silk Fabric Defect Classification

Quality control unit of fabric industry looks for the effective defect detection methodology. The research is required to be done in this area to develop such solution. Various models based on combination of suitable feature extraction, selection and classification approaches need to be experimented out for the same. This paper attempts to experiment and provide such models mainly based on generic wrapper based selection approaches. Widely used broader range of Haralick features are prominently used for detection and classification of defects in this research. It also attempts to identify the suitability of these features based on segmented images provided as an input. The research has been carried on TILDA Dataset consisting of 800 Silk Fabric Images with eight different defects present on it and each carrying 100 images per defect. Models generated using generic wrapper based approach has also been compared with the Gabor Transforms. Then identification of suitable Haralick Features for particular type of defects has been carried out. In this 68% classification accuracy has been achieved using generic wrapper method and 40 % accuracy has been achieved using Gabor Transform with respect to fourteen Haralick Features and seven types of defects.

Assessment of Intratumor Heterogeneity in Parametric Dynamic Contrast-Enhanced MR Images: A Comparative Study of Novel and Established Methods

Intratumor heterogeneity is associated with aggressive disease and poor survival rates in several types of cancer. A novel method for assessing intratumor heterogeneity in medical images, named the spatial gradient method, has been developed in our laboratory. In this study, we measure intratumor heterogeneity in Ktrans maps derived by dynamic contrast-enhanced magnetic resonance imaging using the spatial gradient method, and we compare the performance of the novel method with that of histogram analyses and texture analyses using the Haralick method. Ktrans maps of 58 untreated and sunitinib-treated pancreatic ductal adenocaricoma (PDAC) xenografts from two PDAC models were investigated. Intratumor heterogeneity parameters derived by the spatial gradient method were sensitive to tumor line differences as well as sunitinib-induced changes in intratumor heterogeneity. Furthermore, the parameters provided additional information to the median value and were not severely affected by imaging noise. The parameters derived by histogram analyses were insensitive to spatial heterogeneity and were strongly correlated to the median value, and the Haralick features were severely influenced by imaging noise and did not differentiate between untreated and sunitinib-treated tumors. The spatial gradient method was superior to histogram analyses and Haralick features for assessing intratumor heterogeneity in Ktrans maps of untreated and sunitinib-treated PDAC xenografts, and can possibly be used to assess intratumor heterogeneity in other medical images and to evaluate effects of other treatments as well.

Diabetic Retinopathy Detection Using Local Extrema Quantized Haralick Features with Long Short-Term Memory Network

Diabetic retinopathy is one of the leading diseases affecting eyes. Lack of early detection and treatment can lead to total blindness of the diseased eyes. Recently, numerous researchers have attempted producing automatic diabetic retinopathy detection techniques to supplement diagnosis and early treatment of diabetic retinopathy symptoms. In this manuscript, a new approach has been proposed. The proposed approach utilizes the feature extracted from the fundus image using a local extrema information with quantized Haralick features. The quantized features encode not only the textural Haralick features but also exploit the multiresolution information of numerous symptoms in diabetic retinopathy. Long Short-Term Memory network together with local extrema pattern provides a probabilistic approach to analyze each segment of the image with higher precision which helps to suppress false positive occurrences. The proposed approach analyzes the retina vasculature and hard-exudate symptoms of diabetic retinopathy on two different public datasets. The experimental results evaluated using performance matrices such as specificity, accuracy, and sensitivity reveal promising indices. Similarly, comparison with the related state-of-the-art researches highlights the validity of the proposed method. The proposed approach performs better than most of the researches used for comparison.

Rapid sorting of radio galaxy morphology using Haralick features

ABSTRACT We demonstrate the use of Haralick features for the automated classification of radio galaxies. The set of thirteen Haralick features represent an extremely compact non-parametric representation of image texture, and are calculated directly from imagery using the Grey Level Co-occurrence Matrix (GLCM). The GLCM is an encoding of the relationship between the intensity of neighbouring pixels in an image. Using 10 000 sources detected in the first data release of the LOFAR Two-metre Sky Survey (LoTSS), we demonstrate that Haralick features are highly efficient, rotationally invariant descriptors of radio galaxy morphology. After calculating Haralick features for LoTSS sources, we employ the fast density-based hierarchical clustering algorithm hdbscan to group radio sources into a sequence of morphological classes, illustrating a simple methodology to classify and label new, unseen galaxies in large samples. By adopting a ‘soft’ clustering approach, we can assign each galaxy a probability of belonging to a given cluster, allowing for more flexibility in the selection of galaxies according to combinations of morphological characteristics and for easily identifying outliers: those objects with a low probability of belonging to any cluster in the Haralick space. Although our demonstration focuses on radio galaxies, Haralick features can be calculated for any image, making this approach also relevant to large optical imaging galaxy surveys.

A CUDA-powered method for the feature extraction and unsupervised analysis of medical images

Image texture extraction and analysis are fundamental steps in computer vision. In particular, considering the biomedical field, quantitative imaging methods are increasingly gaining importance because they convey scientifically and clinically relevant information for prediction, prognosis, and treatment response assessment. In this context, radiomic approaches are fostering large-scale studies that can have a significant impact in the clinical practice. In this work, we present a novel method, called CHASM (Cuda, HAralick & SoM), which is accelerated on the graphics processing unit (GPU) for quantitative imaging analyses based on Haralick features and on the self-organizing map (SOM). The Haralick features extraction step relies upon the gray-level co-occurrence matrix, which is computationally burdensome on medical images characterized by a high bit depth. The downstream analyses exploit the SOM with the goal of identifying the underlying clusters of pixels in an unsupervised manner. CHASM is conceived to leverage the parallel computation capabilities of modern GPUs. Analyzing ovarian cancer computed tomography images, CHASM achieved up to $$\sim 19.5\times $$ ∼ 19.5 × and $$\sim 37\times $$ ∼ 37 × speed-up factors for the Haralick feature extraction and for the SOM execution, respectively, compared to the corresponding C++ coded sequential versions. Such computational results point out the potential of GPUs in the clinical research.