Binary Pattern Approach Research Articles

Decision Support System in Identification of Lesions in the Dense Breast on Digital Mammograms

Background The most common cancer affecting women globally is breast cancer. The most effective and extensively used tool for identifying breast abnormalities in the early stage is mammography screening. However, it is less effective when there is high breast density. Therefore, the radiologist considers a sonomammography in addition to mammography to identify and characterize the lesion. Therefore, we aim at modelling a decision support system to identify a lesion in a dense breast using a mammogram and thereby avoiding additional sonomammogram. Methods In this work, the image pixels were pre-processed to produce super-pixels that are very meaningful and easy to analyse. Texture features extracted using a rotation invariant local binary pattern (RI-LBP) approach from super-pixels. Feature Selection algorithm was used to extract the top 5 features. The performance of various machine learning models was studied, and the best model was used for the prediction of the presence or absence of lesions in the mammogram images. Results The findings suggest that breast masses localized in the dense background can be described by features produced from RI-LBP super-pixel patterns in very effective and efficient ways. The experiment showed that with 500 super-pixels and only 5 RI-LBP features, the Support Vector machine model with gaussian kernel yielded an accuracy value of 90.38% and an area under the receiver operating characteristic (ROC) curve score of 0.96 for the mammogram dataset.

An Early Prediction of Tumor in Heart by Cardiac Masses Classification in Echocardiogram Images Using Robust Back Propagation Neural Network Classifier

Identification and classification of intracardiac masses in echocardiogram is one of the significant processes in the diagnosis of cardiovascular disease. A robust back propagation neural network (RBPNN) technique is used to conquer every single conventional-issue utilizing the echocardiogram image analysis for this work, which consists of four phases such as noise removal, automatic segmentation, feature extraction, and intracardiac masses classification. Initially, the noise is diminished from the echocardiogram images utilizing the adaptive vector median filter (AVMF). Then, linear iterative vessel segmentation (LIVS) is applied for automatic segmentation of the masses followed by the extraction of texture features using the multiscale local binary pattern (MS-LBP) approach. Finally, RBPNN is employed to classify the heart mass from the images of echocardiogram with the layered kernel for the system combination. Extensive simulation results obtained using proposed AVMF-MS-LBP based RBPNN approach disclosed the superiority over existing intracardiac mass detection and classification approaches in terms of accuracy of 98.85%.

Internet of Things (IOT) Enabled Bot For Home Security

This is a savvy observation robot that performs face acknowledgment, controlled by means of an Android application for analyzing unapproved passage in its region. The robot is fit for identifying people progressively situations. The movement pattern of the robot is ordered in various errands: Object Detection, Face Detection, Face Recognition, age of ready signs to the client. The robot recognizes the human face dependent on Haar Cascade Algorithm utilizing a camera module interfaced with Arduino uno. The framework is presented to various facial pictures of individuals from various points and foundation conditions. This structures the data set to prepare the framework which helps in facial acknowledgment. In this way, when the robot identifies a human face, it starts the calculation for acknowledgment of face which utilizes Local Binary Pattern (LBP) approach. As indicated by pictures in the dataset, the robot decides if the human recognized is Known or not. In the event of a match with the dataset, the client gets a message through the GSM module on his/her cell phone. If there should be an occurrence of a crisscross, a danger or an alarm message is gotten by the client. The movement of the robot is constrained by the android application through Bluetooth.

Quality Enhancement of Degraded Video and Object Tracking with Local Binary Pattern Approach

Quality Enhancement of Degraded Video and Object Tracking with Local Binary Pattern Approach

Computer vision algorithms for dominant contact lens feature extraction using fuzzy-logic-based classifications

Iris image recognition is an emerging approach for human identification but offers low reliability. An algorithm for dominant contact lens feature extraction based on an improved neighboring binary pattern (NBP) approach is proposed herein. Features are compared with neighboring features in various directions, assigning a value of 1 to dominant features and 0 otherwise. The features in two-dimensional binary tables are then trained using an adaptive neuro fuzzy inference system (ANFIS) and classified using various classifiers. The performance of various feature descriptors based on the classification algorithms is measured and compared using parameters such as the accuracy, training time, positive acceptance rate (PAR), and negative acceptance rate (NAR), and the PAR and NAR are compared based upon a confusion matrix of classifiers. The proposed dominant feature extraction method achieves an accuracy rate of 95.7%.

Relative Spectral Difference Occurrence Matrix: A Metrological Spectral-Spatial Feature for Hyperspectral Texture Analysis

We develop a spectral-spatial feature, Relative Spectral Difference Occurrence Matrix (RSDOM) for hyperspectral texture recognition. Inspired by Julesz's conjecture, the proposed feature is based on spectral difference approach and respects the metrological constraints. It simultaneously considers the distribution of spectra and their spatial arrangement in the hyperspectral image. The feature is generic and adapted for any number of spectral bands and range. We validate our proposition by applying a classification scheme on the HyTexiLa database. An accuracy comparable to local binary pattern approach is obtained, but at a much reduced cost due to the extremely small feature size.

Multi-Hypothesis Approach for Efficient Human Detection

Detection of human beings in a complex background environment is a challenging task in computer vision. Most of the time no single feature algorithm is rich enough to capture all the relevant information available in the image. In this paper, we propose a new feature extraction technique that combines three types of visual information; shape, color, and texture, and is named as the Color space Phase features with Gradient and Texture (CPGT) algorithm. Gradient concept and the phase congruency in color domain are used to localize the shape features. The Center-Symmetric Local Binary Pattern (CSLBP) approach is used to extract the texture information of the image. Fusing of these complementary features yields to capture a broad range of the human appearance details that improves the detection performance. The proposed features are formed by computing the gradient magnitude and CSLBP values for each pixel in the image with respect to its neighborhood in addition to the phase congruency of the three-color channels. Only the maximum phase congruency magnitudes are selected from the corresponding color channels. The histogram of oriented phase and gradients as well as the histogram of CSLBP values for the local regions of the image are determined and concatenated to construct the proposed descriptor. Principal Component Analysis (PCA) is performed to reduce the dimensionality of the resultant features. Several experiments were conducted to evaluate the performance of the proposed descriptor. The experimental results show that the proposed approach yields promising performance and has lower error rates when compared to several state of the art feature extraction methodologies. We observed a miss rate of 2.23% in the INRIA dataset and 2.6% in the NICTA dataset.

The most discriminant subbands for face recognition: A novel information-theoretic framework

In this paper, we propose a consolidated framework for the automatic selection of the most discriminant subbands for the problem of face recognition. Essentially, the face images are transformed into textures using the linear binary pattern (LBP) approach, these texturized-faces undergo the wavelet packet decomposition resulting in several subband images. We propose to use the energy features to effectively represent these subband images. The underlying statistical patterns of the data are harnessed in form of information-theoretic metrics to select the most discriminant subbands. The proposed algorithms are extensively evaluated on several standard databases and are shown to always pick the most significant subbands resulting in better performance. The proposed algorithms are entirely generic and do not depend on the selection of features or/and classifiers.

The search for proteins involved in the formation of crustacean cuticular structures

Crustacean cuticular structures are key features formed during a molt cycle. These structures are complex biomaterials comprising chitin and different mineral forms in distinct scaffold organizations. The formation of these complex biomaterials is controlled by the organic extracellular matrix including structural proteins. Since cuticular structures are formed de novo during each molt cycle, the spatial and temporal expression patterns of structural proteins are tightly linked to molt cycle events. As a model scenario, we demonstrate the molt-related pattern of expression of the gene encoding GAP65, a core structural protein involved in the formation of the cuticular structures of Cherax quadricarinatus. Based on this typical pattern of expression and using a binary-patterning approach, which is a specialized tool for the study of molt-related proteins, we revealed and characterized additional candidate proteins involved in the formation of crustacean cuticular structures. We propose that our approach be applied as a framework in the search for proteins involved in the formation of the crustacean cuticle. To stimulate research on this important aspect of structural biology, we put forward a schematic representation of the extracellular matrix and its proteins in three cuticular structures of C. quadricarinatus, the gastroliths, the mandibles, and the mineralized cuticle.

Intuitionistic fuzzy local binary pattern for features extraction

We propose a novel intuitionistic fuzzy feature extraction method to encode local texture. The proposed method extends the fuzzy local binary pattern approach by incorporating intuitionistic fuzzy set theory in the representation of local patterns of texture in images. Intuitionistic fuzzy local binary pattern also contribute to more than one bin in the distribution of the intuitionistic fuzzy local binary pattern values which can be used as a feature vector. The proposed intuitionistic fuzzy local binary pattern approach was experimentally evaluated for Lena image of the size 256 × 256. The results validate the effectiveness of the proposed intuitionistic fuzzy local binary pattern over the local binary pattern and fuzzy local binary pattern feature extraction methods.

EAR RECOGNITION USING LOCAL BINARY PATTERN

In this paper an efficient complete Ear Recognition System (ERS) has been proposed based on the Local Binary Pattern (LBP) approach that can investigate maximum recognition rate; hence it can be used for surveillance applications. The feature extraction is based on calculating the LBP feature for the ear image and dividing the resultant LBP image into several overlap regions, and then extracts the histogram from each region. These histograms are considered as a similarity measure in the classification phase. To evaluate the proposed approach, the Indian Institute of Technology (IIT) Delhi processed ear image dataset has been considered, which contains 125 individual, each with at least three images acquired in the age group between 14 and 58 years. Practical experiments are employed on the proposed ERS to find the best image division regions at best LBP parameters (radius and neighbors) that lead to maximum recognition rate. Detailed experiments show that the proposed system achieved 93.75 % rank-one recognition rate. Furthermore, an experimental study is achieved to examine the less Equal Error Rate (EER). Some identities from the database are considered as imposters. In a verification scenario, the system achieved an Equal Error Rate (EER) of 14.94 %. The Receiver Operating Characteristics (ROC) curve showed that the Genuine Acceptance Rate (GAR) is about 84%.

Parallel Technique for Medicinal Plant Identification System using Fuzzy Local Binary Pattern

As biological image databases are growing rapidly, automated species identification based on digital data becomes of great interest for accelerating biodiversity assessment, research and monitoring. This research applied high performance computing (HPC) to a medicinal plant identification system. A parallel technique for medicinal plant image processing using Fuzzy Local Binary Pattern (FLBP) is proposed. The FLBP method extends the Local Binary Pattern (LBP) approach by employing fuzzy logic to represent texture images. The main goal of this research was to measure the efficiency of using the proposed parallel technique for medicinal plant image processing and evaluation in order to find out whether this approach is reasonable for handling large data sets. The parallel processing technique was designed in a message-sending model. 30 species of Indonesian medical plants were analyzed. Each species was represented by 48 leaf images. Performance evaluation was measured using the speed-up, efficiency, and isoefficiency of the parallel computing technique. Preliminary results show that HPC worked well in reducing the execution time of medical plant identification. In this work, parallel processing of training images was 7.64 times faster than with sequential processing, with efficiency values greater than 0.9. Parallel processing of testing images was 6.73 times faster than with sequential processing, with efficiency values over 0.9. The system was able to identify images with an accuracy of 68.89%.

MARS: A protein family involved in the formation of vertical skeletal elements.

Vertical organizations of skeletal elements are found in various vertebrate teeth and invertebrate exoskeletons. The molecular mechanism behind the development of such structural organizations is poorly known, although it is generally held that organic matrix proteins play an essential role. While most crustacean cuticular organizations exhibit horizontal chitinous layering, a typical vertical organization is found towards the surface of the teeth in the mandibles of the crayfish Cherax quadricarinatus. Candidate genes encoding for mandible-forming structural proteins were mined in C. quadricarinatus molt-related transcriptomic libraries by using a binary patterning approach. A new protein family, termed the Mandible Alanine Rich Structural (MARS) protein family, with a modular sequence design predicted to form fibers, was found. Investigations of spatial and temporal expression of the different MARS genes suggested specific expression in the mandibular teeth-forming epithelium, particularly during the formation of the chitinous vertical organization. MARS loss-of-function RNAi experiments resulted in the collapse of the organization of the chitin fibers oriented vertically to the surface of the crayfish mandibular incisor tooth. A general search of transcriptomic libraries suggested conservation of MARS proteins across a wide array of crustaceans. Our results provide a first look into the molecular mechanism used to build the complex crustacean mandible and into the specialized vertical structural solution that has evolved in skeletal elements.

Heuristic Classifier for Observe Accuracy of Cancer Polyp Using Video Capsule Endoscopy

Methods:Colonoscopy is a technique for examine colon cancer, polyps. In endoscopy, video capsule is universally used mechanism for finding gastrointestinal stages. But both the mechanisms are used to find the colon cancer or colorectal polyp. The Automatic Polyp Detection sub-challenge conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org).Method:Colonoscopy may be primary way of improve the ability of colon cancer detection especially flat lesions. Which otherwise may be difficult to detect. Recently, automatic polyp detection algorithms have been proposed with various degrees of success. Though polyp detection in colonoscopy and other traditional endoscopy procedure based images is becoming a mature field, due to its unique imaging characteristics, detecting polyps automatically in colonoscopy is a hard problem. So the proposed video capsule cam supports to diagnose the polyps accurate and easy to identify its pattern. Existing methodology mainly concentrated on high accuracy and less time consumption and it uses many different types of data mining techniques. To analyse these high resolution video scale image we have to take segmentation of image in pixel level binary pattern with the help of a mid-pass filter and relative gray level of neighbours. This work consists of three major steps to improve the accuracy of video capsule endoscopy such as missing data imputation, high dimensionality reduction or feature selection and classification. The above steps are performed using a dataset called endoscopy polyp disease dataset with 500 patients. Our binary classification algorithm relieves human analyses using the video frames. SVM has given major contribution to process the dataset.Results:In this paper the key aspect of proposed results provide segmentation, binary pattern approach with Genetic Fuzzy based Improved Kernel Support Vector machine (GF-IKSVM) classifier. The segmented images all are mostly round shape. The result is refined via smooth filtering, computer vision methods and thresholding steps.Conclusion:Our experimental result produces 94.4% accuracy in that the proposed fuzzy system and genetic Fuzzy, which is higher than the methods, used in the literature. The GF-IKSVM classifier is well-organized and provides good accuracy results for patched VCE polyp disease diagnosis.

Texture Feature Extraction Using Intuitionistic Fuzzy Local Binary Pattern

Abstract In this paper, intuitionistic fuzzy local binary for texture feature extraction (IFLBP) has been proposed to encode local texture from the input image. The proposed method extends the fuzzy local binary pattern approach by incorporating intuitionistic fuzzy sets in the representation of local patterns of texture in images. Intuitionistic fuzzy local binary pattern also contributes to more than one bin in the distribution of IFLBP values, which can further be used as a feature vector in the various fields of image processing. The performance of the proposed method has been demonstrated on various medical images and processing images of size 256×256. The obtained results validated the effectiveness and usefulness of our proposed method over the other reported methods, and new improvements are suggested.

An auto tuned noise resistant descriptor for dynamic texture recognition

This paper presents a scheme for the representation and recognition of the dynamic texture in the noisy environment. Dynamic texture is the sequence of images of a moving scene that shows some form of temporal regularity. Though, the dynamic texture is the spatiotemporal extension of the conventional texture, its analysis requires additional attention, since the motion causes continuous changes in the geometry of these textures. Hence, to recognize the noisy dynamic texture, the noise should be estimated not only at the spatial level; it must also be estimated at the temporal level. To this end, an auto tuned noise resistance descriptor, based on the Local Binary Pattern approach, is proposed for the modeling and classification of the dynamic texture. Our approach based on the fact that, uniform local binary patterns are the fundamental units of image texture and occur more frequently in an image in comparison to non-uniform patterns. Noise affects these uniform local binary patterns and more likely fall into non-uniform codes. To counter this, we have extended conventional local binary pattern descriptor from a 2-value code to a 3-value code to include an additional state (called indecisive state) to represent the noise affected pixels. However, the estimation of the indecisive state is of the primary concern of this letter due to the inherently varying nature of the dynamic texture. The proposed technique devised a new scheme to estimate the noisy pixels in a dynamic texture. Eventually, the indecisive state is corrected back to non-noisy natural states using a mapping function so as to form a uniform LBP code. Experimental results on the UCLA and Dyntex++ databases demonstrate high classification efficiency of the proposed approach in the noisy environment.

Fingerprint Liveness Detection Using Convolutional Neural Networks

With the growing use of biometric authentication systems in the recent years, spoof fingerprint detection has become increasingly important. In this paper, we use convolutional neural networks (CNNs) for fingerprint liveness detection. Our system is evaluated on the data sets used in the liveness detection competition of the years 2009, 2011, and 2013, which comprises almost 50 000 real and fake fingerprints images. We compare four different models: two CNNs pretrained on natural images and fine-tuned with the fingerprint images, CNN with random weights, and a classical local binary pattern approach. We show that pretrained CNNs can yield the state-of-the-art results with no need for architecture or hyperparameter selection. Data set augmentation is used to increase the classifiers performance, not only for deep architectures but also for shallow ones. We also report good accuracy on very small training sets (400 samples) using these large pretrained networks. Our best model achieves an overall rate of 97.1% of correctly classified samples—a relative improvement of 16% in test error when compared with the best previously published results. This model won the first prize in the fingerprint liveness detection competition 2015 with an overall accuracy of 95.5%.

Improved Weber’s law based local binary pattern for dynamic texture recognition

Dynamic texture is the moving sequence of images that shows some form of temporal regularity. Various static texture descriptors have been extended to spatiotemporal domain for dynamic texture classification. Local Binary Pattern (LBP) is a simple descriptor computationally but sensitive to noise and sometimes fails to capture different patterns. In view of this, a novel approach for dynamic texture classification is introduced that maintains the advantageous characteristics of uniform LBP. Inspired by the Weber's law, a simple yet very powerful, robust texture descriptor, i.e., Weber's law based LBP with center pixel (WLBPC) is proposed from the local patches based on the conventional Local Binary Pattern approach. A noise resistant variant of Weber's law based LBP with center pixel (NR-WLBPC) is also proposed. To do this, WLBPC is extended to a 3-valued code based on a new threshold. Proposed noise resistant variant of WLBPC descriptor makes use of the indecisive bit and the uniform pattern to compute the feature vector. Center pixel information is fused with both the dynamic texture descriptors to improve the discriminative power. Extensive experimental evaluations on representative dynamic texture databases (DynTex++ and UCLA) show that the proposed descriptors show better performance in comparison to recent state-of-the-art LBP variants and other methods under both normal and noisy conditions. Noise invariant of the proposed descriptor also performs better in the presence of noise due to its robustness and discriminating capabilities.

Binary gene expression patterning of the molt cycle: the case of chitin metabolism.

In crustaceans, like all arthropods, growth is accompanied by a molting cycle. This cycle comprises major physiological events in which mineralized chitinous structures are built and degraded. These events are in turn governed by genes whose patterns of expression are presumably linked to the molting cycle. To study these genes we performed next generation sequencing and constructed a molt-related transcriptomic library from two exoskeletal-forming tissues of the crayfish Cherax quadricarinatus, namely the gastrolith and the mandible cuticle-forming epithelium. To simplify the study of such a complex process as molting, a novel approach, binary patterning of gene expression, was employed. This approach revealed that key genes involved in the synthesis and breakdown of chitin exhibit a molt-related pattern in the gastrolith-forming epithelium. On the other hand, the same genes in the mandible cuticle-forming epithelium showed a molt-independent pattern of expression. Genes related to the metabolism of glucosamine-6-phosphate, a chitin precursor synthesized from simple sugars, showed a molt-related pattern of expression in both tissues. The binary patterning approach unfolds typical patterns of gene expression during the molt cycle of a crustacean. The use of such a simplifying integrative tool for assessing gene patterning seems appropriate for the study of complex biological processes.

Ball detection in soccer images using isophote’s curvature and discriminative features

Ball recognition in soccer matches is a critical issue for automatic soccer video analysis. Unfortunately, because of the difficulty in solving the problem, many efforts of numerous researchers have still not produced fully satisfactory results in terms of accuracy. This paper proposes a ball recognition approach that introduces a double level of innovation. Firstly, a randomized circle detection approach based on the local curvature information of the isophotes is used to identify the edge pixels belonging to the ball boundaries. Then, ball candidates are validated by a learning framework formulated into a three-layered model based on a variation of the conventional local binary pattern approach. Experimental results were obtained on a significant set of real soccer images, acquired under challenging lighting conditions during Italian "Serie A" matches. The results have been also favorably compared with the leading state-of-the-art methods.