Bit Plane Slicing Research Articles

Novel substitution-box generation using group theory for secure medical image encryption in E-healthcare

<abstract> <p>With the increasing need for secure transmission and storage of medical images, the development of robust encryption algorithms is of paramount importance. Securing sensitive digital medical imagery information during transmission has emerged as a critical priority in the e-Healthcare systems. Recent research has highlighted the significance of developing advanced medical image encryption algorithms to ensure secure transmission during telediagnosis and teleconsultations. In this study, we propose a novel medical image encryption algorithm which is based on a novel substitution-box generation algebraic method using a combination of a multiplicative cyclic group with an order of 256 and a permutation group with a large order. To evaluate the security performance of the proposed generated S-box, various standard security indicators are assessed and analyzed. The newly proposed medical image encryption algorithm utilizes the generated S-box, along with bit-plane slicing, circular shifting, and XOR operations, to achieve enhanced security and robustness for encrypting sensitive imagery data. In order to assess the effectiveness of the proposed encryption algorithm, a comprehensive benchmarking analyses, specifically designed for evaluating image encryption schemes, have been conducted. The results obtained from the comparison and other analyses serve to validate the optimal features and high cryptographic strength exhibited by the proposed method. Hence, the proposed algorithm demonstrates significant effectiveness and holds considerable promise in the realm of medical image encryption for secure e-Healthcare systems.</p> </abstract>

Robustness and Explainability of Image Classification Based on QCNN

In this paper, we propose a multiscale entanglement renormalization ansatz (MERA) feature extraction method based on a novel quantum convolutional neural network (QCNN) for binary scanning tunneling microscopy (STM) image classification. We design QCNN quantum circuits for state preparation, quantum convolution, and quantum pooling in the TensorFlow quantum framework and compare the performance of QCNN classifier and two hybrid quantum-classical QCNN models. Adversarial attacks are considered as a type of interpretable method to evaluate the robustness of QCNN models. The similarity between the pixels of image bitplane slicing and Ising phase transition opens up new ways for exploring classification performance enhancement by QCNN classifiers. Classification performance of different bitplanes of QCNN also shows that they can robustly resist adversarial attacks such as FGSM, CW, JSMA, and DEEPFOOL.

An Efficient Chaos-Based Image Encryption Technique Using Bitplane Decay and Genetic Operators.

Social networks have greatly expanded in the last ten years the need for sharing multimedia data. However, on open networks such as the Internet, where security is frequently compromised, it is simple for eavesdroppers to approach the actual contents without much difficulty. Researchers have created a variety of encryption methods to strengthen the security of this transmission and make it difficult for eavesdroppers to get genuine data. However, these conventional approaches increase computing costs and communication overhead and do not offer protection against fresh threats. The problems with current algorithms encourage academics to further investigate the subject and suggest new algorithms that are more effective than current methods, that reduce overhead, and which are equipped with features needed by next-generation multimedia networks. In this paper, a genetic operator-based encryption method for multimedia security is proposed. It has been noted that the proposed algorithm produces improved key strength results. The investigations using attacks on data loss, differential assaults, statistical attacks, and brute force attacks show that the encryption technique suggested has improved security performance. It focuses on two techniques, bitplane slicing and followed by block segmentation and scrambling. The suggested method first divides the plaintext picture into several blocks, which is then followed by block swapping done by the genetic operator used to combine the genetic information of two different images to generate new offspring. The key stream is produced from an iterative chaotic map with infinite collapse (ICMIC). Based on a close-loop modulation coupling (CMC) approach, a three-dimensional hyperchaotic ICMIC modulation map is proposed. By using a hybrid model of multidirectional circular permutation with this map, a brand-new colour image encryption algorithm is created. In this approach, a multidirectional circular permutation is used to disrupt the image’s pixel placements, and genetic operations are used to replace the pixel values. According to simulation findings and security research, the technique can fend off brute-force, statistical, differential, known-plaintext, and chosen-plaintext assaults, and has a strong key sensitivity.

Secure Reversible Data Hiding in Images Based on Linear Prediction and Bit-Plane Slicing

Reversible Data Hiding (RDH) should be secured as per requirements to protect content in open environments such as the cloud and internet. Integrity and undetectability of steganographic images are amongst the main concerns in any RDH scheme. As steganographic encryption using linear prediction over bit-planes is challenging, so the security and embedding capacity of the existing RDH techniques could not be adequate. Therefore, a new steganographic technique is proposed which provides better security, higher embedding capacity and visual quality to the RDH scheme. In this technique, the cover image is divided into n-bit planes (nBPs) and linear prediction is applied to it. Next, the histogram of the residual nBPs image is taken, and secret data bits are encrypted using the RC4 cryptographic algorithm. To embed the encrypted secret data bits, the histogram shifting process is applied. This is achieved by using peak and zero pairs of residual nBPs images. This scheme provides security to the cover image and hidden data. The proposed RDH scheme is capable of extracting the embedded secret data accurately and recovering the original cover or residual nBPs image.

Automated screening of glaucoma stages from retinal fundus images using BPS and LBP based GLCM features

AbstractGlaucoma is an eye disease in which the retinal nerve fibers are irreversibly damaged. Early identification of glaucoma is essential because it may slow the progression of the illness. The clinical treatments and medical imaging methods that are currently available are all manual and require expert supervision. An automated glaucoma diagnosis system that is fast, accurate, and helps to reduce the load on professionals is necessary for mass screening. In our proposed work, a novel approach based on bit‐plane slicing (BPS), local binary pattern (LBP), and gray‐level co‐occurrence matrix (GLCM) is used. First, fundus images are separated into channels like red, green, and blue, and these separated channels are split into plans using BPS. Then, LBP images are obtained from selected green channel images. Second, we extract features based on GLCM from LBP images. Finally, using a least‐squares support vector machine classifier, the higher ranked features are employed to classify glaucoma stages. According to the findings of the experiments, our model outperformed state‐of‐the‐art approaches for glaucoma classification. Using 10‐fold cross‐validation, this model achieved an improved classification accuracy of 95.04%, specificity of 96.37%, and sensitivity of 93.77%. We conducted many relative experiments with deep learning and traditional machine learning‐based models to test our proposed methodology. Compared to existing glaucoma classification approaches, the new method has been shown to be more efficient.

An Efficient Image Steganography Scheme Using Bit-plane Slicing with Elliptic Curve Cryptography and Wavelet Transform

Information security is indispensable in the transmission of multimedia data. While accumulating and distributing such multimedia data, the access of data from a third person is the real security challenging issue. Information hiding plays an important role. Scramble the data before hiding it in carrier media gives enhanced security level for the data. In this paper, bit plane slicing is used to represent an input image with eight planes at bit-level instead of pixel-level. As the least significant bit contains noisy information, only the most significant bit plane can be used to represent an image. At the first level, an input image is processed through the spatial domain. Transform domain techniques are used to process the image at the middle level. Elliptic curve cryptography is used to scramble and descramble the MSB plane image. A logistic chaotic sequence of the input image is added to the most significant bit plane image to generate the final scrambled image. The discrete wavelet transform is used to embed the scrambled image in its high-frequency sub-bands. At the last level, a least significant bit technique, a spatial domain is used to embed the scrambled image in the carrier image. Message integrity is also verified by finding the hash of an input image. The performance of the proposed method is evaluated through various security measures. It gives good results as number of pixel change rate is closer to 100% and unified average changing intensity is 33.46.

Defending against Adversarial Attacks in Deep Learning with Robust Auxiliary Classifiers Utilizing Bit-plane Slicing

Deep Neural Networks (DNNs) have been widely used in variety of fields with great success. However, recent research indicates that DNNs are susceptible to adversarial attacks, which can easily fool the well-trained DNN-based classifiers without being detected by human eyes. In this article, we propose to integrate the target DNN model with our robust bit-plane classifiers to defend against adversarial attacks. The bit-plane classifiers take bit-planes of input images for convolution, which is motivated by our observation that successful attacks aim to generate imperceptible perturbations, and they mainly affect the low-order bits of pixels in clean images when adding the perturbations. We also propose two metrics, bit-plane perturbation rate and channel modification rate, to further explain the robustness of bit-plane classifiers. We discuss potential adaptive attack and find that our defense can be effective as long as the adversarial examples are qualified. We conduct experiments on dataset CIFAR-10 and GTSRB under white-box attack and black-box attack. The results show that our defense method can effectively increase the average model accuracy from 16.23% to 83.53% under white-box attack and from 40.65% to 88.14% under black-box attack on CIFAR-10 without sacrificing the accuracy of clean images.

Local bit-plane neighbour dissimilarity pattern in non-subsampled shearlet transform domain for bio-medical image retrieval.

This paper introduces a novel descriptor non-subsampled shearlet transform (NSST) local bit-plane neighbour dissimilarity pattern (NSST-LBNDP) for biomedical image retrieval based on NSST, bit-plane slicing and local pattern based features. In NSST-LBNDP, the input image is first decomposed by NSST, followed by introduction of non-linearity on the NSST coefficients by computing local energy features. The local energy features are next normalized into 8-bit values. The multiscale NSST is used to provide translational invariance and has flexible directional sensitivity to catch more anisotropic information of an image. The normalised NSST subband features are next decomposed into bit-plane slices in order to capture very fine to coarse subband details. Then each bit-plane slices of all the subbands are encoded by exploiting the dissimilarity relationship between each neighbouring pixel and its adjacent neighbours. Experiments on two computed tomography (CT) and one magnetic resonance imaging (MRI) image datasets confirms the superior results of NSST-LBNDP when compared to many recent well known relevant descriptors both in terms of average retrieval precision (ARP) and average retrieval recall (ARR).

Defect detection methods using boolean functions and the [formula omitted]-coefficient between bit-plane slices

We establish two automated defect detection (ADD) algorithms and apply them to detect faults in printed circuit boards (PCBs). Both techniques are referential and are implemented on the binary bit-plane images of the PCBs. In the first algorithm we measure the association between reference and inspected images via the φ-correlation coefficient of percentage statistics, while in the second approach we apply a carefully selected boolean function, as well as a smoothing median filter. Both techniques show a high accuracy, which is comparable to the state-of-the-art techniques, but in a notably faster time. For instance, the boolean function approach is faster than the use of the normalized cross correlation (NCC) by 1700% and it is faster than the use of a fast form of the NCC by 700%, while the use of the φ-correlation coefficient accelerates the procedure by 500% and 200% for the use of the NCC and some of its fast forms respectively.

Real-Time and Low-Memory Multi-Faces Detection System Design With Naive Bayes Classifier Implemented on FPGA

In recent years, face detection has been widely applied to a variety of fields, such as face recognition, image focusing, and surveillance systems. This study proposes a real-time multi-faces detection system based on naive Bayesian classifier using Field Programmable Gate Array (FPGA). The system includes three main parts, feature extraction, candidate face detection, and false elimination. First, downscale the image to the image pyramid and extract local binary image features from each downscaling image. With the bit-plane slicing for Local Binary Pattern (LBP) can save the memory consumption and speed up the computation. Then, adopt the naive Bayesian classifier to identify candidate faces. Finally use skin color filter and face overlapping elimination to remove false positives. The experimental results show that the accuracy rate is up to 96.14% in face detection, which demonstrates the proposed real-time multi-faces detection system is indeed effective and efficient.

Estimating Cell Count in Overlapped Cluster of ThinPrep Pap Smear Cell Microscopic Image

The cell adequacy is one of the important factor is Pap smear test as per Bethesda reporting system. The adequate number of cells in a given sample helps to effectively check for abnormality of cells. The Pap smear sample with inadequate number of cells will be rejected and patient will be called for collecting sample once again. The cell adequacy is the number of squamous epithelial cells present in the given Pap smear sample. This paper presents an automated method which estimates the number of cells in a given Pap smear image. This method estimates a cell count even when cells are overlapped. The proposed approach first separates disjoint cells and overlapped cell cluster from cervical Pap smear image. Then the overlapped cluster is further segmented to obtain individual cells. The overlapped cluster is selected based on the area factor. That is area of overlapped cluster will be more than disjoint cells. This method uses Bitplane slicing technique for segmentation of overlapped cells. Then the total cell counts is estimated as sum of disjoint cells count and number of cells separated from overlapped cluster. The method is tested on the ThinPrep images publicly available in open access database.

Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques

Background and objectiveGlaucoma is a ocular disorder which causes irreversible damage to the retinal nerve fibers. The diagnosis of glaucoma is important as it may help to slow down the progression. The available clinical methods and imaging techniques are manual and require skilled supervision. For the purpose of mass screening, an automated system is needed for glaucoma diagnosis which is fast, accurate, and helps in reducing the burden on experts. MethodsIn this work, we present a bit-plane slicing (BPS) and local binary pattern (LBP) based novel approach for glaucoma diagnosis. Firstly, our approach separates the red (R), green (G), and blue (B) channels from the input color fundus image and splits the channels into bit planes. Secondly, we extract LBP based statistical features from each of the bit planes of the individual channels. Thirdly, these features from the individual channels are fed separately to three different support vector machines (SVMs) for classification. Finally, the decisions from the individual SVMs are fused at the decision level to classify the input fundus image into normal or glaucoma class. ResultsOur experimental results suggest that the proposed approach is effective in discriminating normal and glaucoma cases with an accuracy of 99.30% using 10-fold cross validation. ConclusionsThe developed system is ready to be tested on large and diverse databases and can assist the ophthalmologists in their daily screening to confirm their diagnosis, thereby increasing accuracy of diagnosis.

Automatic characterisation of chars from the combustion of pulverised coals using machine vision

The study of char morphology, produced during combustion of pulverised coal, may be used to evaluate the effect of coal on the performance of the burner. Particle reactivity is the response to temperature and oxygen concentration and depends on particle size and other variations during the combustion. In this paper, we automatically characterised chars from the combustion of pulverised coal using machine vision. We have followed two different approaches to describe the chars: (i) its morphology and (ii) its intensity distribution provided by texture features. We realised that each of the binary layers obtained after bit-plane slicing a char image returned different representations that highlighted either rough or fine details. Hence we combined this finding with the two previous characterisation approaches. Thus, in this paper, we described char images using both morphology and texture computed on some specific bit-plane slices, and later on, we automatically classify each particle based on its description as having high, medium or low reactivity. To validate experimentally the proposed method we used char images from coals of three Colombian regions: Valle, Antioquia and Cundinamarca. We determined the reactivity of a coal sample by calculating the percentage of particles assigned to each of the three previous reactivity groups. The method that we are proposing obtains similar precision to the obtained by the manual analysis of char morphology following the International Committee for Coal and Organic Petrology criteria, but with the advantages of analysing the particles reactivity automatically.

High Quality - Low Computational Cost Technique for Automated Principal Object Segmentation Applied in Solar and Medical Imaging

The objective of this paper is to introduce a fully computerized, simple and low-computational cost technique that can be used in the preprocessing stages of digital images. This technique is specially designed to detect the principal (largest) closed shape object that embody the useful information in certain image types and neglect and avoid other noisy objects and artifacts. The detection process starts by calculating certain statistics of the image to estimate the amount of bit-plane slicing required to exclude the non-informative and noisy background. A simple closing morphological operation is then applied and followed by circular filter applied only on the outer coarse edge to finalize the detection process. The proposed technique takes its importance from the huge explosion of images that need accurate processing in real time speedy manner. The proposed technique is implemented using MATLAB and tested on many solar and medical images; it was shown by the quantitative evaluation that the proposed technique can handle real-life (e.g. solar, medical fundus) images and shows very good potential even under noisy and artifacts conditions. Compared to the publicly available datasets, 97% and 99% of similarity detection is achieved in medical and solar images, respectively. Although it is well-know, the morphological bit-plane slicing technique is hoped to be used in the preprocessing stages of different applications to ease the subsequent image processing stages especially in real time applications where the proposed technique showed dramatic (~100 times) saving in processing time.

Detection Method for Counterfeit Currency Based on Bit-Plane Slicing Technique

Counterfeiting and forging currencies is a serious threat to any economy. Even though currency exists as a variation of coins, banknotes, and electronic data, many economies remain threatened by counterfeiting which is made possible by the ongoing technological advancements in reprographic equipment available to the general public. Clearly, counterfeit currency detection is not a task that can be neglected. Digital image processing is one of the most common and effective techniques used to distinguish counterfeit banknotes from genuine ones. A new approach is presented in this paper using the bit-plane slicing technique to extract the most significant data from counterfeit banknote images with the application of an edge detector algorithm. The proposed technique consists of decomposing original images of 256 gray levels into their equivalent 8 binary images. This is useful in analyzing the relative importance contributed by each bit of the original image. Higher order bit levels are evaluated for grayscale banknote images with the application of Canny edge detection algorithm. The results are then compared with genuine banknotes and with other existing techniques used for detecting counterfeit notes. Unlike existing research, it was observed that the edges obtained using bit-plane sliced images are more accurate and can be detected faster than obtaining them from the original image without being sliced. The detection of counterfeit currency was also achieved by following the process of using Canny edge detection, image segmentation, and feature extraction.

A Novel Approach for Document Image Binarization Using Bit-plane Slicing

Abstract Historical document restoration has always been a challenging need for information acquisition and conversion to a stable and portable format. Document image binarization, is an important pre-processing phase in document image analysis and processing of such historical documents. It has never been an easy task accounting to the difficulty in choosing a threshold value when the document image suffers from noises due present in the original document due to its degradation, ageing of the paper, other unwanted impurities, background images, bleed-through, and so on. Inconsistency in the darkness of the letters in handwritten documents adds to the difficulty. Though there are many algorithms that have been proposed for document image binarization, there has not been an effective algorithm which can efficiently binarize all kind of documents. In this paper we introduce a novel method to perform document image binarization using bit-plane slicing. As per this approach, the 8 bit planes of the gray image are extracted and processed separately, at the end of which, the results are combined to give the final binarized output. This method works well for all kinds of historical documents except those with both, less noise and low contrast.

Lossless Image Compression based on Predictive Coding and Bit Plane Slicing

In this paper, a simple lossless image compression method based on a combination between bit-plane slicing and adaptive predictive coding is adopted for compressing natural and medical images. The idea basically utilized the spatial domain efficiently after discarding the lowest order bits namely, exploiting only the highest order bits in which the most significant bit corresponds to last layer7 used adaptive predictive coding, while the other layers used run length coding. The test results leads to high system performance in which higher compression ratio achieves for lossless system that characterized by guaranty fully reconstruction. General Terms Bit-plane slicing along with adaptive predictive coding for lossless image compression.

MARKER CONTROLLED WATERSHED SEGMENTATION USING BIT-PLANE SLICING

Image segmentation is the basis for computer vision and object recognition. Watershed transform is one of the common methods used for region based segmentation. The previous watershed methods results in over segmentation. In this paper we present a novel method for efficient image segmentation by using bit-plane slicing and marker-controlled watershed. Bit-Plane slicing method produces the sliced image by taking the most significant bit of the image as the input to the bit-plane slicing algorithm. The output of the Bit-Plane slicing algorithm is used to produce the gradient image .The watershed segmentation algorithm is applied to the average of the marker image and the gradient image so as to get efficient segmentation result. Experimental results, shows that the proposed method reduces the memory consumption and computation.

Automated thermal face recognition based on minutiae extraction

In this paper, an efficient approach for human face recognition based on the use of minutiae points in thermal face image is proposed. The thermogram of human face is captured by thermal infra-red camera. Image processing methods are used to pre-process the captured thermogram, from which different physiological features based on blood perfusion data are extracted. Blood perfusion data are related to distribution of blood vessels under the face skin. In the present work, three different methods have been used to get the blood perfusion image, namely bit-plane slicing and medial axis transform, morphological erosion and medial axis transform, sobel edge operators. A five layer feed-forward back propagation neural network is used as the classification tool. It has been found that the first method supercedes the other two producing an accuracy of 97.62% with block size 16×16 for bit-plane 4.

Hierarchical stereo matching with image bit-plane slicing

We propose a new stereo matching framework based on image bit-plane slicing. A pair of image sequences with various intensity quantization levels constructed by taking different bit-rate of the images is used for hierarchical stereo matching. The basic idea is to use the low bit-rate image pairs to compute rough disparity maps. The hierarchical matching strategy is then carried out iteratively to update the low confident disparities with the information provided by extra image bit-planes. It is shown that, depending on the stereo matching algorithms, even the image pairs with low intensity quantization are able to produce fairly good disparity results. Consequently, variate bit-rate matching is performed only regionally in the images for each iteration, and the average image bit-rate for disparity computation is reduced. Our method provides a hierarchical matching framework and can be combined with the existing stereo matching algorithms. Experiments on Middlebury datasets show that the proposed technique gives good results compared to the conventional full bit-rate matching.