Mousomi Roy

Bio: Mousomi Roy is an academic researcher from Kalyani Government Engineering College. The author has contributed to research in topics: Encryption & Digital image. The author has an hindex of 10, co-authored 18 publications receiving 210 citations.

A Novel Lossless Image Encryption Method using DNA Substitution and Chaotic Logistic Map

Abstract: Presently, there is a growth in the transmission of image and video data. Security becomes a main issue. Very strong image cryptographic techniques are a solution to this problem. There is a use of a randomly generated public key and based on that there is an application of DNA algorithm. In the proposed method DNA algorithm based substitution is used for spatial domain bit permutation. Here the chaotic logistic map is used for generating a pseudorandom bit sequence. We have generated 48bit length sequences for every pixel. After the substitution operation, a final layer of security is imposed to make this process more fault tolerant. The For checking the strength of the work a series of tests are performed and various parameters are checked like Correlation Coefficient Analysis, analysis of NPCR and UACI values etc.

A Study on Different Edge Detection Techniques in Digital Image Processing

Abstract: Image segmentation is one of the fundamental problems in image processing. In digital image processing, there are many image segmentation techniques. One of the most important techniques is Edge detection techniques for natural image segmentation. Edge is a one of the basic feature of an image. Edge detection can be used as a fundamental tool for image segmentation. Edge detection methods transform original images into edge images benefits from the changes of grey tones in the image. The image edges include a good number of rich information that is very significant for obtaining the image characteristic by object recognition and analyzing the image. In a gray scale image, the edge is a local feature that, within a neighborhood, separates two regions, in each of which the gray level is more or less uniform with different values on the two sides of the edge. In this paper, the main objective is to study the theory of edge detection for image segmentation using various computing approaches.

Biomedical image enhancement based on modified Cuckoo Search and morphology

Abstract: This work describes an method for biomedical image enhancement using modified Cuckoo Search Algorithm with some Morphological Operation. In recent years, various digital image processing techniques are developed. Computer Vision, machine interfaces, manufacturing industry, data compression for storage, vehicle tracking and many more are some of the domains of digital image processing application. In most of the cases, digital biomedical images contains various types of noise, artifacts etc. and are not useful for direct applications. Before using it in any process, the input image has to be gone through some preprocessing stages; such preprocessing is generally called as image enhancement. In this work, a new technique has been proposed to enhance biomedical images using modified cuckoo search algorithm and morphological operation. Presence of noise and other unwanted objects generates distortion in an image and it will affect the ultimate result of the process. In case of biomedical images, accuracy of the results is very important. It may also decrease the discernibility of many features inside the images. It can affect the classification accuracy. In this work, this issue has been targeted and improved by obtaining better contrast value after converting the color image into grayscale image. The basic property of the cuckoo search algorithm is that the amplitudes of its components are capable to objectively describe the contribution of the gray levels to the formation of image information for the best contrast value of a digital image. The proposed method modified the conventional cuckoo search method by employing the McCulloch's method for levy flight generation. After computing the best contrast value, morphological operation has been applied. In morphological operation based phase, the intensity parameters are tuned for quality enhancement. Experimental results illustrate the effectiveness of this work.

An Efficient Image Cryptographic Algorithm based on Frequency Domain using Haar Wavelet Transform

Abstract: Presently a number of techniques are used to restrict confidential image data from unauthorized access. In this paper, the authors have proposed an efficient lossless image cryptographic algorithm to transmit pictorial data securely. Initially we take a 64 bit key, we convert our decimal pixel value into binary 8 bits and we XOR the first 8 bits of the key with the pixel value. After that we take the next 8 bits of the key and XOR with the next pixel value. We perform the circular right shit operation when the key gets exhausted. We perform the first level haar wavelet decomposition thereafter. Dividing the LL1 into four equal sections we perform some swapping operations. Decryption follows the reverse of the encryption .Evaluation is done by some parametric tests which includes correlation analysis, NPCR, UACI readings etc. show that the proposed work is resilient and robust in the field of cryptography.

Cellular image processing using morphological analysis

Abstract: Cellular image analysis is considered one of the important job in biomedical image analysis. Analysis of cellular images obtained using a microscope is necessary in various disciplines including engineering and medical imaging. Cell detection is necessary in various jobs of microscopic analysis that helps physicians to diagnose and extract features. Accurate identification of cells is necessary for precise diagnosis. Analysis methods based on morphology is one of the major research area and also useful in biomedical image analysis as well as in bioinformatics. Morphology based analysis acts as the helping hand for physicians. Morphology based analysis methods are useful in determining cell shape, irregularity, feature extraction and classification. In this work, some of the methods have been reported which can be helpful in analyzing some practical problem by employing a suitable technique.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Medical Image Encryption Based on Hybrid Chaotic DNA Diffusion

Abstract: We explore the use of two chaotic systems (Bernoulli shift map and Zizag map) coupled with deoxyribonucleic acid coding in an encryption scheme for medical images in this paper. The scheme consists of two main phases: Chaotic key generation and DNA diffusion. Firstly, the message digest algorithm 5 hash function is performed on the plain medical image and the hash value used in combination with the value of an input ASCII string to generate initial conditions and control parameters for two chaotic systems (Bernoulli shift map and Zigzag map). These chaotic systems are subsequently used to produce two separate key matrices. Secondly, a row-by-row diffusion operation between the plain image matrix and the two chaotic key matrices, using the DNA XOR algebraic operation is performed in an alternating pattern to produce the cipher image. The logistic map is used to select the DNA encoding and decoding rules for each row. Experimental results of statistical, differential and key analyses demonstrate that the proposed scheme is robust and provides resistance to various forms of attacks.

An image encryption approach using particle swarm optimization and chaotic map

Abstract: Encryption is predominantly crucial in order to provide safeguard to sensitive data, specifically images, against any possible illegitimate access and transgressions. This paper presents to propose an optimized image encryption approach for secure image-based communication. The approach makes use of particle swarm optimization to receive optimized encryption effect and a chaotic map. Initially, the approach generates several encrypted images and chaotic Logistic map, where session key for map’s initial conditions are made dependent on pending plain-image. Subsequently, the encrypted images are served as particles and an initial assemblage to operate optimization through PSO. The optimized encrypted image is manifested by correlation coefficient relevant to contiguous pixels as fitness function. The simulation results of proposed encryption approach demonstrate that encrypted image exhibits high de-correlation of adjacent pixels along with other excellent encryption lineaments such as flat histograms, entropies, net pixel change rates, and unified average changing intensities.

SuFMoFPA: A superpixel and meta-heuristic based fuzzy image segmentation approach to explicate COVID-19 radiological images

Abstract: Coronavirus disease 2019 or COVID-19 is one of the biggest challenges which are being faced by mankind. Researchers are continuously trying to discover a vaccine or medicine for this highly infectious disease but, proper success is not achieved to date. Many countries are suffering from this disease and trying to find some solution that can prevent the dramatic spread of this virus. Although the mortality rate is not very high, the highly infectious nature of this virus makes it a global threat. RT-PCR test is the only means to confirm the presence of this virus to date. Only precautionary measures like early screening, frequent hand wash, social distancing use of masks, and other protective equipment can prevent us from this virus. Some researches show that the radiological images can be quite helpful for the early screening purpose because some features of the radiological images indicate the presence of the COVID-19 virus and therefore, it can serve as an effective screening tool. Automated analysis of these radiological images can help the physicians and other domain experts to study and screen the suspected patients easily and reliably within the stipulated amount of time. This method may not replace the traditional RT-PCR method for detection but, it can be helpful to filter the suspected patients from the rest of the community that can effectively reduce the spread in the of this virus. A novel method is proposed in this work to segment the radiological images for the better explication of the COVID-19 radiological images. The proposed method will be known as SuFMoFPA (Superpixel based Fuzzy Modified Flower Pollination Algorithm). The type 2 fuzzy clustering system is blended with this proposed approach to get the better-segmented outcome. Obtained results are quite promising and outperforming some of the standard approaches which are encouraging for the practical uses of the proposed approach to screening the COVID-19 patients.

Detection of skin disease using metaheuristic supported artificial neural networks

Abstract: Automated, efficient and accurate classification of skin diseases using digital images of skin is very important for bio-medical image analysis. Various techniques have already been developed by many researchers. In this work, a technique based on meta-heuristic supported artificial neural network has been proposed to classify images. Here 3 common skin diseases have been considered namely angioma, basal cell carcinoma and lentigo simplex. Images have been obtained from International Skin Imaging Collaboration (ISIC) dataset. A popular multi objective optimization method called Non-dominated Sorting Genetic Algorithm — II is employed to train the ANN (NNNSGA-II). Different feature have been extracted to train the classifier. A comparison has been made with the proposed model and two other popular meta-heuristic based classifier namely NN-PSO (ANN trained with Particle Swarm Optimization) and NN-GA (ANN trained with Genetic algorithm). The results have been evaluated using various performances measuring metrics such as accuracy, precision, recall and F-measure. Experimental results clearly show the superiority of the proposed NN-NSGA-II model with different features.