B. Rajesh Kanna's research while affiliated with Rajiv Gandhi National Institute of Youth Development and other places

Photo attack and anti-forgery techniques have been on the rise in the internet age. Attackers conceal tampering by applying anti-forgery techniques, such as contrast enhancement. Digital image forensics concerns with detecting traces of these operations subjected upon the image. In this work, a novel Anti-Forensic Contrast Enhancement Detector (AFCED) scheme has been proposed to assess contrast changes from anti-forensic digital images. The proposed model is based on the underlying observation that, with repeated gamma correction, the rate of attaining contrast saturation in normal images is greater than already contrast-improved images. Thus, we employ skewness and kurtosis of the gamma-corrected second-order derivative histograms as features to distinguish normal images from contrast-enhanced images. These features are robustly classified on a Support Vector Machine. The proposed model achieved 97.40% F1-score and 98.18% sensitivity on the Dresden and Wikiart image databases on-par with state-of-the-art results.

The main aim of digital image forensics is to validate the authenticity of images by identifying the camera that captured the image and finding traces of any alteration in the spatial content. The majority of the existing literature focuses on manual extraction of intrinsic camera features such as lens aberration, sensor imperfections, pixel non-uniformity, color filter array type, and so on. These handcrafted features are analyzed and characterized as a unique signature for detecting the camera and authenticating the image which is recorded from the device. To facilitate an end-to-end automated forensics analysis, the current research explores the ability of a novel deep learning framework to learn the intrinsic signature of a selected camera model. The proposed deep convolutional network performs source camera identification (SCI). It contains two functional blocks, namely, esidual noise feature extractor (RNFE) and Feature Modulator (FM). To extract the noise pattern from camera images, the RNFE module analyses the debayered image using a U-Net. The generated noise residue is then modulated through a CNN pipeline to an embedding vector. Triplet loss function is used to train the proposed SCI network such that, the images captured from the source cameras are located closer to each other than images from different cameras. Experimental results demonstrate that the CNN achieves a 97.59% F-score and 97.01% recall, on par with state-of-the-art. Hence, the unified architectural representation of the proposed deep-net could be treated as a generic deep net framework in learning the sensor pattern noise (SPN) fingerprint of a camera model.

Sputum smear microscopic examination is an effective, fast, and low-cost technique that is highly specific in areas with a high prevalence of pulmonary tuberculosis. Since manual screening needs trained pathologist in high prevalence zones, the possibility of deploying adequate technicians during the epidemic sessions would be impractical. This condition can cause overburdening and fatigue of working technicians which may tend to reduce the potential efficiency of Tuberculosis (TB) diagnosis. Hence, automation of sputum inspection is the most appropriate aspect in TB outbreak zones to maximize the detection ability. Sputum collection, smear preparing, staining, interpreting smears, and reporting of TB severity are all part of the diagnosis of tuberculosis. This study has analyzed the risk of automating TB severity grading. According to the findings of the analysis, numerous TB-positive cases do not fit into the standard TB severity grade while applying direct rule-driven strategy. The manual investigation, on the other hand, arbitrarily labels the TB grade on those cases. To counter the risk of automation, a fuzzy-based Tuberculosis Severity Level Categorizing Algorithm (TSLCA) is introduced to eliminate uncertainty in classifying the level of TB infection. TSLCA introduces the weight factors, which are dependent on the existence of maximum number of Acid-Fast Bacilli (AFB) per microscopic Field of View (FOV). The fuzzification and defuzzification operations are carried out using the triangular membership function. In addition, the [Formula: see text]-cut approach is used to eliminate the ambiguity in TB severity grading. Several uncertain TB microscopy screening reports are tested using the proposed TSLCA. Based on the experimental results, it is observed that the TB grading by TSLCA is consistent, error-free, significant and fits exactly into the standard criterion. As a result of the proposed TSLCA, the uncertainty of grading is eliminated and the reliability of tuberculosis diagnosis is ensured when adapting automatic diagnosis.

The proposed work deals with the design and development of touch and native voice‐assisted prototype to enable the intuitive communication & interaction between health professionals and patients who are affected with Severe Acute Respiratory Infection (SARI), Ventilator‐dependent and admitted in Quarantine care. It also ensures the development of the multilingual capability to communicate effectively in most speaking ten Indian languages, so that the patients will be relieved from pains etc., as their queries are being addressed by health professionals. In this prototype, touch based gesture patterns can be effectively used as an interactive module and helps the doctors to monitor and answer to the queries of ICU patients regularly by updating it to the caretakers such that the patients are at ease to express their emotions or pains. The proposed prototype will be made available and accessible in an open software repository. As per the existing methods patients express their needs through non‐verbal communication methods and they could be missed out or misinterpreted resulting in symptoms that are poorly understood and the clinicians overestimate their ability to understand their communication feelings. These situations are eradicated by employing the use of “Touch Voice of SARI” Application. Hence this can be considered as an assistive communication tool which replaces the nonverbal communication to a meaningful communication for ventilator patients and healthcare professionals.

Microscopy is a rapid diagnosis method for many infectious diseases like tuberculosis (TB). In TB bacilli identification, specimens are stained using Ziehl–Neelsen or Auramine dye and are examined by technicians thoroughly for any infectious microbes. For pathological study, the images of these microbes are captured using microscopes and image processing is applied for further analysis. However, choosing 100 field of views (FOV) randomly from a 2 × 1 cm square area of sputum specimen may lead to inconsistency in specificity. The examination of specimens is a tedious process, and it requires especially skilled technicians for screening the sputum smear samples. The proposed tuberculosis detection system consists of two subsystems—a data acquisition system and a recognition system. In the data acquisition system, a motorized microscopic stage is designed and developed to automate the acquisition of all FOVs. Here the microscopic stage movement is motorized and scanning patterns are defined by the user for specimen examination. After the acquisition of all FOVs, data are passed to the recognition system. In the recognition system, transfer learning method is implemented by customizing the Inception V3 DeepNet model. This model learns from the pre-trained weights of Inception V3 and classifies the data using support vector machine (SVM) from the transferred knowledge. For training and testing the customized Inception V3 model, a public TB dataset (Shah et al. in J Med Imaging 4(2):027503, 2017. https://doi.org/10.1117/1.jmi.4.2.027503) and our own acquired microscopic digital dataset are used for analysis. In this model, the fixed feature representations are taken from the top stack layer of Inception V3 DeepNet and are classified using SVM. This model attains an accuracy of 95.05%, thereby reducing the dependency on skilled technicians in the screening process and increasing sensitivity and specificity.

Micro-organisms or microbes are single- or multi-cellular living organisms viewed under a microscope because they are too tiny to be seen with naked eyes. Tracking them is important as they play a vital role in our lives in terms of breaking down substances, production of medicines, etc., as well as causing several diseases like malaria, tuberculosis, etc., which need to be taken care of. For a pathological study, the images of these microbes are captured from the microscope and image processing is done for further analysis. These operations involved for the analysis requires skilled technicians for error-free results. When the number of images increases, it becomes cumbersome for those technicians as there is a chance of ambiguity in results, which hampers the sensitivity of the study. Further, image processing is a bit challenging and time-consuming as a single image provides only a snapshot of the scene. In this situation, video has come into the picture which works on different frames taken over time making it possible to capture motion in the images keeping track of the changes temporally. Video combines a sequence of images, and the capability of automatically analyzing video to determine temporal events is known as video analytics. The aim of this paper is to develop a new computing paradigm for video analytics which will be helpful for the comprehensive understanding of the microbial data context in the form of video files along with effective management of that data with less human intervention. Since video processing requires more processing speed, a scalable cluster computing framework is also set up to improve the sensitivity and scalability for detecting microbes in a video. The HDP, an open source data processing platform for scalable data management, is used to set up the cluster by combining a group of computers or nodes. Apache Spark, a powerful and fast data processing tool is used for the analysis of these video files along with OpenCV libraries in an efficient manner which is monitored with a Web UI known as Apache Ambari for keeping in track all the nodes in the cluster.

Malaria parasite has been visually inspected from the Giemsa-stained blood smear image using light microscope. The trained technicians are needed to screen the malaria from the microscope; this manual inspection requires more time. To reduce the problems in manual inspection, nowadays pathologist moves to the digital image visual inspection. The computer-aided microscopic image examination will improve the consistency in detection, and even a semiskilled laboratory technician can be employed for diagnosis. Most of the computer-aided malaria parasite detection consists of four stages namely preprocessing of blood smear images, segmentation of infected erythrocyte, extracting the features, detection of parasite and classification of the parasites. Feature extraction is one of the vital stages to detect and classify the parasite. To carry out feature extraction, geometric, color, and texture-based features are extracted for identifying the infected erythrocyte. Among these clause of features, texture might be considered as a very fine feature, and it provides the characteristics of smoothness over the region of interest using the spatial distribution of intensity. The proposed work demonstrates the merit of the texture feature in digital pathology which is prone to vary with respect to change in image brightness. In microscope, brightness of the image could be altered by iris aperture diameter and illumination intensity control knob. However, the existing literature failed to mention the details about these illumination controlling parameters. So the obtained texture feature may not be considered as distinct feature. In this paper, we conducted an experiment to bring out the deviation of texture feature values by changing the brightness of the acquired image by varying the intensity control knob.

Tuberculosis is a contagious disease which is one of the leading causes of death, globally. Due to high incidence rate of the disease, a computer assisted system is designed for early detection of tuberculosis to assist pathologists with increased sensitivity and specificity. The diagnosis of tuberculosis can be done by different methods like micro-biological identification, tuberculin skin test, culture method, enzyme linked immunosorbent assay (ELISA) and electronic nose system. Methods/Statistical Analysis: WHO recommends standard microscopy examination for diagnosis of tuberculosis for early diagnosis using Ziehl-Neelsen stained sputum smears. The microscopic examination, takes several minutes with the very focused concentration for an experienced staff to view a single sputum smear for reporting about the severity level. This examination demand for skilled technicians in high-prevalence countries could lead to overload and fatigue, which diminish the sensitivity of microscopy. Findings: To minimize the manual efforts, a design of computer assisted system is proposed for identification and counting the number of TB bacilli per Field Of View (FOV). Ziehl-Neelsen stained microscopic images are obtained from the bright field microscope for automated detection of TB bacilli. Segmentation of TB bacilli was done using RGB thresholding and Sauvola's adaptive thresholding algorithm. To eliminate the non TB bacilli, shape descriptors like area, perimeter, convex hull, major axis length and eccentricity are used to extract the bacilli feature. Finally the extracted TB bacilli are counted by covering the boundaries using the generated bounding box to produce the clinical report.

Body parts have an important role in the life of a human being. In today’s era, smart phones have also become a part of these body parts virtually. Smart phones and touch devices are of different interaction medium and it has essentially become a part and parcel of our daily life; equipped with touch based screens and thus making the user to interact with the smart phone in a better way. The objective of our project is to use smart phones without using touch feature and use facial features to select the applications, thus making life more comfortable to the end users especially the people with amputated or no fingers. The invention of these smart phones played a vital role in improving the lives of the people with such disabilities. These smart phones will be equipped with both the types of features like touch screen with and without using hands.