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Rajarajan, M. (2017). Protection of medical images and patient related information in healthcare: Using an intelligent and reversible watermarking technique. Applied Soft Computing Journal, 51, pp. 1668-179. doi: 10.1016/j.asoc.2016.11.044 This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent AbstractThis work presents an intelligent technique based on reversible watermarking for protecting patient and medical related information. In the proposed technique 'IRW-Med', the concept of companding function is exploited for reducing embedding distortion, while Integer Wavelet Transform (IWT) is used as an embedding domain for achieving reversibility. Histogram processing is employed to avoid underflow/overflow. In addition, the learning capabilities of Genetic Programming (GP) are exploited for intelligent wavelet coefficient selection. In this context, GP is used to evolve models that not only make an optimal tradeoff between imperceptibility and capacity of the watermark, but also exploit the wavelet coefficient hidden dependencies and information related to the type of sub band. The novelty of the proposed IRW-Med technique lies in its ability to generate a model that can find optimal wavelet coefficients for embedding, and also acts as a companding factor for watermark embedding. The proposed IRW-Med is thus able to embed watermark with low distortion, take out the hidden information, and also recovers the original image. The proposed IRW-Med technique is effective with respect to capacity and imperceptibility and effectiveness is demonstrated through experimental comparisons with existing techniques using standard images as well as a publically available medical image dataset.

Devising automated procedures for accurate vessel segmentation (retinal) is crucial for timely prognosis of vision-threatening eye diseases. In this paper, a novel supervised deep learning-based approach is proposed which extends a variant of the fully convolutional neural network. The existing fully convolutional neural network-based counterparts have associated critical drawbacks of involving a large number of tunable hyper-parameters and an increased end-toend training time furnished by their decoder structure. The proposed approach addresses these intricate challenges by using a skip-connections strategy by sharing indices obtained through maxpooling to the decoder from the encoder stage (respective stages) for enhancing the resolution of the feature map. This significantly reduces the number of required tunable hyper-parameters and the computational overhead of the training as well as testing stages. Furthermore, the proposed approach particularly helps in eradicating the requirement for employing both postprocessing and pre-processing steps. In the proposed approach, the retinal vessel segmentation problem is formulated as a semantic pixel-wise segmentation task which helps in spanning the gap between semantic segmentation and medical image segmentation. A prime contribution of the proposed approach is the introduction of external skip-connection for passing the preserved lowlevel semantic edge information in order to reliably detect tiny vessels in the retinal fundus images. The performance of the proposed scheme is analyzed based on the three publicly available notable fundus image datasets, while the widely recognized evaluation metrics of specificity, sensitivity, accuracy, and the Receiver Operating Characteristics curves are used. Based on the assessment of the images in {DRIVE, CHASE_DB1, and STARE} datasets, the proposed approach achieves a sensitivity, specificity, accuracy, and ROC performance of {0.

Aggressive driving emotions is indeed one of the major causes for traffic accidents throughout the world. Real-time classification in time series data of abnormal and normal driving is a keystone to avoiding road accidents. Existing work on driving behaviors in time series data have some limitations and discomforts for the users that need to be addressed. We proposed a multimodal based method to remotely detect driver aggressiveness in order to deal these issues. The proposed method is based on change in gaze and facial emotions of drivers while driving using near-infrared (NIR) camera sensors and an illuminator installed in vehicle. Driver's aggressive and normal time series data are collected while playing car racing and truck driving computer games, respectively, while using driving game simulator. Dlib program is used to obtain driver's image data to extract face, left and right eye images for finding change in gaze based on convolutional neural network (CNN). Similarly, facial emotions that are based on CNN are also obtained through lips, left and right eye images extracted from Dlib program. Finally, the score level fusion is applied to scores that were obtained from change in gaze and facial emotions to classify aggressive and normal driving. The proposed method accuracy is measured through experiments while using a self-constructed large-scale testing database that shows the classification accuracy of the driver's change in gaze and facial emotions for aggressive and normal driving is high, and the performance is superior to that of previous methods.Association Foundation for Traffic safety, published in 2009, that the aggressive behavior of driver causes 56% of traffic accidents [2]. Besides precious human lives, people, company, and government also lose billions of dollars due to road accidents. For this reason, aggressive driving behavior must be strongly discouraged that will result in reduction of the number of traffic accidents.The classification of aggressive and normal behavior is an important issue that can be used to increase awareness of driving habits of drivers as many drivers are over confident and are unaware of their bad driving habits [3]. If we can automatically identify the drivers driving behaviors, the drivers can be aware of their bad habits and assist them to avoid potential car accidents. Other than this if, monitoring results could be sent back to a security observing server of the local police station that could help to automatically detect aggressive drivers. The conventional method to keep a check on aggressive driving is by police patrolling, but, due to lack of police force, all roads cannot be simultaneously monitored and it also costs a lot [4]. The need of intelligent surveillance system is increasing with the increase in population. The advance driver assistance system (ADAS) that can monitor driver's attention and driving behavior can improve road safety, which will also enhance the effectiveness of the ADAS [5]. Many challenges are faced by these real time systems that ...

Computer-Aided diagnosis (CAD) is a widely used technique to detect and diagnose diseases like tumors, cancers, edemas, etc. Several critical retinal diseases like diabetic retinopathy (DR), hypertensive retinopathy (HR), Macular degeneration, retinitis pigmentosa (RP) are mainly analyzed based on the observation of fundus images. The raw fundus images are of inferior quality to represent the minor changes directly. To detect and analyze minor changes in retinal vasculature or to apply advanced disease detection algorithms, the fundus image should be enhanced enough to visibly present vessel touristy. The performance of deep learning models for diagnosing these critical diseases is highly dependent on accurate segmentation of images. Specifically, for retinal vessels segmentation, accurate segmentation of fundus images is highly challenging due to low vessel contrast, varying widths, branching, and the crossing of vessels. For contrast enhancement, various retinal-vessel segmentation methods apply imagecontrast enhancement as a pre-processing step, which can introduce noise in an image and affect vessel detection. Recently, numerous studies applied Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement, but with the default values for the contextual region and clip limit. In this study, our aim is to improve the performance of both supervised and unsupervised machine learning models for retinal-vessel segmentation by applying modified particle swarm optimization (MPSO) for CLAHE parameter tuning, with a specific focus on optimizing the clip limit and contextual regions. We subsequently assessed the capabilities of the optimized version of CLAHE using standard evaluation metrics. We used the contrast enhanced images achieved using MPSO-based CLAHE for demonstrating its real impact on performance of deep learning model for semantic segmentation of retinal images. The achieved results proved positive impact on sensitivity of supervised machine learning models, which is highly important. By applying the proposed approach on the enhanced retinal images of the publicly available databases of {DRIVE and STARE}, we achieved a sensitivity, specificity and accuracy of {0.8315 and 0.8433 }, {0.9750 and 0.9760} and {0.9620 and 0.9645}, respectively.