Image denoising is a key pre-processing step in medical image analysis. At current, deep learning-based models have shown a great promise, which outperformed many conventional methods over the past three decades. Speckle noise removal is a major issue in preserving all the delicate details and the edges in ultrasound image processing as it degrades the visual evaluation of ultrasound images. The multiplicative behavior of speckle-noise is converted into additive by using log transform as the additive noise removal is easy as compared to multiplicative noise. An innovative approach for denoising highly distorted images affected by speckle noise is proposed. This paper presents a result of significant work in image denoising and exploring several thresholding methods of denoising images such as SureShrink, VisuShrink and BayesShrink. The results of different approaches of wavelet-based image denoising methods are tabulated to find the best method. The main aim is to show the result of wavelet coefficients on a new basis, so that the noise can be minimized or removed from the data.

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Background: The spectrum of autism includes High Functioning Autism(HFA)and Low Functioning Autism(LFA). Brain mapping studies have revealed that, the autism individuals have overlaps in brain behavioural characteristics. Generally, the high functioning individuals are known to exhibit higher intelligence and better language processing abilities. However, the specific mechanisms associated with the functional capabilities are still under research. Objective: This paper attempts to address the overlapping phenomenon present in autism through functional connectivity patterns along with, brain connectivity parameters and distinguish the classes using deep belief networks. Methods: The task-based functional Magnetic Resonance Images (fMRI) of both groups were acquired from ABIDE database, for 58 low functioning against 43 high functioning individuals while they were involving in a defined language processing task. The language processing regions of the brain, along with the Default Mode Network (DMN) have been focussed. The functional connectivity maps have been plotted through graph theory. Brain connectivity parameters such as Granger Causality (GC) and Phase Slope Index (PSI) have been calculated for the groups. These parameters have been fed to the Deep Belief Networks (DBN) to classify the input as either LFA or HFA. Results: Results showed an increased functional connectivity in high functioning subjects. It was found that, the additional interaction of the Primary Auditory Cortex with the other regions of interest complimented their enhanced connectivity. Results were validated using DBN measuring the classification accuracy of 85.85% for high functioning and 81.71% for low functioning group. Conclusion: Since it is known that autism involves enhanced, but imbalanced components of intelligence, the reason behind the supremacy of high functioning group in language processing and region responsible for enhanced connectivity has been identified. Therefore, this work that signifies the effect of Primary Auditory Cortex in characterizing the dominance of language processing in high functioning young adults seems to be highly significant in discriminating the different groups in autism spectrum.

Cognitive measures are directed to assess the load of working memory while performing different tasks. Excessive load on working memory hinders learning or performance of individuals. Lexile measure is the current tool used in assessing the difficulty levels of text reading in English language. Studies on correlating the cognitive load with EEG for classifying tasks based on Lexile measures have been performed for native English speakers. In this work, an attempt has been made to analyze the scope of Lexile measure for assessing the cognitive load of normal subjects. The protocol included reading and recall of texts with different Lexile complexities followed by resting phases. For increasing Lexile level complexities, a considerable increase in cognitive processing was noticed during task phase. Further, an increase in beta power was noticed at the central region indicating active information processing and decision making. Relative theta power (R?=0.11) was significant (p=0.022) in low Lexile level material and gradually decreased as the difficulty level of the tasks increased. Relative theta power was found to be decreasing as the complexity level of the text material increased and was found to dominate in both mid frontal and mid parietal regions during the recall phase. During test phase an increase in alpha power was observed at parietal region reflecting active information processing. This was evident from the highly significant (p=0.022), relative alpha power (Ra =0.036) for recall of high complexity Lexile material compared to medium (Ra=0.005) and low (Ra=0.005) level materials. Thus, it is seen that this study could be more effective in analyzing the cognitive load of subjects with different working memory efficiency. Also, while performing analysis on instructional material design based on cognitive load of different subjects, such procedures seem to be more significant.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by learning, attention, social, communication, and behavioral impairments. Each person with Autism has a different severity and level of brain functioning, ranging from high functioning (HF) to low functioning (LF), depending on their intellectual/developmental abilities. Identifying the level of functionality remains crucial in understanding the cognitive abilities of Autistic children. Assessment of EEG signals acquired during specific cognitive tasks is more appropriate in identifying brain functional and cognitive load variations. The spectral power of EEG sub-band frequency and parameters related to brain asymmetry has the potential to be employed as indices to characterize brain functioning. Thus, the objective of this work is to analyze the cognitive task-based electrophysiological variations in autistic and control groups, using EEG acquired during two well-defined protocols. Theta to Alpha ratio (TAR) and Theta to Beta ratio (TBR) of absolute powers of the respective sub-band frequencies have been estimated to quantify the cognitive load. The variations in interhemispheric cortical power measured by EEG were studied using the brain asymmetry index. For the arithmetic task, the TBR of the LF group was found to be considerably higher than the HF group. The findings reveal that the spectral powers of EEG sub-bands can be a key indicator in the assessment of high and low-functioning ASD to facilitate appropriate training strategies. Instead of depending solely on behavioral tests to diagnose autism, it could be a beneficial approach to use task-based EEG characteristics to differentiate between the LF and HF groups.