No abstract

An efficiency of cochlea has a significant contribution in a quality of human hearing and in a language development of newborn. The efficiency of cochlea clinically measured using Distortion Product Otoacoustic Emission (DPOAE). The measurement mainly restricted by acoustic interferences that disrupt response estimation. The disrupted estimation gives rise to repetition in measurement for many times or probably inaccurate efficiency assessment. In this study, investigation of cochlea response estimation was considered based on signal processing (SP), which was regarded as preliminary step toward interference reduction. An in-vivo measurement was performed on a left ear of 36 years old female with normal hearing, where cochlea stimulation and response recording was conducted using personal computer sound card in conjunction of sensitive probe system of ER-10C. The response signals were recorded and then analyzed off-line using SP of Fast Fourier Transform (FFT) and Band Pass-Finite Impulse Response (BP-FIR) filter. Results show that DPOAE frequency components can be extracted using proposed SP method at interference-free situation, where BP-FIR parameters of bandwidth 50Hz-200Hz and order 32-2048 have to be adjusted based on stimulation parameters. The findings dedicate useful investigated parameters for real-time implementation, and for further SP development at interference situation.

An ultrasound elastography introduced to differentiate hard tumor inclusion embedded in soft tissue background based on similarity measurement of before and after deformation. In this study, freehand elastography has considered to localize hard inclusion embedded in soft tissue of phantom breast, where deformation generated by applying gentile compression using probe physical surface of ultrasound machine. Radiofrequency data of before and after deformation acquired and then processed off-line. A non-ability of refinement operation to regularize displacement estimation outliers at correlation window length of 2λ is addressed, where a multilevel processing algorithm has proposed to reinforce refinement operation by producing smooth elastography. In the first level of the processing, displacement field has estimated at correlation window length of 3λ, where global stretching as re-correlation operation and refinement operation as spatial regulation are included. While at second level, production of displacement estimation outliers at correlation window length of 2λ are regularized based on replacement of estimated cells with that interpolated one at first level. Results show an ability of multilevel algorithm to cope the issues that encountered previously proposed algorithm of refinement on estimation outlier free displacement field at an axial resolution of 2λ, and produces differential strain field.

MASITs provides an optimum outcomes if it is not probable to become the solutions of huge inflexible optimization difficulties. Computerized investigation of skin lesions is a significant problem in data retrieval for medical imaging, it supports human experts to enhance their choice construction for rapid and accurate analysis of unhealthy nevi and other skin diseases. In this article, computerized investigation of skin lesions has been addressed, by an adjustment of controlling swarm intelligence system (Artifical Bee Colony{ABC}).The modified system is hybridized with a search technique for improved performance. Experimental outcomes on a level of medical images of early diagnosis skin lesions confirmation that this technique outclasses conventional mathematical approaches for the cases in the standard. It is identical good and regularly higher to advanced systems in the area in relationships of mathematical accuracy. The chief benefit of the proposed technique is that this diagnosis can segment skin lesions by resolve images. So, additional comprehensive features can be found from the segmented portion of the lesion, which in turn contributes on organization medical service accuracy.

In this study, an ultrasound Radio-Frequency (RF) data of healthy and tumour breast regions are considered for tissue characterisation. The main aim of this study is to differentiate the consistent statistical distribution of backscattered RF data with the objective of performing semi-automatic segmentation based on statistics. The differentiation considers Gamma, Generalised Extreme Value (GEV), Log-normal, and Rayleigh distributions. The accuracy of the statistical parameters is measured based on the criteria of both the Kolmogorov-Smirnov Test (KS) and Mean Square Error (MSE) goodness of fit. Results show that there is a possibility of using the parameters of Rayleigh, Gamma, and GEV for different healthy and tumour tissue regions, where GEV yields the best goodness of fit test and its parameters have a good potential to be exploited for further study for segmentation purposes.