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Medical image processing is the most challenging and emerging field now a day's processing of MRI images is one of the parts of this field. The quantitative analysis of MRI brain tumor allows obtaining useful key indicators of disease progression. This is a computer aided diagnosis systems for detecting malignant texture in biological study. This paper presents an approach in computer-aided diagnosis for early prediction of brain cancer using Texture features and neuro classification logic. This paper describes the proposed strategy for detection; extraction and classification of brain tumour from MRI scan images of brain; which incorporates segmentation and morphological functions which are the basic functions of image processing. Here we detect the tumour, segment the tumour and we calculate the area of the tumour. Severity of the disease can be known, through classes of brain tumour which is done through neuro fuzzy classifier and creating a user friendly environment using GUI in MATLAB. In this paper cases of 10 patients is taken and severity of disease is shown and different features of images are calculated.

Hospitals need several measurement systems that can measure physiological parameters of the patient. Measurement systems should be able to measure accurately the vitals of patients like heart conditions, body temperature, electrical activity of the muscles etc. Approaching EMG is used to monitor the patient during the exercise and advise him to stop as he is the fatigue condition. During muscle activation, the signal is produced from small electrical currents generated by the exchange of ions across the muscle membranes using surface electrodes is amplified and recorded with instrument known as the EMG. The pc based signal acquisition is efficient and effective method for biomedical signal acquisition and monitoring. In this paper a simple and cost effective method for detecting voluntary muscle movement of human being is proposed .in this work .EMG signal is picked up from the hand biceps using simple computer interface and processed by MATLAB based filter algorithm for online clean display.

Abstract-Electromyography (EMG) signal picked up from the muscle fibers have signal in the range below 1KHZ coupled with some noise .The objective is to apply de-nosing by wavelet. Biomedical amplifier AD620 is used for detecting the EMG signal having the gain 4 and approach in 20 to 500Hz. A notch filter is used to eliminate 50Hz pick up. Wave let de-nosing is used to estimate the transform coefficients of basis signals removing the noise. MATLAB is used to calculate the average power of signal. The muscle activates of normal and abnormal persons are compared in case of writer's cramp the cases of alphabets A, B, C are taken for comparison by trapezoidal integration.