Problem associated with biomedical signal like ECG is to extract noise cause by high frequency interference, electromagnetic fields, power line interference and body movement. It is difficult to apply filters with fixed coefficients to reduce random noises. Adaptive filter technique is required to overcome this problem. This paper presents an innovative technique for estimation of ECG waves using Adaptive Noise Cancellation (ANC) algorithm, widrow-hoff LMS algorithm. Comparisons are made for original signal to noisy. Simulations are done for random noise pattern in matlab.
Breast cancer is most common disease in women of all ages. To identify & confirm the state of tumor in breast cancer diagnosis, patients are undergo biopsy number of times to identify malignancy. Early detection of cancer can save the patient. In this paper a novel approach for automatic segmentation & classification of breast calcification is proposed. The diagnostic test technique for detection of breast condition is very costly & requires human expertise whereas proposed method can help in automatically identifying the disease by comparing the data with the standard database. In proposed method a database has been created to define various stage of breast calcification & testing images are pre-processed to resize, enhance & filtered to remove background noise. Clustering is performed by using k-means clustering algorithm. GLCM is used to extract out statistical feature like area, mean, variance, standard deviation, homogeneity, skewness etc. to classify the state of tumor. SVM classifier is used for the classification using extracted feature. .
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