Early identification of breast cancer is important for reducing the mortality rate. A common screening and detection technique for breast cancer is mammography. Though Mammography is now considered as the benchmark technique for the early screening and diagnosis of breast cancer, it utilizes harmful ionizing radiations, namely, X-Rays. Moreover the procedure is quite uncomfortable, painful and embarrassing for women, making it less attractive as a preventive screening tool. These disadvantages of mammography can be overcome by using ultrasound imaging technique. Ultrasound is normally considered safe and can be used in soft tissue such as breast. However the quality of the image is compromised due to the predominance of speckle noise. This paper focuses on developing an algorithm for an automatic segmentation and classification of breast lesions from ultrasound image in which the speckle noise was reduced using Tetrolet filter and breast lesions were automatically segmented by using statistical feature based active contour method. After segmentation, for the classification of breast lesion, totally 40 features such as 15 textural, 21 morphological and 4 fractal features were extracted from the images. Optimal features were selected to increase the classification performance by using ReleifF algorithm and 10 best features were taken into account for feature ranking. These features were used to classify the lesions from breast ultrasound images by using Support Vector Machine (SVM) with polynomial kernel for the combination of texture, morphological and fractal features from the Tetrolet filter. This method would help the radiologist to detect and classify the lesions automatically.

(AD) Alzheimer's disease is widely recognized as a disorder of the mental processes initiated by brain disease which a□ects more than 35 million individuals around the world. Structural Magnetic Resonance (MR) imaging strategies noninvasive tool and it assumes a significant part in analyzing Structural brain changes in subjects with neurodegenerative infections. Magnetic resonance imaging which delineates the pathology of brain structures and enhances the finding of the AD. This paper gives an analysis of Alzheimer Condition in Tl-Weighted MR Images using Texture Features and k-NN Classifier. Texture features are extracted utilizing GLCM to identify the AD and the Normal Control (NC). With a specific end goal to assess the proposed technique, performed assessments on the MRI securing from the OASIS database. The proposed strategy yields a normal testing exactness of 74.73% which demonstrates that the proposed technique can separate AD and NC agreeably.

A vision-based pedestrian and car tracking system which is able to distinguish between car and pedestrian is possible using Morphological processing and Blob analysis. Videos are sequence of image frames. Here the algorithm is developed to analyse a frame and the same will be applied to all frames in a video. The unwanted objects in video frame can be removed by converting colour frames into a gray scale and by applying thresholding algorithm. Threshold can be set depending on the object to be detected. Gray scale image will be converted to binary during thresholding process. Morphological processing will be applied on binary image to remove small unwanted objects that are presented in a frame. A developed blob analysis technique for extracted binary image facilitates pedestrian and car detection. Processing blob's information of relative size and location leads to distinguishing between pedestrian and car. The threshold, morphological and blobs process is applied to all frames in a video and finally original video with tagged cars will be displayed.