The objective of the current work is to develop an automatic tool to identify microbiological data types using computer vision and pattern recognition. Current systems rely on the subjective reading of profiles by a human expert. This process is time-consuming and prone to errors. Bacteriophage (phage) typing & Fluorescent imaging methods are used to extract representative feature profiles and identify the bacterial types. For feature selection of Bacterial identification system, the most successful method seems to be the appearance-based approach, which generally operates directly on images or appearances of bacterial objects. The image segmentation, Linear Discriminant Analysis (LDA), Direct Fractional LDA (DFLDA) and Principal Component Analysis (PCA) are the powerful tools used for feature extraction. Then the principal components are analyzed by DFLDA and simple Nearest Neighbor Classifier technique is used to identify the type of bacteria. The effectiveness of the proposed method has been verified through experimentation using fifty popular bacterial image databases.
Uterine fibroid is the most predominant problem among women of child-bearing age where the secretion of estrogen hormone plays significant role. The presence of fibroid can cause severe pain, infertility, and repeated miscarriages. Since the detection of fibroid and treatment is the crucial factor on women health especially in pregnancy, ultrasound (US) imaging is the most common modality for detecting fibroids. Because of the presence of speckle noise, the segmentation of fibroid from an US image is the tedious process. The proposed methodology has been used for automating this task by morphological functions available in graphical vision assistant tool. The modified morphological image cleaning (MMIC) algorithm for filtering and Canny edge detector have been utilized for fibroid segmentation and binary image morphological approaches adopted for analyzing the fibroid. The proposed algorithm has been developed, implemented, and validated in LabVIEW vision assistant toolbox. The outcomes of the proposed method have been evaluated and appreciated by experienced gynecologists and found that the manual intervention is eliminated on the investigation of diseased.
Alzheimer’s disease (AD), Mental retardation, Cerebral Palsy, and other Dementias are the neurogenerative brain disorders which are statistically proven that 2% - 3% of people affected in the world today. The proposed method considered the symptoms which stands distinct for Alzheimer’s disease. Many structural neuroimaging studies have found the atrophy of the Corpus Callosum (CC) and the decrease in brain volume in AD. The measurement, area has been extracted from the gradient mask of the image to characterize the local atrophy of the CC. The result showed decreased area of the CC in AD when compared to the control groups. The volume has also been calculated by volume rendering and voxel size measurement for the same set of control groups and was found to be reduced in the AD patients. These findings confirmed the pathology characteristics in AD of brain disorders. The system’s validity with respect to results obtained with conventional diagnosis has been examined and proved to offer better results
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