Kongu Engineering College

About: Kongu Engineering College is a based out in . It is known for research contribution in the topics: Cluster analysis & Control theory. The organization has 2001 authors who have published 1978 publications receiving 16923 citations.

A Modified Watershed Segmentation Method to Segment Renal Calculi in Ultrasound Kidney Images

Abstract: Segmentation of stones from abdominal ultrasound images is a unique challenge to the researchers because these images have heavy speckle noise and attenuated artifacts. In the previous renal calculi segmentation method, the stones were segmented from the medical ultra sound kidney stone images using Adaptive Neuro Fuzzy Inference System ANFIS. But, the method lacks in sensitivity and specificity measures. The segmentation method is inadequate in its performance in terms of these two parameters. So, to avoid these drawbacks, a new segmentation method is proposed in this paper. Here, new region indicators and new modified watershed transformation is utilized. The proposed method is comprised of four major processes, namely, preprocessing, determination of outer and inner region indictors, modified watershed segmentation with ANFIS performance. The method is implemented and the results are analyzed in terms of various statistical performance measures. The results show the effectiveness of proposed segmentation method in segmenting the kidney stones and the achieved improvement in sensitivity and specificity measures. Furthermore, the performance of the proposed technique is evaluated by comparing with the other segmentation methods.

A Miniaturized High-Gain (MHG) Ultra-Wideband Unidirectional Monopole Antenna for UWB Applications

Abstract: A miniaturized high-gain (MHG) ultra-wideband (UWB) unidirectional monopole antenna with defected ground structure (DGS) is designed for ultra-wideband applications. The MHG antenna is printed on t...

An Experimental Investigation of Marble Dust in Luffa Fibre Reinforced Concrete

Abstract: Typically, Concrete mixture is firm in Compression yet sickly in Tension and shear Reason for this examination is to discover the conduct of cement built up with half-breed full-scale strands By adding Luffa strands in rates like 05%, 1%, 15%& 2% to the solid, the mechanical properties are researched The ideal level of Luffa fiber was discovered to be 1% Marble has been generally utilized in structures since old occasions The current examination is pointed toward using waste marble dust (WMD) in development industry itself as fine total in solid, supplanting characteristic sand and furthermore by adding the ideal level of Luffa fiber The substitution is done halfway and completely in the different extents like 0%, 20%, 40%, 60% and 80% and its impact on properties of cement were examined The ideal level of the solid by adding 1% of Luffa fiber and the extents was discovered to be 20%

An Efficient Method for Brain Tumor Detection Using Texture Features and SVM Classifier in MR Images

Abstract: Objective: Detection and classification of abnormalities in Magnetic Resonance (MR) brain images in medical field is very much needed. The proposed brain tumor classification system composed of denoising, feature extraction and classification. Noise is one of the major problems in the medical image and due to that retrieval of useful information from the image is difficult. The proposed method for denoising an image is PURE-LET transform. Methods: This method preserves the diagnostic property of the images. In feature extraction, combination of Modified Multi-Texton Histogram (MMTH) and Multi-Texton Microstructure Descriptor (MTMD) is used and then Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM)are used to extract the feature from the image to compare performance. In classification, classifiers like Support Vector Machine (SVM), K Nearest Neighbors (KNN) and Extreme Learning Machine (ELM)are trained by the extracted features and are used to classify the images. Result: The performance of feature extraction methods with three different classifiers are compared in terms of the performance metrics like sensitivity, specificity, and accuracy. Conclusion: The result shows that the combination of MMTH and MTMD with SVM shows the highest accuracy of 95%.