M. Monica Subashini

Bio: M. Monica Subashini is an academic researcher from VIT University. The author has contributed to research in topics: Body area network & Traffic sign. The author has an hindex of 8, co-authored 41 publications receiving 427 citations. Previous affiliations of M. Monica Subashini include Saint Petersburg State Electrotechnical University.

Review: Pulse coupled neural networks and its applications

Abstract: This paper surveys the extensive usage of pulse coupled neural networks. The visual cortex system of mammalians was the backbone for the development of pulse coupled neural network. PCNN (Pulse Coupled Neural Networks) is unique from other techniques due to its synchronous pulsed output, adjustable threshold and controllable parameters. is Hence the uniqueness of this network utilized in the fields of image processing. The basic model of PCNN and the consecutive changes implemented, to strengthen the pulse coupled neural network are discussed initially. Then the applications of PCNN are broadly discussed. The other miscellaneous applications utilizing pulse coupled neural networks are thrown light in the last section.

A non-invasive methodology for the grade identification of astrocytoma using image processing and artificial intelligence techniques

Abstract: To suggest a non-invasive method for classification of Astrocytoma through rigorous training and testing.To compare and conclude the best medical image segmentation technique.To develop an efficient automatic feature selection technique for grade identification.To analyze and quantify the performance of classifiers constructed for the grade identification of Astrocytoma (tumor). Brain tumor grade identification is an invasive technique and clinicians rely on biopsy and spinal tap method. The proposed method takes an effort to develop a non-invasive method for the tumor grade (Low/High) identification using magnetic resonant images. The process involves preprocessing, image segmentation, tumor isolation, feature extraction, feature selection and classification. An analysis on the performance of the segmentation techniques, feature extraction methods, automatic feature selection (SFLA) and constructed classifiers (support vector machines, learning vector quantization and Naives Bayes) is done on the basis of accuracy, efficiency and elapsed time. This analysis motivates towards the accurate determination of tumor grade from MR images instead of depending on magnetic resonant spectroscopy and biopsy. Fuzzy c-means segmentation outperformed other segmentation techniques, shape and size based textural feature promoted the demarcation of tumor grades, Naive Bayes classifier succeeded in terms of efficiency, error and elapse time when compared with SVM and LVQ. The study was carried out with 200 images consisting training set (164 images) and testing set (36 images). The results revealed that the system is robust and accurate (91%), consumed less time in grade identification, an alternative for biopsy and MRS in the brain tumor grade identification diagnosis procedure.

Analysis of Electrocardiograph (ECG) Signal for the Detection of Abnormalities Using MATLAB

Abstract: The proposed method is to study and analyze Electrocardiograph (ECG) waveform to detect abnormalities present with reference to P, Q, R and S peaks. The first phase includes the acquisition of real time ECG data. In the next phase, generation of signals followed by pre-processing. Thirdly, the procured ECG signal is subjected to feature extraction. The extracted features detect abnormal peaks present in the waveform Thus the normal and abnormal ECG signal could be differentiated based on the features extracted. The work is implemented in the most familiar multipurpose tool, MATLAB. This software efficiently uses algorithms and techniques for detection of any abnormalities present in the ECG signal. Proper utilization of MATLAB functions (both built-in and user defined) can lead us to work with ECG signals for processing and analysis in real time applications. The simulation would help in improving the accuracy and the hardware could be built conveniently. Keywords—ECG Waveform, Peak Detection, Arrhythmia, Matlab.

Brain tumour detection using Pulse coupled neural network (PCNN) and back propagation network

Abstract: Brain tumor detection is an important application in recent days. The medical problems are severe if tumor is identified at the later stage. Hence diagnosis is necessary at the earliest. MRI is the current technology which enables the detection, diagnosis and evaluation. In this work, the images obtained through MRI are segmented and then fed to a model known as Pulse coupled neural network for detecting the presence of tumor in the brain image. The physician could seek the help of this model if the input MRI brain images are more in number and the network would help the physician to save time for further analysis. The work also utilizes back propagation network for classification. Both the networks are less complex in nature and hence the processing of MRI brain images is very simple. The network classifies the input images as normal and tumor containing. The tumor may be benign and malignant and it needs medical support for further classification.

Decision Support System

Abstract: Ein Decision Support System umfast die Komponenten Daten, Dialog und Modell, weshalb in diesem Kontext auch von DDM-Paradigma gesprochen wird.1 Die Komponente Daten sollte derart gestaltet sein, das die benotigten Informationen adaquat bereitgehalten werden. Durch die Komponente Dialog ist sicherzustellen, das die Interaktion des Anwenders mit dem Decision Support System und damit auch der Zugriff auf die Daten einfach und leicht verstandlich gestaltet ist. Die Komponente Modell sollte der Aufgabenstellung angemessene Modellierungs- und Analysemoglichkeiten zur Verfugung stellen. Ein Decision Support System, welches dem DDM-Paradigma Rechnung tragt, fugt die beschriebenen Komponenten geeignet zusammen.