Anamika Ahirwar

Work place: Department of Computer Science Engineering & IT, Maharana Pratap College of Technology, Gwalior, India



Research Interests: Medical Informatics, Image Compression, Image Manipulation, Image Processing, Data Mining


Anamika Ahirwar is working as an Assistant Professor in the Department of Computer Science Engineering & IT in Maharana Pratap College of Technology, Gwalior (India). She is submitted her PhD. thesis in Medical Imaging, from Rajiv Gandhi Proudyogiki Vishwavidyalaya, (University of Technology of Madhya Pradesh) Bhopal, India. She has published several National and International Research Publications in her name. Her research interests include Data Mining, Image Processing and Medical Imaging.

Author Articles
Study of Techniques used for Medical Image Segmentation and Computation of Statistical Test for Region Classification of Brain MRI

By Anamika Ahirwar

DOI:, Pub. Date: 8 Apr. 2013

This paper explores the possibility of applying techniques for segmenting the regions of medical image. For this we need to investigate the use of different techniques which helps for detection and classification of image regions. We also discuss some segmentation methods classified by researchers. Region classification is an essential process in the visualization of brain tissues of MRI. Brain image is basically classified into three regions; WM, GM and CSF. The forth region can be called as the tumor region, if the image is not normal. In the paper; Segmentation and characterization of Brain MR image regions using SOM and neuro fuzzy techniques, we integrate Self Organizing Map(SOM) and Neuro Fuzzy scheme to automatically extract WM, GM, CSF and tumor region of brain MRI image tested on three normal and three abnormal brain MRI images. Now in this paper this scheme is further tested on axial view images to classify the regions of brain MRI and compare the results from the Keith‘s database. Using some statistical tests like accuracy, precision, sensitivity, specificity, positive predictive value, negative predictive value, false positive rate, false negative rate, likelihood ratio positive, likelihood ratio negative and prevalence of disease we calculate the effectiveness of the scheme.

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