Intuitionistic Neutro Soft Rough Sets and Classical Regression Model for Brain Image Segmentation
Prasanthi Boyapati1, N. Nagamalleswara Rao2
1Prasanthi Boyapati, Research Scholar, Assistant Professor, Department of CSE, Acharya Nagarjuna University R.V.R & J.C College of Engineering, Guntur (Andhra Pradesh), India.
2N. Nagamalleswara Rao, Professor, Department of IT, R.V.R & J.C College of Engineering, Guntur (Andhra Pradesh), India.
Manuscript received on 13 February 2019 | Revised Manuscript received on 09 April 2019 | Manuscript Published on 28 April 2019 | PP: 295-300 | Volume-7 Issue-5C February 2019 | Retrieval Number: E10670275C19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Magnetic resonance image (MRI) is one of major component in medical brain image, imaging technique and segmentation of brain medical image is a crucial & complex task in evaluation of MRI images. Conventionally, different types of fuzzy, soft set related approaches like intuitionistic, fuzzy c-means, fuzzy c-means were developed to segmentation of brain related image, but these approaches face accuracy loss in brain image segmentation. So we consider new segmentation approach i.e. Intuitionistic neutro soft based rough sets and Classical Regression model (INSRCRM) which is extension to Advanced machine learning approach i.e. Enhanced & Explored Intuitionistic FCM clustering (EEISFCM) for smoothness and to increase image accuracy and intensity. Proposed approach is applied to increase accuracy and intensity with respect to spatial data processing for medical brain image segmentation and evaluate histon and histogram based image smoothness. Proposed approach evaluated with lower and upper approximations for intensity based brain image segmentation. This approach mainly identifies real valleys to smooth measure to present brain image segmentation to reduce noise reduction based on threshold of image pixels with different image notations. Experimental results of proposed approach gives to find peaks and valleys to demonstrate better image segmentation results with respect to traditional approaches.
Keywords: Medical Image Segmentation, Regression Model, Intuitionistic Soft Based Rough Sets, Fuzzy C- Means, Classification Accuracy And Spatial Weighted Data.
Scope of the Article: Image Security