Show simple item record

dc.contributor.advisorAndriamanalimanana, Bruno
dc.contributor.authorKalakuntla, Shashank
dc.contributor.authorAndriamanalimanana, Bruno R.; First Reader
dc.contributor.authorNovillo, Jorge E.; Second Reader
dc.contributor.authorSpetka, Scott; Third Reader
dc.date.accessioned2020-12-28T16:43:09Z
dc.date.available2020-12-28T16:43:09Z
dc.date.issued2020-08
dc.identifier.citationKalakuntla, S., & Andriamanalimanana, B. (2020, August). Detection of Brain Tumor in Magnetic Resonance Imaging (MRI) Images using Fuzzy C-Means and Thresholding: A Project Presented to Department of Computer Sciences, State University Of New York Polytechnic Institute, in Partial Fulfillment of the Requirements of the Master of Science Degree. Department of Computer Science, College of Engineering, SUNY Polytechnic Institute.en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12648/1610
dc.description.abstractAlthough many clinical experts or radiologists are well trained to identify tumors and other abnormalities in the brain, the identification, detection and segmentation of the affected area in the brain is observed to be a tedious and time consuming task. MRI has been a conventional and resultant image processing technique to visualize structures of the human body. It is very difficult to visualize abnormal structures of the brain using simple imaging techniques. MRI technique uses many imaging modalities that scan and capture the internal structure of the human brain. Even with the use of these techniques, it is a difficult and tedious task for a human eye to be always sophisticated in detecting brain tumors from these images. With emerging technology, we can provide a way to ease the process of detection. This project focuses on identification of brain tumor in MR images, it involves in removing noise using noise removal technique AMF followed by enhancing the images using Balance Enhancement Contrast technique (BCET).Further, image segmentation is performed using fuzzy c-means and finally the segmented images are produced as an input to a canny edge detection resulting with the tumor image. This report entices the approach, design, and implementation of the application and finally the results. I have tried implementing/developing this application in Python. The Jupyter notebook provides a block simulation for the entire flow of the project.en_US
dc.publisherSUNY Polytechnic Instituteen_US
dc.subjectmedical diagnosticsen_US
dc.subjectbrain imagingen_US
dc.subjectimage processingen_US
dc.subjectmachine learningen_US
dc.subjectfuzzy C-meansen_US
dc.subjectbalance enhancement contrast techniqueen_US
dc.subjectadaptive median filteringen_US
dc.subjectOtsu’s thresholdingen_US
dc.subjectcanny edge detectionen_US
dc.subjectmagnetic resonance imaging (MRI)en_US
dc.titleDetection of Brain Tumor in Magnetic Resonance Imaging (MRI) Images using Fuzzy C-Means and Thresholdingen_US
dc.title.alternativeA Project Presented to Department of Computer Sciences, State University of New York Polytechnic Institute, In Partial fulfillment of the Requirements of the Master of Science Degreeen_US
dc.typeOtheren_US
dc.description.versionNAen_US
refterms.dateFOA2020-12-28T16:43:09Z
dc.description.institutionSUNY Polytechnic Instituteen_US
dc.description.departmentDepartment of Computer Scienceen_US
dc.description.degreelevelMSen_US


Files in this item

Thumbnail
Name:
Kalakuntla_Shashank Project Final ...
Size:
1.957Mb
Format:
PDF
Thumbnail
Name:
Kalakunta signed advisor sheets.pdf
Size:
130.0Kb
Format:
PDF
Thumbnail
Name:
Kalakuntla_Shashank_signed ...
Size:
206.0Kb
Format:
PDF
Thumbnail
Name:
Shashank Kalakuntla Online ...
Size:
356.9Kb
Format:
PDF
Description:
Online Distribution License

This item appears in the following Collection(s)

  • SUNY Polytechnic Institute College of Engineering
    This collection contains master's theses, capstone projects, and other student and faculty work from programs within the Department of Engineering, including computer science and network security.

Show simple item record